diff --git a/server/env.example b/server/env.example
index 10079105..4952a937 100644
--- a/server/env.example
+++ b/server/env.example
@@ -46,38 +46,11 @@ TRANSLATE_URL=https://monadical-sas--reflector-translator-web.modal.run
 ## llm backend implementation
 ## =======================================================
 
-## Using serverless modal.com (require reflector-gpu-modal deployed)
-LLM_BACKEND=modal
-LLM_URL=https://monadical-sas--reflector-llm-web.modal.run
-LLM_MODAL_API_KEY=
-ZEPHYR_LLM_URL=https://monadical-sas--reflector-llm-zephyr-web.modal.run
-
-
-## Using OpenAI
-#LLM_BACKEND=openai
-#LLM_OPENAI_KEY=xxx
-#LLM_OPENAI_MODEL=gpt-3.5-turbo
-
-## Using GPT4ALL
-#LLM_BACKEND=openai
-#LLM_URL=http://localhost:4891/v1/completions
-#LLM_OPENAI_MODEL="GPT4All Falcon"
-
-## Default LLM MODEL NAME
-#DEFAULT_LLM=lmsys/vicuna-13b-v1.5
-
-## Cache directory to store models
-CACHE_DIR=data
-
-## =======================================================
-## Summary LLM configuration
-## =======================================================
-
 ## Context size for summary generation (tokens)
-SUMMARY_LLM_CONTEXT_SIZE_TOKENS=16000
-SUMMARY_LLM_URL=
-SUMMARY_LLM_API_KEY=sk-
-SUMMARY_MODEL=
+# LLM_MODEL=microsoft/phi-4
+LLM_CONTEXT_WINDOW=16000
+LLM_URL=
+LLM_API_KEY=sk-
 
 ## =======================================================
 ## Diarization
diff --git a/server/gpu/modal_deployments/README.md b/server/gpu/modal_deployments/README.md
index dee4052e..f31810e1 100644
--- a/server/gpu/modal_deployments/README.md
+++ b/server/gpu/modal_deployments/README.md
@@ -3,8 +3,9 @@
 This repository hold an API for the GPU implementation of the Reflector API service,
 and use [Modal.com](https://modal.com)
 
-- `reflector_llm.py` - LLM API
+- `reflector_diarizer.py` - Diarization API
 - `reflector_transcriber.py` - Transcription API
+- `reflector_translator.py` - Translation API
 
 ## Modal.com deployment
 
diff --git a/server/gpu/modal_deployments/reflector_llm.py b/server/gpu/modal_deployments/reflector_llm.py
deleted file mode 100644
index f3752f5d..00000000
--- a/server/gpu/modal_deployments/reflector_llm.py
+++ /dev/null
@@ -1,213 +0,0 @@
-"""
-Reflector GPU backend - LLM
-===========================
-
-"""
-
-import json
-import os
-import threading
-from typing import Optional
-
-from modal import App, Image, Secret, asgi_app, enter, exit, method
-
-# LLM
-LLM_MODEL: str = "lmsys/vicuna-13b-v1.5"
-LLM_LOW_CPU_MEM_USAGE: bool = True
-LLM_TORCH_DTYPE: str = "bfloat16"
-LLM_MAX_NEW_TOKENS: int = 300
-
-IMAGE_MODEL_DIR = "/root/llm_models"
-
-app = App(name="reflector-llm")
-
-
-def download_llm():
-    from huggingface_hub import snapshot_download
-
-    print("Downloading LLM model")
-    snapshot_download(LLM_MODEL, cache_dir=IMAGE_MODEL_DIR)
-    print("LLM model downloaded")
-
-
-def migrate_cache_llm():
-    """
-    XXX The cache for model files in Transformers v4.22.0 has been updated.
-    Migrating your old cache. This is a one-time only operation. You can
-    interrupt this and resume the migration later on by calling
-    `transformers.utils.move_cache()`.
-    """
-    from transformers.utils.hub import move_cache
-
-    print("Moving LLM cache")
-    move_cache(cache_dir=IMAGE_MODEL_DIR, new_cache_dir=IMAGE_MODEL_DIR)
-    print("LLM cache moved")
-
-
-llm_image = (
-    Image.debian_slim(python_version="3.10.8")
-    .apt_install("git")
-    .pip_install(
-        "transformers",
-        "torch",
-        "sentencepiece",
-        "protobuf",
-        "jsonformer==0.12.0",
-        "accelerate==0.21.0",
-        "einops==0.6.1",
-        "hf-transfer~=0.1",
-        "huggingface_hub==0.16.4",
-    )
-    .env({"HF_HUB_ENABLE_HF_TRANSFER": "1"})
-    .run_function(download_llm)
-    .run_function(migrate_cache_llm)
-)
-
-
-@app.cls(
-    gpu="A100",
-    timeout=60 * 5,
-    scaledown_window=60 * 5,
-    allow_concurrent_inputs=15,
-    image=llm_image,
-)
-class LLM:
-    @enter()
-    def enter(self):
-        import torch
-        from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
-
-        print("Instance llm model")
-        model = AutoModelForCausalLM.from_pretrained(
-            LLM_MODEL,
-            torch_dtype=getattr(torch, LLM_TORCH_DTYPE),
-            low_cpu_mem_usage=LLM_LOW_CPU_MEM_USAGE,
-            cache_dir=IMAGE_MODEL_DIR,
-            local_files_only=True,
-        )
-
-        # JSONFormer doesn't yet support generation configs
-        print("Instance llm generation config")
-        model.config.max_new_tokens = LLM_MAX_NEW_TOKENS
-
-        # generation configuration
-        gen_cfg = GenerationConfig.from_model_config(model.config)
-        gen_cfg.max_new_tokens = LLM_MAX_NEW_TOKENS
-
-        # load tokenizer
-        print("Instance llm tokenizer")
-        tokenizer = AutoTokenizer.from_pretrained(
-            LLM_MODEL, cache_dir=IMAGE_MODEL_DIR, local_files_only=True
-        )
-
-        # move model to gpu
-        print("Move llm model to GPU")
-        model = model.cuda()
-
-        print("Warmup llm done")
-        self.model = model
-        self.tokenizer = tokenizer
-        self.gen_cfg = gen_cfg
-        self.GenerationConfig = GenerationConfig
-
-        self.lock = threading.Lock()
-
-    @exit()
-    def exit():
-        print("Exit llm")
-
-    @method()
-    def generate(
-        self, prompt: str, gen_schema: str | None, gen_cfg: str | None
-    ) -> dict:
-        """
-        Perform a generation action using the LLM
-        """
-        print(f"Generate {prompt=}")
-        if gen_cfg:
-            gen_cfg = self.GenerationConfig.from_dict(json.loads(gen_cfg))
-        else:
-            gen_cfg = self.gen_cfg
-
-        # If a gen_schema is given, conform to gen_schema
-        with self.lock:
-            if gen_schema:
-                import jsonformer
-
-                print(f"Schema {gen_schema=}")
-                jsonformer_llm = jsonformer.Jsonformer(
-                    model=self.model,
-                    tokenizer=self.tokenizer,
-                    json_schema=json.loads(gen_schema),
-                    prompt=prompt,
-                    max_string_token_length=gen_cfg.max_new_tokens,
-                )
-                response = jsonformer_llm()
-            else:
-                # If no gen_schema, perform prompt only generation
-
-                # tokenize prompt
-                input_ids = self.tokenizer.encode(prompt, return_tensors="pt").to(
-                    self.model.device
-                )
-                output = self.model.generate(input_ids, generation_config=gen_cfg)
-
-                # decode output
-                response = self.tokenizer.decode(
-                    output[0].cpu(), skip_special_tokens=True
-                )
-                response = response[len(prompt) :]
-        print(f"Generated {response=}")
-        return {"text": response}
-
-
-# -------------------------------------------------------------------
-# Web API
-# -------------------------------------------------------------------
-
-
-@app.function(
-    scaledown_window=60 * 10,
-    timeout=60 * 5,
-    allow_concurrent_inputs=45,
-    secrets=[
-        Secret.from_name("reflector-gpu"),
-    ],
-)
-@asgi_app()
-def web():
-    from fastapi import Depends, FastAPI, HTTPException, status
-    from fastapi.security import OAuth2PasswordBearer
-    from pydantic import BaseModel
-
-    llmstub = LLM()
-
-    app = FastAPI()
-    oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")
-
-    def apikey_auth(apikey: str = Depends(oauth2_scheme)):
-        if apikey != os.environ["REFLECTOR_GPU_APIKEY"]:
-            raise HTTPException(
-                status_code=status.HTTP_401_UNAUTHORIZED,
-                detail="Invalid API key",
-                headers={"WWW-Authenticate": "Bearer"},
-            )
-
-    class LLMRequest(BaseModel):
-        prompt: str
-        gen_schema: Optional[dict] = None
-        gen_cfg: Optional[dict] = None
-
-    @app.post("/llm", dependencies=[Depends(apikey_auth)])
-    def llm(
-        req: LLMRequest,
-    ):
-        gen_schema = json.dumps(req.gen_schema) if req.gen_schema else None
-        gen_cfg = json.dumps(req.gen_cfg) if req.gen_cfg else None
-        func = llmstub.generate.spawn(
-            prompt=req.prompt, gen_schema=gen_schema, gen_cfg=gen_cfg
-        )
-        result = func.get()
-        return result
-
-    return app
diff --git a/server/gpu/modal_deployments/reflector_llm_zephyr.py b/server/gpu/modal_deployments/reflector_llm_zephyr.py
deleted file mode 100644
index 5d9c0390..00000000
--- a/server/gpu/modal_deployments/reflector_llm_zephyr.py
+++ /dev/null
@@ -1,219 +0,0 @@
-"""
-Reflector GPU backend - LLM
-===========================
-
-"""
-
-import json
-import os
-import threading
-from typing import Optional
-
-from modal import App, Image, Secret, asgi_app, enter, exit, method
-
-# LLM
-LLM_MODEL: str = "HuggingFaceH4/zephyr-7b-alpha"
-LLM_LOW_CPU_MEM_USAGE: bool = True
-LLM_TORCH_DTYPE: str = "bfloat16"
-LLM_MAX_NEW_TOKENS: int = 300
-
-IMAGE_MODEL_DIR = "/root/llm_models/zephyr"
-
-app = App(name="reflector-llm-zephyr")
-
-
-def download_llm():
-    from huggingface_hub import snapshot_download
-
-    print("Downloading LLM model")
-    snapshot_download(LLM_MODEL, cache_dir=IMAGE_MODEL_DIR)
-    print("LLM model downloaded")
-
-
-def migrate_cache_llm():
-    """
-    XXX The cache for model files in Transformers v4.22.0 has been updated.
-    Migrating your old cache. This is a one-time only operation. You can
-    interrupt this and resume the migration later on by calling
-    `transformers.utils.move_cache()`.
-    """
-    from transformers.utils.hub import move_cache
-
-    print("Moving LLM cache")
-    move_cache(cache_dir=IMAGE_MODEL_DIR, new_cache_dir=IMAGE_MODEL_DIR)
-    print("LLM cache moved")
-
-
-llm_image = (
-    Image.debian_slim(python_version="3.10.8")
-    .apt_install("git")
-    .pip_install(
-        "transformers==4.34.0",
-        "torch",
-        "sentencepiece",
-        "protobuf",
-        "jsonformer==0.12.0",
-        "accelerate==0.21.0",
-        "einops==0.6.1",
-        "hf-transfer~=0.1",
-        "huggingface_hub==0.16.4",
-    )
-    .env({"HF_HUB_ENABLE_HF_TRANSFER": "1"})
-    .run_function(download_llm)
-    .run_function(migrate_cache_llm)
-)
-
-
-@app.cls(
-    gpu="A10G",
-    timeout=60 * 5,
-    scaledown_window=60 * 5,
-    allow_concurrent_inputs=10,
-    image=llm_image,
-)
-class LLM:
-    @enter()
-    def enter(self):
-        import torch
-        from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
-
-        print("Instance llm model")
-        model = AutoModelForCausalLM.from_pretrained(
-            LLM_MODEL,
-            torch_dtype=getattr(torch, LLM_TORCH_DTYPE),
-            low_cpu_mem_usage=LLM_LOW_CPU_MEM_USAGE,
-            cache_dir=IMAGE_MODEL_DIR,
-            local_files_only=True,
-        )
-
-        # JSONFormer doesn't yet support generation configs
-        print("Instance llm generation config")
-        model.config.max_new_tokens = LLM_MAX_NEW_TOKENS
-
-        # generation configuration
-        gen_cfg = GenerationConfig.from_model_config(model.config)
-        gen_cfg.max_new_tokens = LLM_MAX_NEW_TOKENS
-
-        # load tokenizer
-        print("Instance llm tokenizer")
-        tokenizer = AutoTokenizer.from_pretrained(
-            LLM_MODEL, cache_dir=IMAGE_MODEL_DIR, local_files_only=True
-        )
-        gen_cfg.pad_token_id = tokenizer.eos_token_id
-        gen_cfg.eos_token_id = tokenizer.eos_token_id
-        tokenizer.pad_token = tokenizer.eos_token
-        model.config.pad_token_id = tokenizer.eos_token_id
-
-        # move model to gpu
-        print("Move llm model to GPU")
-        model = model.cuda()
-
-        print("Warmup llm done")
-        self.model = model
-        self.tokenizer = tokenizer
-        self.gen_cfg = gen_cfg
-        self.GenerationConfig = GenerationConfig
-        self.lock = threading.Lock()
-
-    @exit()
-    def exit():
-        print("Exit llm")
-
-    @method()
-    def generate(
-        self, prompt: str, gen_schema: str | None, gen_cfg: str | None
-    ) -> dict:
-        """
-        Perform a generation action using the LLM
-        """
-        print(f"Generate {prompt=}")
-        if gen_cfg:
-            gen_cfg = self.GenerationConfig.from_dict(json.loads(gen_cfg))
-            gen_cfg.pad_token_id = self.tokenizer.eos_token_id
-            gen_cfg.eos_token_id = self.tokenizer.eos_token_id
-        else:
-            gen_cfg = self.gen_cfg
-
-        # If a gen_schema is given, conform to gen_schema
-        with self.lock:
-            if gen_schema:
-                import jsonformer
-
-                print(f"Schema {gen_schema=}")
-                jsonformer_llm = jsonformer.Jsonformer(
-                    model=self.model,
-                    tokenizer=self.tokenizer,
-                    json_schema=json.loads(gen_schema),
-                    prompt=prompt,
-                    max_string_token_length=gen_cfg.max_new_tokens,
-                )
-                response = jsonformer_llm()
-            else:
-                # If no gen_schema, perform prompt only generation
-
-                # tokenize prompt
-                input_ids = self.tokenizer.encode(prompt, return_tensors="pt").to(
-                    self.model.device
-                )
-                output = self.model.generate(input_ids, generation_config=gen_cfg)
-
-                # decode output
-                response = self.tokenizer.decode(
-                    output[0].cpu(), skip_special_tokens=True
-                )
-                response = response[len(prompt) :]
-                response = {"long_summary": response}
-        print(f"Generated {response=}")
-        return {"text": response}
-
-
-# -------------------------------------------------------------------
-# Web API
-# -------------------------------------------------------------------
-
-
-@app.function(
-    scaledown_window=60 * 10,
-    timeout=60 * 5,
-    allow_concurrent_inputs=30,
-    secrets=[
-        Secret.from_name("reflector-gpu"),
-    ],
-)
-@asgi_app()
-def web():
-    from fastapi import Depends, FastAPI, HTTPException, status
-    from fastapi.security import OAuth2PasswordBearer
-    from pydantic import BaseModel
-
-    llmstub = LLM()
-
-    app = FastAPI()
-    oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")
-
-    def apikey_auth(apikey: str = Depends(oauth2_scheme)):
-        if apikey != os.environ["REFLECTOR_GPU_APIKEY"]:
-            raise HTTPException(
-                status_code=status.HTTP_401_UNAUTHORIZED,
-                detail="Invalid API key",
-                headers={"WWW-Authenticate": "Bearer"},
-            )
-
-    class LLMRequest(BaseModel):
-        prompt: str
-        gen_schema: Optional[dict] = None
-        gen_cfg: Optional[dict] = None
-
-    @app.post("/llm", dependencies=[Depends(apikey_auth)])
-    def llm(
-        req: LLMRequest,
-    ):
-        gen_schema = json.dumps(req.gen_schema) if req.gen_schema else None
-        gen_cfg = json.dumps(req.gen_cfg) if req.gen_cfg else None
-        func = llmstub.generate.spawn(
-            prompt=req.prompt, gen_schema=gen_schema, gen_cfg=gen_cfg
-        )
-        result = func.get()
-        return result
-
-    return app
diff --git a/server/reflector/llm.py b/server/reflector/llm.py
new file mode 100644
index 00000000..eed50e4a
--- /dev/null
+++ b/server/reflector/llm.py
@@ -0,0 +1,83 @@
+from typing import Type, TypeVar
+
+from llama_index.core import Settings
+from llama_index.core.output_parsers import PydanticOutputParser
+from llama_index.core.program import LLMTextCompletionProgram
+from llama_index.core.response_synthesizers import TreeSummarize
+from llama_index.llms.openai_like import OpenAILike
+from pydantic import BaseModel
+
+T = TypeVar("T", bound=BaseModel)
+
+STRUCTURED_RESPONSE_PROMPT_TEMPLATE = """
+Based on the following analysis, provide the information in the requested JSON format:
+
+Analysis:
+{analysis}
+
+{format_instructions}
+"""
+
+
+class LLM:
+    def __init__(self, settings, temperature: float = 0.4, max_tokens: int = 2048):
+        self.settings_obj = settings
+        self.model_name = settings.LLM_MODEL
+        self.url = settings.LLM_URL
+        self.api_key = settings.LLM_API_KEY
+        self.context_window = settings.LLM_CONTEXT_WINDOW
+        self.temperature = temperature
+        self.max_tokens = max_tokens
+
+        # Configure llamaindex Settings
+        self._configure_llamaindex()
+
+    def _configure_llamaindex(self):
+        """Configure llamaindex Settings with OpenAILike LLM"""
+        Settings.llm = OpenAILike(
+            model=self.model_name,
+            api_base=self.url,
+            api_key=self.api_key,
+            context_window=self.context_window,
+            is_chat_model=True,
+            is_function_calling_model=False,
+            temperature=self.temperature,
+            max_tokens=self.max_tokens,
+        )
+
+    async def get_response(
+        self, prompt: str, texts: list[str], tone_name: str | None = None
+    ) -> str:
+        """Get a text response using TreeSummarize for non-function-calling models"""
+        summarizer = TreeSummarize(verbose=False)
+        response = await summarizer.aget_response(prompt, texts, tone_name=tone_name)
+        return str(response).strip()
+
+    async def get_structured_response(
+        self,
+        prompt: str,
+        texts: list[str],
+        output_cls: Type[T],
+        tone_name: str | None = None,
+    ) -> T:
+        """Get structured output from LLM for non-function-calling models"""
+        summarizer = TreeSummarize(verbose=True)
+        response = await summarizer.aget_response(prompt, texts, tone_name=tone_name)
+
+        output_parser = PydanticOutputParser(output_cls)
+
+        program = LLMTextCompletionProgram.from_defaults(
+            output_parser=output_parser,
+            prompt_template_str=STRUCTURED_RESPONSE_PROMPT_TEMPLATE,
+            verbose=False,
+        )
+
+        format_instructions = output_parser.format(
+            "Please structure the above information in the following JSON format:"
+        )
+
+        output = await program.acall(
+            analysis=str(response), format_instructions=format_instructions
+        )
+
+        return output
diff --git a/server/reflector/llm/__init__.py b/server/reflector/llm/__init__.py
deleted file mode 100644
index 446a20d6..00000000
--- a/server/reflector/llm/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .base import LLM  # noqa: F401
-from .llm_params import LLMTaskParams  # noqa: F401
diff --git a/server/reflector/llm/base.py b/server/reflector/llm/base.py
deleted file mode 100644
index d26ee36e..00000000
--- a/server/reflector/llm/base.py
+++ /dev/null
@@ -1,347 +0,0 @@
-import importlib
-import json
-import re
-from typing import TypeVar
-
-import nltk
-from prometheus_client import Counter, Histogram
-from transformers import GenerationConfig
-
-from reflector.llm.llm_params import TaskParams
-from reflector.logger import logger as reflector_logger
-from reflector.settings import settings
-from reflector.utils.retry import retry
-
-T = TypeVar("T", bound="LLM")
-
-
-class LLM:
-    _nltk_downloaded = False
-    _registry = {}
-    model_name: str
-    m_generate = Histogram(
-        "llm_generate",
-        "Time spent in LLM.generate",
-        ["backend"],
-    )
-    m_generate_call = Counter(
-        "llm_generate_call",
-        "Number of calls to LLM.generate",
-        ["backend"],
-    )
-    m_generate_success = Counter(
-        "llm_generate_success",
-        "Number of successful calls to LLM.generate",
-        ["backend"],
-    )
-    m_generate_failure = Counter(
-        "llm_generate_failure",
-        "Number of failed calls to LLM.generate",
-        ["backend"],
-    )
-
-    @classmethod
-    def ensure_nltk(cls):
-        """
-        Make sure NLTK package is installed. Searches in the cache and
-        downloads only if needed.
-        """
-        if not cls._nltk_downloaded:
-            nltk.download("punkt_tab")
-            # For POS tagging
-            nltk.download("averaged_perceptron_tagger_eng")
-            cls._nltk_downloaded = True
-
-    @classmethod
-    def register(cls, name, klass):
-        cls._registry[name] = klass
-
-    @classmethod
-    def get_instance(cls, model_name: str | None = None, name: str = None) -> T:
-        """
-        Return an instance depending on the settings.
-        Settings used:
-
-        - `LLM_BACKEND`: key of the backend
-        - `LLM_URL`: url of the backend
-        """
-        if name is None:
-            name = settings.LLM_BACKEND
-        if name not in cls._registry:
-            module_name = f"reflector.llm.llm_{name}"
-            importlib.import_module(module_name)
-        cls.ensure_nltk()
-
-        return cls._registry[name](model_name)
-
-    def get_model_name(self) -> str:
-        """
-        Get the currently set model name
-        """
-        return self._get_model_name()
-
-    def _get_model_name(self) -> str:
-        pass
-
-    def set_model_name(self, model_name: str) -> bool:
-        """
-        Update the model name with the provided model name
-        """
-        return self._set_model_name(model_name)
-
-    def _set_model_name(self, model_name: str) -> bool:
-        raise NotImplementedError
-
-    @property
-    def template(self) -> str:
-        """
-        Return the LLM Prompt template
-        """
-        return """
-        ### Human:
-        {instruct}
-
-        {text}
-
-        ### Assistant:
-        """
-
-    def __init__(self):
-        name = self.__class__.__name__
-        self.m_generate = self.m_generate.labels(name)
-        self.m_generate_call = self.m_generate_call.labels(name)
-        self.m_generate_success = self.m_generate_success.labels(name)
-        self.m_generate_failure = self.m_generate_failure.labels(name)
-        self.detokenizer = nltk.tokenize.treebank.TreebankWordDetokenizer()
-
-    @property
-    def tokenizer(self):
-        """
-        Return the tokenizer instance used by LLM
-        """
-        return self._get_tokenizer()
-
-    def _get_tokenizer(self):
-        pass
-
-    def has_structured_output(self):
-        # whether implementation supports structured output
-        # on the model side (otherwise it's prompt engineering)
-        return False
-
-    async def generate(
-        self,
-        prompt: str,
-        logger: reflector_logger,
-        gen_schema: dict | None = None,
-        gen_cfg: GenerationConfig | None = None,
-        **kwargs,
-    ) -> dict:
-        logger.info("LLM generate", prompt=repr(prompt))
-
-        if gen_cfg:
-            gen_cfg = gen_cfg.to_dict()
-        self.m_generate_call.inc()
-        try:
-            with self.m_generate.time():
-                result = await retry(self._generate)(
-                    prompt=prompt,
-                    gen_schema=gen_schema,
-                    gen_cfg=gen_cfg,
-                    logger=logger,
-                    **kwargs,
-                )
-            self.m_generate_success.inc()
-
-        except Exception:
-            logger.exception("Failed to call llm after retrying")
-            self.m_generate_failure.inc()
-            raise
-
-        logger.debug("LLM result [raw]", result=repr(result))
-        if isinstance(result, str):
-            result = self._parse_json(result)
-        logger.debug("LLM result [parsed]", result=repr(result))
-
-        return result
-
-    async def completion(
-        self, messages: list, logger: reflector_logger, **kwargs
-    ) -> dict:
-        """
-        Use /v1/chat/completion Open-AI compatible endpoint from the URL
-        It's up to the user to validate anything or transform the result
-        """
-        logger.info("LLM completions", messages=messages)
-
-        try:
-            with self.m_generate.time():
-                result = await retry(self._completion)(
-                    messages=messages, **{**kwargs, "logger": logger}
-                )
-            self.m_generate_success.inc()
-        except Exception:
-            logger.exception("Failed to call llm after retrying")
-            self.m_generate_failure.inc()
-            raise
-
-        logger.debug("LLM completion result", result=repr(result))
-        return result
-
-    def ensure_casing(self, title: str) -> str:
-        """
-        LLM takes care of word casing, but in rare cases this
-        can falter. This is a fallback to ensure the casing of
-        topics is in a proper format.
-
-        We select nouns, verbs and adjectives and check if camel
-         casing is present and fix it, if not. Will not perform
-         any other changes.
-        """
-        tokens = nltk.word_tokenize(title)
-        pos_tags = nltk.pos_tag(tokens)
-        camel_cased = []
-
-        whitelisted_pos_tags = [
-            "NN",
-            "NNS",
-            "NNP",
-            "NNPS",  # Noun POS
-            "VB",
-            "VBD",
-            "VBG",
-            "VBN",
-            "VBP",
-            "VBZ",  # Verb POS
-            "JJ",
-            "JJR",
-            "JJS",  # Adjective POS
-        ]
-
-        # If at all there is an exception, do not block other reflector
-        # processes. Return the LLM generated title, at the least.
-        try:
-            for word, pos in pos_tags:
-                if pos in whitelisted_pos_tags and word[0].islower():
-                    camel_cased.append(word[0].upper() + word[1:])
-                else:
-                    camel_cased.append(word)
-            modified_title = self.detokenizer.detokenize(camel_cased)
-
-            # Irrespective of casing changes, the starting letter
-            # of title is always upper-cased
-            title = modified_title[0].upper() + modified_title[1:]
-        except Exception as e:
-            reflector_logger.info(
-                f"Failed to ensure casing on {title=} with exception : {str(e)}"
-            )
-
-        return title
-
-    def trim_title(self, title: str) -> str:
-        """
-        List of manual trimming to the title.
-
-        Longer titles are prone to run into A prefix of phrases that don't
-        really add any descriptive information and in some cases, this
-        behaviour can be repeated for several consecutive topics. Trim the
-        titles to maintain quality of titles.
-        """
-        phrases_to_remove = ["Discussing", "Discussion on", "Discussion about"]
-        try:
-            pattern = (
-                r"\b(?:"
-                + "|".join(re.escape(phrase) for phrase in phrases_to_remove)
-                + r")\b"
-            )
-            title = re.sub(pattern, "", title, flags=re.IGNORECASE)
-        except Exception as e:
-            reflector_logger.info(f"Failed to trim {title=} with exception : {str(e)}")
-        return title
-
-    async def _generate(
-        self, prompt: str, gen_schema: dict | None, gen_cfg: dict | None, **kwargs
-    ) -> str:
-        raise NotImplementedError
-
-    async def _completion(self, messages: list, **kwargs) -> dict:
-        raise NotImplementedError
-
-    def _parse_json(self, result: str) -> dict:
-        result = result.strip()
-        # try detecting code block if exist
-        # starts with ```json\n, ends with ```
-        # or starts with ```\n, ends with ```
-        # or starts with \n```javascript\n, ends with ```
-
-        regex = r"```(json|javascript|)?(.*)```"
-        matches = re.findall(regex, result.strip(), re.MULTILINE | re.DOTALL)
-        if matches:
-            result = matches[0][1]
-
-        else:
-            # maybe the prompt has been started with ```json
-            # so if text ends with ```, just remove it and use it as json
-            if result.endswith("```"):
-                result = result[:-3]
-
-        return json.loads(result.strip())
-
-    def text_token_threshold(self, task_params: TaskParams | None) -> int:
-        """
-        Choose the token size to set as the threshold to pack the LLM calls
-        """
-        buffer_token_size = 100
-        default_output_tokens = 1000
-        context_window = self.tokenizer.model_max_length
-        tokens = self.tokenizer.tokenize(
-            self.create_prompt(instruct=task_params.instruct, text="")
-        )
-        threshold = context_window - len(tokens) - buffer_token_size
-        if task_params.gen_cfg:
-            threshold -= task_params.gen_cfg.max_new_tokens
-        else:
-            threshold -= default_output_tokens
-        return threshold
-
-    def split_corpus(
-        self,
-        corpus: str,
-        task_params: TaskParams,
-        token_threshold: int | None = None,
-    ) -> list[str]:
-        """
-        Split the input to the LLM due to CUDA memory limitations and LLM context window
-        restrictions.
-
-        Accumulate tokens from full sentences till threshold and yield accumulated
-        tokens. Reset accumulation when threshold is reached and repeat process.
-        """
-        if not token_threshold:
-            token_threshold = self.text_token_threshold(task_params=task_params)
-
-        accumulated_tokens = []
-        accumulated_sentences = []
-        accumulated_token_count = 0
-        corpus_sentences = nltk.sent_tokenize(corpus)
-
-        for sentence in corpus_sentences:
-            tokens = self.tokenizer.tokenize(sentence)
-            if accumulated_token_count + len(tokens) <= token_threshold:
-                accumulated_token_count += len(tokens)
-                accumulated_tokens.extend(tokens)
-                accumulated_sentences.append(sentence)
-            else:
-                yield "".join(accumulated_sentences)
-                accumulated_token_count = len(tokens)
-                accumulated_tokens = tokens
-                accumulated_sentences = [sentence]
-
-        if accumulated_tokens:
-            yield " ".join(accumulated_sentences)
-
-    def create_prompt(self, instruct: str, text: str) -> str:
-        """
-        Create a consumable prompt based on the prompt template
-        """
-        return self.template.format(instruct=instruct, text=text)
diff --git a/server/reflector/llm/llm_modal.py b/server/reflector/llm/llm_modal.py
deleted file mode 100644
index 327300c7..00000000
--- a/server/reflector/llm/llm_modal.py
+++ /dev/null
@@ -1,155 +0,0 @@
-import httpx
-from transformers import AutoTokenizer, GenerationConfig
-
-from reflector.llm.base import LLM
-from reflector.logger import logger as reflector_logger
-from reflector.settings import settings
-from reflector.utils.retry import retry
-
-
-class ModalLLM(LLM):
-    def __init__(self, model_name: str | None = None):
-        super().__init__()
-        self.timeout = settings.LLM_TIMEOUT
-        self.llm_url = settings.LLM_URL + "/llm"
-        self.headers = {
-            "Authorization": f"Bearer {settings.LLM_MODAL_API_KEY}",
-        }
-        self._set_model_name(model_name if model_name else settings.DEFAULT_LLM)
-
-    @property
-    def supported_models(self):
-        """
-        List of currently supported models on this GPU platform
-        """
-        # TODO: Query the specific GPU platform
-        # Replace this with a HTTP call
-        return [
-            "lmsys/vicuna-13b-v1.5",
-            "HuggingFaceH4/zephyr-7b-alpha",
-            "NousResearch/Hermes-3-Llama-3.1-8B",
-        ]
-
-    async def _generate(
-        self, prompt: str, gen_schema: dict | None, gen_cfg: dict | None, **kwargs
-    ) -> str:
-        json_payload = {"prompt": prompt}
-        if gen_schema:
-            json_payload["gen_schema"] = gen_schema
-        if gen_cfg:
-            json_payload["gen_cfg"] = gen_cfg
-
-        # Handing over generation of the final summary to Zephyr model
-        # but replacing the Vicuna model will happen after more testing
-        # TODO: Create a mapping of model names and cloud deployments
-        if self.model_name == "HuggingFaceH4/zephyr-7b-alpha":
-            self.llm_url = settings.ZEPHYR_LLM_URL + "/llm"
-
-        async with httpx.AsyncClient() as client:
-            response = await retry(client.post)(
-                self.llm_url,
-                headers=self.headers,
-                json=json_payload,
-                timeout=self.timeout,
-                retry_timeout=60 * 5,
-                follow_redirects=True,
-                logger=kwargs.get("logger", reflector_logger),
-            )
-            response.raise_for_status()
-            text = response.json()["text"]
-            return text
-
-    async def _completion(self, messages: list, **kwargs) -> dict:
-        # returns full api response
-        kwargs.setdefault("temperature", 0.3)
-        kwargs.setdefault("max_tokens", 2048)
-        kwargs.setdefault("stream", False)
-        kwargs.setdefault("repetition_penalty", 1)
-        kwargs.setdefault("top_p", 1)
-        kwargs.setdefault("top_k", -1)
-        kwargs.setdefault("min_p", 0.05)
-        data = {"messages": messages, "model": self.model_name, **kwargs}
-
-        if self.model_name == "NousResearch/Hermes-3-Llama-3.1-8B":
-            self.llm_url = settings.HERMES_3_8B_LLM_URL + "/v1/chat/completions"
-
-        async with httpx.AsyncClient() as client:
-            response = await retry(client.post)(
-                self.llm_url,
-                headers=self.headers,
-                json=data,
-                timeout=self.timeout,
-                retry_timeout=60 * 5,
-                follow_redirects=True,
-                logger=kwargs.get("logger", reflector_logger),
-            )
-            response.raise_for_status()
-            return response.json()
-
-    def _set_model_name(self, model_name: str) -> bool:
-        """
-        Set the model name
-        """
-        # Abort, if the model is not supported
-        if model_name not in self.supported_models:
-            reflector_logger.info(
-                f"Attempted to change {model_name=}, but is not supported."
-                f"Setting model and tokenizer failed !"
-            )
-            return False
-        # Abort, if the model is already set
-        elif hasattr(self, "model_name") and model_name == self._get_model_name():
-            reflector_logger.info("No change in model. Setting model skipped.")
-            return False
-        # Update model name and tokenizer
-        self.model_name = model_name
-        self.llm_tokenizer = AutoTokenizer.from_pretrained(
-            self.model_name, cache_dir=settings.CACHE_DIR
-        )
-        reflector_logger.info(f"Model set to {model_name=}. Tokenizer updated.")
-        return True
-
-    def _get_tokenizer(self) -> AutoTokenizer:
-        """
-        Return the currently used LLM tokenizer
-        """
-        return self.llm_tokenizer
-
-    def _get_model_name(self) -> str:
-        """
-        Return the current model name from the instance details
-        """
-        return self.model_name
-
-
-LLM.register("modal", ModalLLM)
-
-if __name__ == "__main__":
-    from reflector.logger import logger
-
-    async def main():
-        llm = ModalLLM()
-        prompt = llm.create_prompt(
-            instruct="Complete the following task",
-            text="Tell me a joke about programming.",
-        )
-        result = await llm.generate(prompt=prompt, logger=logger)
-        print(result)
-
-        gen_schema = {
-            "type": "object",
-            "properties": {"response": {"type": "string"}},
-        }
-
-        result = await llm.generate(prompt=prompt, gen_schema=gen_schema, logger=logger)
-        print(result)
-
-        gen_cfg = GenerationConfig(max_new_tokens=150)
-        result = await llm.generate(
-            prompt=prompt, gen_cfg=gen_cfg, gen_schema=gen_schema, logger=logger
-        )
-        print(result)
-
-    import asyncio
-
-    asyncio.run(main())
diff --git a/server/reflector/llm/llm_openai.py b/server/reflector/llm/llm_openai.py
deleted file mode 100644
index e28211ef..00000000
--- a/server/reflector/llm/llm_openai.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import httpx
-from transformers import GenerationConfig
-
-from reflector.llm.base import LLM
-from reflector.logger import logger
-from reflector.settings import settings
-
-
-class OpenAILLM(LLM):
-    def __init__(self, model_name: str | None = None, **kwargs):
-        super().__init__(**kwargs)
-        self.openai_key = settings.LLM_OPENAI_KEY
-        self.openai_url = settings.LLM_URL
-        self.openai_model = settings.LLM_OPENAI_MODEL
-        self.openai_temperature = settings.LLM_OPENAI_TEMPERATURE
-        self.timeout = settings.LLM_TIMEOUT
-        self.max_tokens = settings.LLM_MAX_TOKENS
-        logger.info(f"LLM use openai backend at {self.openai_url}")
-
-    async def _generate(
-        self,
-        prompt: str,
-        gen_schema: dict | None,
-        gen_cfg: GenerationConfig | None,
-        **kwargs,
-    ) -> str:
-        headers = {
-            "Content-Type": "application/json",
-            "Authorization": f"Bearer {self.openai_key}",
-        }
-
-        async with httpx.AsyncClient(timeout=self.timeout) as client:
-            response = await client.post(
-                self.openai_url,
-                headers=headers,
-                json={
-                    "model": self.openai_model,
-                    "prompt": prompt,
-                    "max_tokens": self.max_tokens,
-                    "temperature": self.openai_temperature,
-                },
-            )
-            response.raise_for_status()
-            result = response.json()
-            return result["choices"][0]["text"]
-
-
-LLM.register("openai", OpenAILLM)
diff --git a/server/reflector/llm/llm_params.py b/server/reflector/llm/llm_params.py
deleted file mode 100644
index fbe73bd9..00000000
--- a/server/reflector/llm/llm_params.py
+++ /dev/null
@@ -1,219 +0,0 @@
-from typing import Optional, TypeVar
-
-from pydantic import BaseModel
-from transformers import GenerationConfig
-
-
-class TaskParams(BaseModel, arbitrary_types_allowed=True):
-    instruct: str
-    gen_cfg: Optional[GenerationConfig] = None
-    gen_schema: Optional[dict] = None
-
-
-T = TypeVar("T", bound="LLMTaskParams")
-
-
-class LLMTaskParams:
-    _registry = {}
-
-    @classmethod
-    def register(cls, task, klass) -> None:
-        cls._registry[task] = klass
-
-    @classmethod
-    def get_instance(cls, task: str) -> T:
-        return cls._registry[task]()
-
-    @property
-    def task_params(self) -> TaskParams | None:
-        """
-        Fetch the task related parameters
-        """
-        return self._get_task_params()
-
-    def _get_task_params(self) -> None:
-        pass
-
-
-class FinalLongSummaryParams(LLMTaskParams):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        self._gen_cfg = GenerationConfig(
-            max_new_tokens=1000, num_beams=3, do_sample=True, temperature=0.3
-        )
-        self._instruct = """
-        Take the key ideas and takeaways from the text and create a short
-         summary. Be sure to keep the length of the response to a minimum.
-         Do not include trivial information in the summary.
-          """
-        self._schema = {
-            "type": "object",
-            "properties": {"long_summary": {"type": "string"}},
-        }
-        self._task_params = TaskParams(
-            instruct=self._instruct, gen_schema=self._schema, gen_cfg=self._gen_cfg
-        )
-
-    def _get_task_params(self) -> TaskParams:
-        """gen_schema
-        Return the parameters associated with a specific LLM task
-        """
-        return self._task_params
-
-
-class FinalShortSummaryParams(LLMTaskParams):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        self._gen_cfg = GenerationConfig(
-            max_new_tokens=800, num_beams=3, do_sample=True, temperature=0.3
-        )
-        self._instruct = """
-        Take the key ideas and takeaways from the text and create a short
-         summary. Be sure to keep the length of the response to a minimum.
-         Do not include trivial information in the summary.
-          """
-        self._schema = {
-            "type": "object",
-            "properties": {"short_summary": {"type": "string"}},
-        }
-        self._task_params = TaskParams(
-            instruct=self._instruct, gen_schema=self._schema, gen_cfg=self._gen_cfg
-        )
-
-    def _get_task_params(self) -> TaskParams:
-        """
-        Return the parameters associated with a specific LLM task
-        """
-        return self._task_params
-
-
-class FinalTitleParams(LLMTaskParams):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        self._gen_cfg = GenerationConfig(
-            max_new_tokens=200, num_beams=5, do_sample=True, temperature=0.5
-        )
-        self._instruct = """
-            Combine the following individual titles into one single short title that
-            condenses the essence of all titles.
-        """
-        self._schema = {
-            "type": "object",
-            "properties": {"title": {"type": "string"}},
-        }
-        self._task_params = TaskParams(
-            instruct=self._instruct, gen_schema=self._schema, gen_cfg=self._gen_cfg
-        )
-
-    def _get_task_params(self) -> TaskParams:
-        """
-        Return the parameters associated with a specific LLM task
-        """
-        return self._task_params
-
-
-class TopicParams(LLMTaskParams):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        self._gen_cfg = GenerationConfig(
-            max_new_tokens=500, num_beams=6, do_sample=True, temperature=0.9
-        )
-        self._instruct = """
-                Create a JSON object as response.The JSON object must have 2 fields:
-                i) title and ii) summary.
-                For the title field, generate a very detailed and self-explanatory
-                 title for the given text. Let the title be as descriptive as possible.
-                For the summary field, summarize the given text in a maximum of
-                two sentences.
-            """
-        self._schema = {
-            "type": "object",
-            "properties": {
-                "title": {"type": "string"},
-                "summary": {"type": "string"},
-            },
-        }
-        self._task_params = TaskParams(
-            instruct=self._instruct, gen_schema=self._schema, gen_cfg=self._gen_cfg
-        )
-
-    def _get_task_params(self) -> TaskParams:
-        """
-        Return the parameters associated with a specific LLM task
-        """
-        return self._task_params
-
-
-class BulletedSummaryParams(LLMTaskParams):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        self._gen_cfg = GenerationConfig(
-            max_new_tokens=800,
-            num_beams=1,
-            do_sample=True,
-            temperature=0.2,
-            early_stopping=True,
-        )
-        self._instruct = """
-        Given a meeting transcript, extract the key things discussed in the
-         form of a list.
-
-        While generating the response, follow the constraints mentioned below.
-
-        Summary constraints:
-        i) Do not add new content, except to fix spelling or punctuation.
-        ii) Do not add any prefixes or numbering in the response.
-        iii) The summarization should be as information dense as possible.
-        iv) Do not add any additional sections like Note, Conclusion, etc. in
-        the response.
-
-        Response format:
-        i) The response should be in the form of a bulleted list.
-        ii) Iteratively merge all the relevant paragraphs together to keep the
-         number of paragraphs to a minimum.
-        iii) Remove any unfinished sentences from the final response.
-        iv) Do not include narrative or reporting clauses.
-        v) Use "*" as the bullet icon.
-    """
-        self._task_params = TaskParams(
-            instruct=self._instruct, gen_schema=None, gen_cfg=self._gen_cfg
-        )
-
-    def _get_task_params(self) -> TaskParams:
-        """gen_schema
-        Return the parameters associated with a specific LLM task
-        """
-        return self._task_params
-
-
-class MergedSummaryParams(LLMTaskParams):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        self._gen_cfg = GenerationConfig(
-            max_new_tokens=600,
-            num_beams=1,
-            do_sample=True,
-            temperature=0.2,
-            early_stopping=True,
-        )
-        self._instruct = """
-        Given the key points of a meeting, summarize the points to describe the
-         meeting in the form of paragraphs.
-        """
-        self._task_params = TaskParams(
-            instruct=self._instruct, gen_schema=None, gen_cfg=self._gen_cfg
-        )
-
-    def _get_task_params(self) -> TaskParams:
-        """gen_schema
-        Return the parameters associated with a specific LLM task
-        """
-        return self._task_params
-
-
-LLMTaskParams.register("topic", TopicParams)
-LLMTaskParams.register("final_title", FinalTitleParams)
-LLMTaskParams.register("final_short_summary", FinalShortSummaryParams)
-LLMTaskParams.register("final_long_summary", FinalLongSummaryParams)
-LLMTaskParams.register("bullet_summary", BulletedSummaryParams)
-LLMTaskParams.register("merged_summary", MergedSummaryParams)
diff --git a/server/reflector/llm/openai_llm.py b/server/reflector/llm/openai_llm.py
deleted file mode 100644
index 90f31869..00000000
--- a/server/reflector/llm/openai_llm.py
+++ /dev/null
@@ -1,118 +0,0 @@
-import httpx
-from transformers import AutoTokenizer
-
-from reflector.logger import logger
-
-
-def apply_gen_config(payload: dict, gen_cfg) -> None:
-    """Apply generation config overrides to the payload."""
-    config_mapping = {
-        "temperature": "temperature",
-        "max_new_tokens": "max_tokens",
-        "max_tokens": "max_tokens",
-        "top_p": "top_p",
-        "frequency_penalty": "frequency_penalty",
-        "presence_penalty": "presence_penalty",
-    }
-
-    for cfg_attr, payload_key in config_mapping.items():
-        value = getattr(gen_cfg, cfg_attr, None)
-        if value is not None:
-            payload[payload_key] = value
-            if cfg_attr == "max_new_tokens":  # Handle max_new_tokens taking precedence
-                break
-
-
-class OpenAILLM:
-    def __init__(self, config_prefix: str, settings):
-        self.config_prefix = config_prefix
-        self.settings_obj = settings
-        self.model_name = getattr(settings, f"{config_prefix}_MODEL")
-        self.url = getattr(settings, f"{config_prefix}_LLM_URL")
-        self.api_key = getattr(settings, f"{config_prefix}_LLM_API_KEY")
-
-        timeout = getattr(settings, f"{config_prefix}_LLM_TIMEOUT", 300)
-        self.temperature = getattr(settings, f"{config_prefix}_LLM_TEMPERATURE", 0.7)
-        self.max_tokens = getattr(settings, f"{config_prefix}_LLM_MAX_TOKENS", 1024)
-        self.client = httpx.AsyncClient(timeout=timeout)
-
-        # Use a tokenizer that approximates OpenAI token counting
-        tokenizer_name = getattr(settings, f"{config_prefix}_TOKENIZER", "gpt2")
-        try:
-            self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
-        except Exception:
-            logger.debug(
-                f"Failed to load tokenizer '{tokenizer_name}', falling back to default 'gpt2' tokenizer"
-            )
-            self.tokenizer = AutoTokenizer.from_pretrained("gpt2")
-
-    async def generate(
-        self, prompt: str, gen_schema=None, gen_cfg=None, logger=None
-    ) -> str:
-        if logger:
-            logger.debug(
-                "OpenAI LLM generate",
-                prompt=repr(prompt[:100] + "..." if len(prompt) > 100 else prompt),
-            )
-
-        messages = [{"role": "user", "content": prompt}]
-        result = await self.completion(
-            messages, gen_schema=gen_schema, gen_cfg=gen_cfg, logger=logger
-        )
-        return result["choices"][0]["message"]["content"]
-
-    async def completion(
-        self, messages: list, gen_schema=None, gen_cfg=None, logger=None, **kwargs
-    ) -> dict:
-        if logger:
-            logger.info("OpenAI LLM completion", messages_count=len(messages))
-
-        payload = {
-            "model": self.model_name,
-            "messages": messages,
-            "temperature": self.temperature,
-            "max_tokens": self.max_tokens,
-        }
-
-        # Apply generation config overrides
-        if gen_cfg:
-            apply_gen_config(payload, gen_cfg)
-
-        # Apply structured output schema
-        if gen_schema:
-            payload["response_format"] = {
-                "type": "json_schema",
-                "json_schema": {"name": "response", "schema": gen_schema},
-            }
-
-        headers = {
-            "Content-Type": "application/json",
-            "Authorization": f"Bearer {self.api_key}",
-        }
-
-        url = f"{self.url.rstrip('/')}/chat/completions"
-
-        if logger:
-            logger.debug(
-                "OpenAI API request", url=url, payload_keys=list(payload.keys())
-            )
-
-        response = await self.client.post(url, json=payload, headers=headers)
-        response.raise_for_status()
-
-        result = response.json()
-
-        if logger:
-            logger.debug(
-                "OpenAI API response",
-                status_code=response.status_code,
-                choices_count=len(result.get("choices", [])),
-            )
-
-        return result
-
-    async def __aenter__(self):
-        return self
-
-    async def __aexit__(self, exc_type, exc_val, exc_tb):
-        await self.client.aclose()
diff --git a/server/reflector/processors/summary/summary_builder.py b/server/reflector/processors/summary/summary_builder.py
index c6744183..ccbae48a 100644
--- a/server/reflector/processors/summary/summary_builder.py
+++ b/server/reflector/processors/summary/summary_builder.py
@@ -12,15 +12,9 @@ from textwrap import dedent
 from typing import Type, TypeVar
 
 import structlog
-from llama_index.core import Settings
-from llama_index.core.output_parsers import PydanticOutputParser
-from llama_index.core.program import LLMTextCompletionProgram
-from llama_index.core.response_synthesizers import TreeSummarize
-from llama_index.llms.openai_like import OpenAILike
 from pydantic import BaseModel, Field
 
-from reflector.llm.base import LLM
-from reflector.llm.openai_llm import OpenAILLM
+from reflector.llm import LLM
 from reflector.settings import settings
 
 T = TypeVar("T", bound=BaseModel)
@@ -168,23 +162,12 @@ class SummaryBuilder:
         self.summaries: list[dict[str, str]] = []
         self.subjects: list[str] = []
         self.transcription_type: TranscriptionType | None = None
-        self.llm_instance: LLM = llm
+        self.llm: LLM = llm
         self.model_name: str = llm.model_name
         self.logger = logger or structlog.get_logger()
         if filename:
             self.read_transcript_from_file(filename)
 
-        Settings.llm = OpenAILike(
-            model=llm.model_name,
-            api_base=llm.url,
-            api_key=llm.api_key,
-            context_window=settings.SUMMARY_LLM_CONTEXT_SIZE_TOKENS,
-            is_chat_model=True,
-            is_function_calling_model=llm.has_structured_output,
-            temperature=llm.temperature,
-            max_tokens=llm.max_tokens,
-        )
-
     def read_transcript_from_file(self, filename: str) -> None:
         """
         Load a transcript from a text file.
@@ -202,40 +185,16 @@ class SummaryBuilder:
         self.transcript = transcript
 
     def set_llm_instance(self, llm: LLM) -> None:
-        self.llm_instance = llm
+        self.llm = llm
 
     async def _get_structured_response(
         self, prompt: str, output_cls: Type[T], tone_name: str | None = None
-    ) -> Type[T]:
+    ) -> T:
         """Generic function to get structured output from LLM for non-function-calling models."""
-        # First, use TreeSummarize to get the response
-        summarizer = TreeSummarize(verbose=True)
-
-        response = await summarizer.aget_response(
-            prompt, [self.transcript], tone_name=tone_name
+        return await self.llm.get_structured_response(
+            prompt, [self.transcript], output_cls, tone_name=tone_name
         )
 
-        # Then, use PydanticOutputParser to structure the response
-        output_parser = PydanticOutputParser(output_cls)
-
-        prompt_template_str = STRUCTURED_RESPONSE_PROMPT_TEMPLATE
-
-        program = LLMTextCompletionProgram.from_defaults(
-            output_parser=output_parser,
-            prompt_template_str=prompt_template_str,
-            verbose=False,
-        )
-
-        format_instructions = output_parser.format(
-            "Please structure the above information in the following JSON format:"
-        )
-
-        output = await program.acall(
-            analysis=str(response), format_instructions=format_instructions
-        )
-
-        return output
-
     # ----------------------------------------------------------------------------
     # Participants
     # ----------------------------------------------------------------------------
@@ -354,19 +313,18 @@ class SummaryBuilder:
     async def generate_subject_summaries(self) -> None:
         """Generate detailed summaries for each extracted subject."""
         assert self.transcript is not None
-        summarizer = TreeSummarize(verbose=False)
         summaries = []
 
         for subject in self.subjects:
             detailed_prompt = DETAILED_SUBJECT_PROMPT_TEMPLATE.format(subject=subject)
 
-            detailed_response = await summarizer.aget_response(
+            detailed_response = await self.llm.get_response(
                 detailed_prompt, [self.transcript], tone_name="Topic assistant"
             )
 
             paragraph_prompt = PARAGRAPH_SUMMARY_PROMPT
 
-            paragraph_response = await summarizer.aget_response(
+            paragraph_response = await self.llm.get_response(
                 paragraph_prompt, [str(detailed_response)], tone_name="Topic summarizer"
             )
 
@@ -377,7 +335,6 @@ class SummaryBuilder:
 
     async def generate_recap(self) -> None:
         """Generate a quick recap from the subject summaries."""
-        summarizer = TreeSummarize(verbose=True)
 
         summaries_text = "\n\n".join(
             [
@@ -388,7 +345,7 @@ class SummaryBuilder:
 
         recap_prompt = RECAP_PROMPT
 
-        recap_response = await summarizer.aget_response(
+        recap_response = await self.llm.get_response(
             recap_prompt, [summaries_text], tone_name="Recap summarizer"
         )
 
@@ -483,7 +440,7 @@ if __name__ == "__main__":
     async def main():
         # build the summary
 
-        llm = OpenAILLM(config_prefix="SUMMARY", settings=settings)
+        llm = LLM(settings=settings)
         sm = SummaryBuilder(llm=llm, filename=args.transcript)
 
         if args.subjects:
diff --git a/server/reflector/processors/transcript_final_summary.py b/server/reflector/processors/transcript_final_summary.py
index 9cfc4a00..0b4a594c 100644
--- a/server/reflector/processors/transcript_final_summary.py
+++ b/server/reflector/processors/transcript_final_summary.py
@@ -1,4 +1,4 @@
-from reflector.llm.openai_llm import OpenAILLM
+from reflector.llm import LLM
 from reflector.processors.base import Processor
 from reflector.processors.summary.summary_builder import SummaryBuilder
 from reflector.processors.types import FinalLongSummary, FinalShortSummary, TitleSummary
@@ -17,7 +17,7 @@ class TranscriptFinalSummaryProcessor(Processor):
         super().__init__(**kwargs)
         self.transcript = transcript
         self.chunks: list[TitleSummary] = []
-        self.llm = OpenAILLM(config_prefix="SUMMARY", settings=settings)
+        self.llm = LLM(settings=settings)
         self.builder = None
 
     async def _push(self, data: TitleSummary):
diff --git a/server/reflector/processors/transcript_final_title.py b/server/reflector/processors/transcript_final_title.py
index 4b486c08..75b62b5a 100644
--- a/server/reflector/processors/transcript_final_title.py
+++ b/server/reflector/processors/transcript_final_title.py
@@ -1,67 +1,72 @@
-from reflector.llm import LLM, LLMTaskParams
+from textwrap import dedent
+
+from reflector.llm import LLM
 from reflector.processors.base import Processor
 from reflector.processors.types import FinalTitle, TitleSummary
+from reflector.settings import settings
+from reflector.utils.text import clean_title
+
+TITLE_PROMPT = dedent(
+    """
+    Generate a concise title for this meeting based on the following topic titles.
+    Ignore casual conversation, greetings, or administrative matters.
+
+    The title must:
+    - Be maximum 10 words
+    - Use noun phrases when possible (e.g., "Q1 Budget Review" not "Reviewing the Q1 Budget")
+    - Avoid generic terms like "Team Meeting" or "Discussion"
+
+    If multiple unrelated topics were discussed, prioritize the most significant one.
+    or create a compound title (e.g., "Product Launch and Budget Planning").
+
+    <topics_discussed>
+    {titles}
+    </topics_discussed>
+
+    Do not explain, just output the meeting title as a single line.
+    """
+).strip()
 
 
 class TranscriptFinalTitleProcessor(Processor):
     """
-    Assemble all summary into a line-based json
+    Generate a final title from topic titles using LlamaIndex
     """
 
     INPUT_TYPE = TitleSummary
     OUTPUT_TYPE = FinalTitle
-    TASK = "final_title"
 
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
         self.chunks: list[TitleSummary] = []
-        self.llm = LLM.get_instance()
-        self.params = LLMTaskParams.get_instance(self.TASK).task_params
+        self.llm = LLM(settings=settings, temperature=0.5, max_tokens=200)
 
     async def _push(self, data: TitleSummary):
         self.chunks.append(data)
 
-    async def get_title(self, text: str) -> dict:
+    async def get_title(self, accumulated_titles: str) -> str:
         """
-        Generate a title for the whole recording
+        Generate a title for the whole recording using LLM
         """
-        chunks = list(self.llm.split_corpus(corpus=text, task_params=self.params))
+        prompt = TITLE_PROMPT.format(titles=accumulated_titles)
+        response = await self.llm.get_response(
+            prompt,
+            [accumulated_titles],
+            tone_name="Title generator",
+        )
 
-        if len(chunks) == 1:
-            chunk = chunks[0]
-            prompt = self.llm.create_prompt(instruct=self.params.instruct, text=chunk)
-            title_result = await self.llm.generate(
-                prompt=prompt,
-                gen_schema=self.params.gen_schema,
-                gen_cfg=self.params.gen_cfg,
-                logger=self.logger,
-            )
-            return title_result
-        else:
-            accumulated_titles = ""
-            for chunk in chunks:
-                prompt = self.llm.create_prompt(
-                    instruct=self.params.instruct, text=chunk
-                )
-                title_result = await self.llm.generate(
-                    prompt=prompt,
-                    gen_schema=self.params.gen_schema,
-                    gen_cfg=self.params.gen_cfg,
-                    logger=self.logger,
-                )
-                accumulated_titles += title_result["title"]
+        self.logger.info(f"Generated title response: {response}")
 
-            return await self.get_title(accumulated_titles)
+        return response
 
     async def _flush(self):
         if not self.chunks:
             self.logger.warning("No summary to output")
             return
 
-        accumulated_titles = ".".join([chunk.title for chunk in self.chunks])
-        title_result = await self.get_title(accumulated_titles)
-        final_title = self.llm.trim_title(title_result["title"])
-        final_title = self.llm.ensure_casing(final_title)
+        accumulated_titles = "\n".join([f"- {chunk.title}" for chunk in self.chunks])
+        title = await self.get_title(accumulated_titles)
+        title = clean_title(title)
 
-        final_title = FinalTitle(title=final_title)
+        final_title = FinalTitle(title=title)
         await self.emit(final_title)
diff --git a/server/reflector/processors/transcript_topic_detector.py b/server/reflector/processors/transcript_topic_detector.py
index dd14ce93..e0e306ce 100644
--- a/server/reflector/processors/transcript_topic_detector.py
+++ b/server/reflector/processors/transcript_topic_detector.py
@@ -1,7 +1,41 @@
-from reflector.llm import LLM, LLMTaskParams
+from textwrap import dedent
+
+from pydantic import BaseModel, Field
+
+from reflector.llm import LLM
 from reflector.processors.base import Processor
 from reflector.processors.types import TitleSummary, Transcript
 from reflector.settings import settings
+from reflector.utils.text import clean_title
+
+TOPIC_PROMPT = dedent(
+    """
+    Analyze the following transcript segment and extract the main topic being discussed.
+    Focus on the substantive content and ignore small talk or administrative chatter.
+
+    Create a title that:
+    - Captures the specific subject matter being discussed
+    - Is descriptive and self-explanatory
+    - Uses professional language
+    - Is specific rather than generic
+
+    For the summary:
+    - Summarize the key points in maximum two sentences
+    - Focus on what was discussed, decided, or accomplished
+    - Be concise but informative
+
+    <transcript>
+    {text}
+    </transcript>
+    """
+).strip()
+
+
+class TopicResponse(BaseModel):
+    """Structured response for topic detection"""
+
+    title: str = Field(description="A descriptive title for the topic being discussed")
+    summary: str = Field(description="A concise 1-2 sentence summary of the discussion")
 
 
 class TranscriptTopicDetectorProcessor(Processor):
@@ -11,7 +45,6 @@ class TranscriptTopicDetectorProcessor(Processor):
 
     INPUT_TYPE = Transcript
     OUTPUT_TYPE = TitleSummary
-    TASK = "topic"
 
     def __init__(
         self, min_transcript_length: int = int(settings.MIN_TRANSCRIPT_LENGTH), **kwargs
@@ -19,8 +52,7 @@ class TranscriptTopicDetectorProcessor(Processor):
         super().__init__(**kwargs)
         self.transcript = None
         self.min_transcript_length = min_transcript_length
-        self.llm = LLM.get_instance()
-        self.params = LLMTaskParams.get_instance(self.TASK).task_params
+        self.llm = LLM(settings=settings, temperature=0.9, max_tokens=500)
 
     async def _push(self, data: Transcript):
         if self.transcript is None:
@@ -34,18 +66,15 @@ class TranscriptTopicDetectorProcessor(Processor):
             return
         await self.flush()
 
-    async def get_topic(self, text: str) -> dict:
+    async def get_topic(self, text: str) -> TopicResponse:
         """
-        Generate a topic and description for a transcription excerpt
+        Generate a topic and description for a transcription excerpt using LLM
         """
-        prompt = self.llm.create_prompt(instruct=self.params.instruct, text=text)
-        topic_result = await self.llm.generate(
-            prompt=prompt,
-            gen_schema=self.params.gen_schema,
-            gen_cfg=self.params.gen_cfg,
-            logger=self.logger,
+        prompt = TOPIC_PROMPT.format(text=text)
+        response = await self.llm.get_structured_response(
+            prompt, [text], TopicResponse, tone_name="Topic analyzer"
         )
-        return topic_result
+        return response
 
     async def _flush(self):
         if not self.transcript:
@@ -53,13 +82,13 @@ class TranscriptTopicDetectorProcessor(Processor):
 
         text = self.transcript.text
         self.logger.info(f"Topic detector got {len(text)} length transcript")
+
         topic_result = await self.get_topic(text=text)
-        title = self.llm.trim_title(topic_result["title"])
-        title = self.llm.ensure_casing(title)
+        title = clean_title(topic_result.title)
 
         summary = TitleSummary(
             title=title,
-            summary=topic_result["summary"],
+            summary=topic_result.summary,
             timestamp=self.transcript.timestamp,
             duration=self.transcript.duration,
             transcript=self.transcript,
diff --git a/server/reflector/processors/transcript_translator.py b/server/reflector/processors/transcript_translator.py
index 8cb9b676..2489e563 100644
--- a/server/reflector/processors/transcript_translator.py
+++ b/server/reflector/processors/transcript_translator.py
@@ -13,14 +13,13 @@ class TranscriptTranslatorProcessor(Processor):
 
     INPUT_TYPE = Transcript
     OUTPUT_TYPE = Transcript
-    TASK = "translate"
 
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
         self.transcript = None
         self.translate_url = settings.TRANSLATE_URL
         self.timeout = settings.TRANSLATE_TIMEOUT
-        self.headers = {"Authorization": f"Bearer {settings.LLM_MODAL_API_KEY}"}
+        self.headers = {"Authorization": f"Bearer {settings.TRANSCRIPT_MODAL_API_KEY}"}
 
     async def _push(self, data: Transcript):
         self.transcript = data
diff --git a/server/reflector/settings.py b/server/reflector/settings.py
index 6cd54be5..30af270b 100644
--- a/server/reflector/settings.py
+++ b/server/reflector/settings.py
@@ -9,13 +9,14 @@ class Settings(BaseSettings):
     )
 
     # CORS
+    UI_BASE_URL: str = "http://localhost:3000"
     CORS_ORIGIN: str = "*"
     CORS_ALLOW_CREDENTIALS: bool = False
 
     # Database
     DATABASE_URL: str = "sqlite:///./reflector.sqlite3"
 
-    # local data directory (audio for no)
+    # local data directory
     DATA_DIR: str = "./data"
 
     # Audio Transcription
@@ -24,10 +25,6 @@ class Settings(BaseSettings):
     TRANSCRIPT_URL: str | None = None
     TRANSCRIPT_TIMEOUT: int = 90
 
-    # Translate into the target language
-    TRANSLATE_URL: str | None = None
-    TRANSLATE_TIMEOUT: int = 90
-
     # Audio transcription modal.com configuration
     TRANSCRIPT_MODAL_API_KEY: str | None = None
 
@@ -40,31 +37,15 @@ class Settings(BaseSettings):
     TRANSCRIPT_STORAGE_AWS_ACCESS_KEY_ID: str | None = None
     TRANSCRIPT_STORAGE_AWS_SECRET_ACCESS_KEY: str | None = None
 
+    # Translate into the target language
+    TRANSLATE_URL: str | None = None
+    TRANSLATE_TIMEOUT: int = 90
+
     # LLM
-    # available backend: openai, modal
-    LLM_BACKEND: str = "modal"
-
-    # LLM common configuration
+    LLM_MODEL: str = "microsoft/phi-4"
     LLM_URL: str | None = None
-    LLM_HOST: str = "localhost"
-    LLM_PORT: int = 7860
-    LLM_OPENAI_KEY: str | None = None
-    LLM_OPENAI_MODEL: str = "gpt-3.5-turbo"
-    LLM_OPENAI_TEMPERATURE: float = 0.7
-    LLM_TIMEOUT: int = 60 * 5  # take cold start into account
-    LLM_MAX_TOKENS: int = 1024
-    LLM_TEMPERATURE: float = 0.7
-    ZEPHYR_LLM_URL: str | None = None
-    HERMES_3_8B_LLM_URL: str | None = None
-
-    # LLM Modal configuration
-    LLM_MODAL_API_KEY: str | None = None
-
-    # per-task cases
-    SUMMARY_MODEL: str = "monadical/private/smart"
-    SUMMARY_LLM_URL: str | None = None
-    SUMMARY_LLM_API_KEY: str | None = None
-    SUMMARY_LLM_CONTEXT_SIZE_TOKENS: int = 16000
+    LLM_API_KEY: str | None = None
+    LLM_CONTEXT_WINDOW: int = 16000
 
     # Diarization
     DIARIZATION_ENABLED: bool = True
@@ -86,12 +67,6 @@ class Settings(BaseSettings):
     # if set, all anonymous record will be public
     PUBLIC_MODE: bool = False
 
-    # Default LLM model name
-    DEFAULT_LLM: str = "lmsys/vicuna-13b-v1.5"
-
-    # Cache directory for all model storage
-    CACHE_DIR: str = "./data"
-
     # Min transcript length to generate topic + summary
     MIN_TRANSCRIPT_LENGTH: int = 750
 
@@ -116,24 +91,20 @@ class Settings(BaseSettings):
     # Healthcheck
     HEALTHCHECK_URL: str | None = None
 
-    AWS_PROCESS_RECORDING_QUEUE_URL: str | None = None
-    SQS_POLLING_TIMEOUT_SECONDS: int = 60
-
+    # Whereby integration
     WHEREBY_API_URL: str = "https://api.whereby.dev/v1"
-
     WHEREBY_API_KEY: str | None = None
-
+    WHEREBY_WEBHOOK_SECRET: str | None = None
     AWS_WHEREBY_S3_BUCKET: str | None = None
     AWS_WHEREBY_ACCESS_KEY_ID: str | None = None
     AWS_WHEREBY_ACCESS_KEY_SECRET: str | None = None
+    AWS_PROCESS_RECORDING_QUEUE_URL: str | None = None
+    SQS_POLLING_TIMEOUT_SECONDS: int = 60
 
+    # Zulip integration
     ZULIP_REALM: str | None = None
     ZULIP_API_KEY: str | None = None
     ZULIP_BOT_EMAIL: str | None = None
 
-    UI_BASE_URL: str = "http://localhost:3000"
-
-    WHEREBY_WEBHOOK_SECRET: str | None = None
-
 
 settings = Settings()
diff --git a/server/reflector/utils/text.py b/server/reflector/utils/text.py
new file mode 100644
index 00000000..ea5dd7e3
--- /dev/null
+++ b/server/reflector/utils/text.py
@@ -0,0 +1,33 @@
+def clean_title(title: str) -> str:
+    """
+    Clean and format a title string for consistent capitalization.
+
+    Rules:
+    - Strip surrounding quotes (single or double)
+    - Capitalize the first word
+    - Capitalize words longer than 3 characters
+    - Keep words with 3 or fewer characters lowercase (except first word)
+
+    Args:
+        title: The title string to clean
+
+    Returns:
+        The cleaned title with consistent capitalization
+
+    Examples:
+        >>> clean_title("hello world")
+        "Hello World"
+        >>> clean_title("meeting with the team")
+        "Meeting With the Team"
+        >>> clean_title("'Title with quotes'")
+        "Title With Quotes"
+    """
+    title = title.strip("\"'")
+    words = title.split()
+    if words:
+        words = [
+            word.capitalize() if i == 0 or len(word) > 3 else word.lower()
+            for i, word in enumerate(words)
+        ]
+        title = " ".join(words)
+    return title
diff --git a/server/tests/conftest.py b/server/tests/conftest.py
index 2707daed..434e7dea 100644
--- a/server/tests/conftest.py
+++ b/server/tests/conftest.py
@@ -37,8 +37,12 @@ def dummy_processors():
             "reflector.processors.transcript_translator.TranscriptTranslatorProcessor.get_translation"
         ) as mock_translate,
     ):
-        mock_topic.return_value = {"title": "LLM TITLE", "summary": "LLM SUMMARY"}
-        mock_title.return_value = {"title": "LLM TITLE"}
+        from reflector.processors.transcript_topic_detector import TopicResponse
+
+        mock_topic.return_value = TopicResponse(
+            title="LLM TITLE", summary="LLM SUMMARY"
+        )
+        mock_title.return_value = "LLM Title"
         mock_long_summary.return_value = "LLM LONG SUMMARY"
         mock_short_summary.return_value = "LLM SHORT SUMMARY"
         mock_translate.return_value = "Bonjour le monde"
@@ -103,14 +107,15 @@ async def dummy_diarization():
 
 @pytest.fixture
 async def dummy_llm():
-    from reflector.llm.base import LLM
+    from reflector.llm import LLM
 
     class TestLLM(LLM):
         def __init__(self):
             self.model_name = "DUMMY MODEL"
             self.llm_tokenizer = "DUMMY TOKENIZER"
 
-    with patch("reflector.llm.base.LLM.get_instance") as mock_llm:
+    # LLM doesn't have get_instance anymore, mocking constructor instead
+    with patch("reflector.llm.LLM") as mock_llm:
         mock_llm.return_value = TestLLM()
         yield
 
@@ -129,22 +134,19 @@ async def dummy_storage():
         async def _get_file_url(self, *args, **kwargs):
             return "http://fake_server/audio.mp3"
 
-    with patch("reflector.storage.base.Storage.get_instance") as mock_storage:
-        mock_storage.return_value = DummyStorage()
-        yield
+        async def _get_file(self, *args, **kwargs):
+            from pathlib import Path
 
+            test_mp3 = Path(__file__).parent / "records" / "test_mathieu_hello.mp3"
+            return test_mp3.read_bytes()
 
-@pytest.fixture
-def nltk():
-    with patch("reflector.llm.base.LLM.ensure_nltk") as mock_nltk:
-        mock_nltk.return_value = "NLTK PACKAGE"
-        yield
-
-
-@pytest.fixture
-def ensure_casing():
-    with patch("reflector.llm.base.LLM.ensure_casing") as mock_casing:
-        mock_casing.return_value = "LLM TITLE"
+    dummy = DummyStorage()
+    with (
+        patch("reflector.storage.base.Storage.get_instance") as mock_storage,
+        patch("reflector.storage.get_transcripts_storage") as mock_get_transcripts,
+    ):
+        mock_storage.return_value = dummy
+        mock_get_transcripts.return_value = dummy
         yield
 
 
diff --git a/server/tests/test_processors_broadcast.py b/server/tests/test_processors_broadcast.py
index 5480de36..197ebac4 100644
--- a/server/tests/test_processors_broadcast.py
+++ b/server/tests/test_processors_broadcast.py
@@ -2,7 +2,7 @@ import pytest
 
 
 @pytest.mark.asyncio
-async def test_processor_broadcast(nltk):
+async def test_processor_broadcast():
     from reflector.processors.base import BroadcastProcessor, Pipeline, Processor
 
     class TestProcessor(Processor):
diff --git a/server/tests/test_processors_pipeline.py b/server/tests/test_processors_pipeline.py
index 0124d5ba..a1787c49 100644
--- a/server/tests/test_processors_pipeline.py
+++ b/server/tests/test_processors_pipeline.py
@@ -3,11 +3,9 @@ import pytest
 
 @pytest.mark.asyncio
 async def test_basic_process(
-    nltk,
     dummy_transcript,
     dummy_llm,
     dummy_processors,
-    ensure_casing,
 ):
     # goal is to start the server, and send rtc audio to it
     # validate the events received
@@ -16,8 +14,8 @@ async def test_basic_process(
     from reflector.settings import settings
     from reflector.tools.process import process_audio_file
 
-    # use an LLM test backend
-    settings.LLM_BACKEND = "test"
+    # LLM_BACKEND no longer exists in settings
+    # settings.LLM_BACKEND = "test"
     settings.TRANSCRIPT_BACKEND = "whisper"
 
     # event callback
diff --git a/server/tests/test_transcripts_process.py b/server/tests/test_transcripts_process.py
index ef105c85..e4973acb 100644
--- a/server/tests/test_transcripts_process.py
+++ b/server/tests/test_transcripts_process.py
@@ -10,7 +10,6 @@ from httpx import AsyncClient
 @pytest.mark.asyncio
 async def test_transcript_process(
     tmpdir,
-    ensure_casing,
     dummy_llm,
     dummy_processors,
     dummy_diarization,
@@ -69,7 +68,7 @@ async def test_transcript_process(
     transcript = resp.json()
     assert transcript["status"] == "ended"
     assert transcript["short_summary"] == "LLM SHORT SUMMARY"
-    assert transcript["title"] == "LLM TITLE"
+    assert transcript["title"] == "Llm Title"
 
     # check topics and transcript
     response = await ac.get(f"/transcripts/{tid}/topics")
diff --git a/server/tests/test_transcripts_rtc_ws.py b/server/tests/test_transcripts_rtc_ws.py
index 24b70d6f..a8406337 100644
--- a/server/tests/test_transcripts_rtc_ws.py
+++ b/server/tests/test_transcripts_rtc_ws.py
@@ -69,8 +69,6 @@ async def test_transcript_rtc_and_websocket(
     dummy_diarization,
     dummy_storage,
     fake_mp3_upload,
-    ensure_casing,
-    nltk,
     appserver,
 ):
     # goal: start the server, exchange RTC, receive websocket events
@@ -185,7 +183,7 @@ async def test_transcript_rtc_and_websocket(
 
     assert "FINAL_TITLE" in eventnames
     ev = events[eventnames.index("FINAL_TITLE")]
-    assert ev["data"]["title"] == "LLM TITLE"
+    assert ev["data"]["title"] == "Llm Title"
 
     assert "WAVEFORM" in eventnames
     ev = events[eventnames.index("WAVEFORM")]
@@ -228,8 +226,6 @@ async def test_transcript_rtc_and_websocket_and_fr(
     dummy_diarization,
     dummy_storage,
     fake_mp3_upload,
-    ensure_casing,
-    nltk,
     appserver,
 ):
     # goal: start the server, exchange RTC, receive websocket events
@@ -353,7 +349,7 @@ async def test_transcript_rtc_and_websocket_and_fr(
 
     assert "FINAL_TITLE" in eventnames
     ev = events[eventnames.index("FINAL_TITLE")]
-    assert ev["data"]["title"] == "LLM TITLE"
+    assert ev["data"]["title"] == "Llm Title"
 
     # check status order
     statuses = [e["data"]["value"] for e in events if e["event"] == "STATUS"]
diff --git a/server/tests/test_transcripts_upload.py b/server/tests/test_transcripts_upload.py
index fab21321..1bc82386 100644
--- a/server/tests/test_transcripts_upload.py
+++ b/server/tests/test_transcripts_upload.py
@@ -10,7 +10,6 @@ from httpx import AsyncClient
 @pytest.mark.asyncio
 async def test_transcript_upload_file(
     tmpdir,
-    ensure_casing,
     dummy_llm,
     dummy_processors,
     dummy_diarization,
@@ -53,7 +52,7 @@ async def test_transcript_upload_file(
     transcript = resp.json()
     assert transcript["status"] == "ended"
     assert transcript["short_summary"] == "LLM SHORT SUMMARY"
-    assert transcript["title"] == "LLM TITLE"
+    assert transcript["title"] == "Llm Title"
 
     # check topics and transcript
     response = await ac.get(f"/transcripts/{tid}/topics")
diff --git a/server/tests/test_utils_text.py b/server/tests/test_utils_text.py
new file mode 100644
index 00000000..8b86fe0a
--- /dev/null
+++ b/server/tests/test_utils_text.py
@@ -0,0 +1,21 @@
+import pytest
+
+from reflector.utils.text import clean_title
+
+
+@pytest.mark.parametrize(
+    "input_title,expected",
+    [
+        ("hello world", "Hello World"),
+        ("HELLO WORLD", "Hello World"),
+        ("hello WORLD", "Hello World"),
+        ("the quick brown fox", "The Quick Brown fox"),
+        ("discussion about API design", "Discussion About api Design"),
+        ("Q1 2024 budget review", "Q1 2024 Budget Review"),
+        ("'Title with quotes'", "Title With Quotes"),
+        ("'title with quotes'", "Title With Quotes"),
+        ("MiXeD CaSe WoRdS", "Mixed Case Words"),
+    ],
+)
+def test_clean_title(input_title, expected):
+    assert clean_title(input_title) == expected