diff --git a/wavefront/server/apps/floconsole/pyproject.toml b/wavefront/server/apps/floconsole/pyproject.toml
index f562f764..8c46280a 100644
--- a/wavefront/server/apps/floconsole/pyproject.toml
+++ b/wavefront/server/apps/floconsole/pyproject.toml
@@ -19,7 +19,7 @@ dependencies = [
     "sqlalchemy>=2.0.40,<3.0.0",
     "python-dotenv>=1.1.0,<2.0.0",
     "dependency-injector>=4.46.0,<5.0.0",
-    "psycopg2>=2.9.10,<3.0.0",
+    "psycopg2-binary>=2.9.10,<3.0.0",
     "python-jose[cryptography]>=3.3.0,<4.0.0",
     "async-lru>=2.0.5",
 ]
diff --git a/wavefront/server/apps/inference_app/inference_app/config.ini b/wavefront/server/apps/inference_app/inference_app/config.ini
index cc12d9df..8b137891 100644
--- a/wavefront/server/apps/inference_app/inference_app/config.ini
+++ b/wavefront/server/apps/inference_app/inference_app/config.ini
@@ -1,8 +1 @@
-[aws]
-model_storage_bucket=${MODEL_STORAGE_BUCKET}
 
-[gcp]
-model_storage_bucket=${MODEL_STORAGE_BUCKET}
-
-[cloud_config]
-cloud_provider=${CLOUD_PROVIDER}
diff --git a/wavefront/server/apps/inference_app/inference_app/controllers/inference_controller.py b/wavefront/server/apps/inference_app/inference_app/controllers/inference_controller.py
index e9a5e82a..5a90c435 100644
--- a/wavefront/server/apps/inference_app/inference_app/controllers/inference_controller.py
+++ b/wavefront/server/apps/inference_app/inference_app/controllers/inference_controller.py
@@ -1,156 +1,58 @@
 import base64
-from typing import Any, Dict
+import binascii
 
 from common_module.common_container import CommonContainer
-from common_module.log.logger import logger
 from common_module.response_formatter import ResponseFormatter
 from dependency_injector.wiring import Provide, inject
 from fastapi import APIRouter, Depends, status
 from fastapi.responses import JSONResponse
 from inference_app.inference_app_container import InferenceAppContainer
-from inference_app.service.image_analyser import ImageClarityService
-from inference_app.service.model_inference import (
-    ModelInferenceService,
-    PreprocessingStep,
-)
-from inference_app.service.model_repository import ModelRepository
 from inference_app.service.image_embedding import ImageEmbedding
-from pydantic import BaseModel, Field
+from pydantic import BaseModel
 
 
-class InferencePayload(BaseModel):
-    data: str
-    payload_type: str
-    model_info: dict
-    preprocessing_steps: list[PreprocessingStep]
-    max_expected_variance: int = Field(default=1000)
-    resize_width: int = Field(default=224)
-    resize_height: int = Field(default=224)
-    gaussian_blur_kernel: int = Field(default=3)
-    min_threshold: int = Field(default=50)
-    max_threshold: int = Field(default=150)
-    normalize_mean: str = Field(default='0.485,0.456,0.406')
-    normalize_std: str = Field(default='0.229,0.224,0.225')
-
-
-class InferenceResult(BaseModel):
-    results: Dict[str, Any] = Field(..., description='Dictionary of inference results')
+class ImagePayload(BaseModel):
+    image_data: str  # base64 encoded image data
 
 
-class ImagePayload(BaseModel):
-    image_data: str
+class ImageBatchPayload(BaseModel):
+    image_batch: list[str]  # list of base64 encoded image data
 
 
 inference_app_router = APIRouter()
 
 
-@inference_app_router.post('/v1/model-repository/model/{model_id}/infer')
+@inference_app_router.post('/v1/query/embeddings')
 @inject
-async def generic_inference_handler(
-    payload: InferencePayload,
+async def image_embedding(
+    payload: ImagePayload,
     response_formatter: ResponseFormatter = Depends(
         Provide[CommonContainer.response_formatter]
     ),
-    model_repository: ModelRepository = Depends(
-        Provide[InferenceAppContainer.model_repository]
-    ),
-    image_analyser: ImageClarityService = Depends(
-        Provide[InferenceAppContainer.image_analyser]
-    ),
-    config: dict = Depends(Provide[InferenceAppContainer.config]),
-    model_inference: ModelInferenceService = Depends(
-        Provide[InferenceAppContainer.model_inference]
+    image_embedding_service: ImageEmbedding = Depends(
+        Provide[InferenceAppContainer.image_embedding]
     ),
 ):
-    try:
-        provider = config['cloud_config']['cloud_provider']
-        model_storage_bucket = (
-            config['gcp']['model_storage_bucket']
-            if provider.lower() == 'gcp'
-            else config['aws']['model_storage_bucket']
-        )
-
-        logger.info(
-            f'Loading model from bucket: {model_storage_bucket}, model_info: {payload.model_info}'
-        )
-        model = await model_repository.load_model(
-            model_info=payload.model_info, bucket_name=model_storage_bucket
-        )
-        logger.debug('Model loaded successfully for model_id')
-
-        if payload.payload_type.lower() == 'image':
-            base64_data_uri = payload.data
-            parts = base64_data_uri.split(',')
-            if len(parts) == 2:
-                base64_data = parts[1]
-                image_bytes = base64.b64decode(base64_data)
-
-                clarity_score = image_analyser.laplacian_detection(
-                    image_bytes, payload.max_expected_variance
-                )
-
-                infer_data = model_inference.model_infer_score(
-                    model,
-                    image_bytes,
-                    payload.resize_width,
-                    payload.resize_height,
-                    payload.normalize_mean,
-                    payload.normalize_std,
-                    payload.gaussian_blur_kernel,
-                    payload.min_threshold,
-                    payload.max_threshold,
-                    preprocessing_steps=payload.preprocessing_steps,
-                )
-                logger.debug('Model inference completed successfully for model_id')
-
-                inference_results = InferenceResult(
-                    results={
-                        'clarity_score': clarity_score,
-                        'infer_data': infer_data,
-                        'data_type': payload.payload_type.lower(),
-                    }
-                )
-
-                logger.info('Inference request completed successfully for model_id')
-                return JSONResponse(
-                    status_code=status.HTTP_201_CREATED,
-                    content=response_formatter.buildSuccessResponse(
-                        inference_results.dict()
-                    ),
-                )
-            else:
-                error_msg = (
-                    "Input data is not in expected Data URI format (missing 'base64,')."
-                )
-                logger.error(
-                    f"Expected Data URI format with 'base64,' prefix. "
-                    f'Data length: {len(base64_data_uri)}'
-                )
-                return JSONResponse(
-                    status_code=status.HTTP_400_BAD_REQUEST,
-                    content=response_formatter.buildErrorResponse(error_msg),
-                )
-        else:
-            error_msg = f"Invalid payload_type: {payload.payload_type}. Accepted values are 'image'"
-            logger.error(f'{error_msg}')
-            return JSONResponse(
-                status_code=status.HTTP_400_BAD_REQUEST,
-                content=response_formatter.buildErrorResponse(
-                    'Invalid payload_type. Accepted values are "image"'
-                ),
-            )
-    except Exception as e:
-        logger.error(f'Error in generic_inference_handler {str(e)}')
+    # 1. Decode Base64 string
+    image_data = extract_decoded_image_data(payload.image_data)
+    embeddings = image_embedding_service.query_embed(image_data)
+    if not embeddings:
         return JSONResponse(
-            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            content=response_formatter.buildErrorResponse('Internal server error'),
+            status_code=status.HTTP_400_BAD_REQUEST,
+            content=response_formatter.buildErrorResponse(
+                'No Embedding data is present'
+            ),
         )
+    return JSONResponse(
+        status_code=status.HTTP_200_OK,
+        content=response_formatter.buildSuccessResponse(data={'response': embeddings}),
+    )
 
 
-@inference_app_router.post('/v1/query/embeddings')
+@inference_app_router.post('/v1/query/embeddings/batch')
 @inject
-async def image_embedding(
-    payload: ImagePayload,
+async def image_embedding_batch(
+    payload: ImageBatchPayload,
     response_formatter: ResponseFormatter = Depends(
         Provide[CommonContainer.response_formatter]
     ),
@@ -158,12 +60,18 @@ async def image_embedding(
         Provide[InferenceAppContainer.image_embedding]
     ),
 ):
-    # 1. Decode Base64 string
-    base64_data_uri = payload.image_data
-    parts = base64_data_uri.split(',')
-    base64_data = parts[1] if len(parts) == 2 else parts[0]
-    image_data = base64.b64decode(base64_data)
-    embeddings = image_embedding_service.query_embed(image_data)
+    try:
+        image_batch = [
+            extract_decoded_image_data(image_data) for image_data in payload.image_batch
+        ]
+    except binascii.Error:
+        return JSONResponse(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            content=response_formatter.buildErrorResponse(
+                'Invalid base64 image data in batch'
+            ),
+        )
+    embeddings = image_embedding_service.query_embed_batch(image_batch)
     if not embeddings:
         return JSONResponse(
             status_code=status.HTTP_400_BAD_REQUEST,
@@ -175,3 +83,9 @@ async def image_embedding(
         status_code=status.HTTP_200_OK,
         content=response_formatter.buildSuccessResponse(data={'response': embeddings}),
     )
+
+
+def extract_decoded_image_data(image_data: str) -> bytes:
+    parts = image_data.split(',')
+    base64_data = parts[1] if len(parts) == 2 else parts[0]
+    return base64.b64decode(base64_data)
diff --git a/wavefront/server/apps/inference_app/inference_app/inference_app_container.py b/wavefront/server/apps/inference_app/inference_app/inference_app_container.py
index 8666dcbe..b0fd7ea8 100644
--- a/wavefront/server/apps/inference_app/inference_app/inference_app_container.py
+++ b/wavefront/server/apps/inference_app/inference_app/inference_app_container.py
@@ -1,31 +1,9 @@
 from dependency_injector import containers
 from dependency_injector import providers
-from inference_app.service.image_analyser import ImageClarityService
-from flo_cloud.cloud_storage import CloudStorageManager
-from inference_app.service.model_repository import ModelRepository
-from inference_app.service.model_inference import ModelInferenceService
 from inference_app.service.image_embedding import ImageEmbedding
 
 
 class InferenceAppContainer(containers.DeclarativeContainer):
     config = providers.Configuration(ini_files=['config.ini'])
-    cache_manager = providers.Dependency()
 
-    cloud_storage_manager = providers.Singleton(
-        CloudStorageManager, provider=config.cloud_config.cloud_provider
-    )
-
-    model_repository = providers.Singleton(
-        ModelRepository,
-        cloud_storage_manager=cloud_storage_manager,
-    )
-
-    model_inference = providers.Singleton(ModelInferenceService)
-
-    image_analyser = providers.Singleton(
-        ImageClarityService,
-    )
-
-    image_embedding = providers.Singleton(
-        ImageEmbedding,
-    )
+    image_embedding = providers.Singleton(ImageEmbedding)
diff --git a/wavefront/server/apps/inference_app/inference_app/server.py b/wavefront/server/apps/inference_app/inference_app/server.py
index 10274e65..b0d73530 100644
--- a/wavefront/server/apps/inference_app/inference_app/server.py
+++ b/wavefront/server/apps/inference_app/inference_app/server.py
@@ -1,5 +1,6 @@
 import glob
 import os
+from contextlib import asynccontextmanager
 
 from dotenv import load_dotenv
 from fastapi import FastAPI
@@ -22,14 +23,22 @@
 
 # Initialize dependency containers
 common_container = CommonContainer(cache_manager=None)
-inference_app_container = InferenceAppContainer(
-    cache_manager=None,
-)
+inference_app_container = InferenceAppContainer()
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    logger.info('Preloading ML models...')
+    inference_app_container.image_embedding()
+    logger.info('ML models loaded and ready.')
+    yield
+
 
 app = FastAPI(
     title='FloConsole API',
     description='Console application for RootFlo platform',
     version='1.0.0',
+    lifespan=lifespan,
 )
 
 
diff --git a/wavefront/server/apps/inference_app/inference_app/service/image_analyser.py b/wavefront/server/apps/inference_app/inference_app/service/image_analyser.py
deleted file mode 100644
index e7a89f0a..00000000
--- a/wavefront/server/apps/inference_app/inference_app/service/image_analyser.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import cv2
-from common_module.log.logger import logger
-from inference_app.utils.image_utils import decode_image_from_bytes
-
-
-class ImageClarityService:
-    def __init__(self):
-        pass
-
-    def laplacian_detection(self, image_bytes, max_expected_variance):
-        # Decode image from bytes array
-        logger.info(
-            f'Successfully decoded Base64 string. Data length: {len(image_bytes)} bytes.'
-        )
-        images = decode_image_from_bytes(image_bytes)
-        images = cv2.resize(images, (256, 256), interpolation=cv2.INTER_AREA)
-        gray = cv2.cvtColor(images, cv2.COLOR_BGR2GRAY)
-        laplacian = cv2.Laplacian(gray, cv2.CV_64F)
-        variance = laplacian.var()
-        clamped_variance = min(variance, int(max_expected_variance))
-        score = (clamped_variance / int(max_expected_variance)) * 100
-        return int(score)
diff --git a/wavefront/server/apps/inference_app/inference_app/service/image_embedding.py b/wavefront/server/apps/inference_app/inference_app/service/image_embedding.py
index 106eba37..659d769d 100644
--- a/wavefront/server/apps/inference_app/inference_app/service/image_embedding.py
+++ b/wavefront/server/apps/inference_app/inference_app/service/image_embedding.py
@@ -5,24 +5,24 @@
 from typing import List, Dict, Any
 from common_module.log.logger import logger
 
+CLIP_MODEL_NAME = 'openai/clip-vit-base-patch32'
+DINO_MODEL_NAME = 'facebook/dinov3-vitl16-pretrain-lvd1689m'
 
-class ImageEmbedding:
-    CLIP_MODEL_NAME = 'openai/clip-vit-base-patch32'
-    DINO_MODEL_NAME = 'facebook/dinov3-vitl16-pretrain-lvd1689m'
 
+class ImageEmbedding:
     def __init__(self):
         self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
         logger.info(f'Using device: {self.device}')
 
-        self.clip_processor = CLIPProcessor.from_pretrained(self.CLIP_MODEL_NAME)
-        self.clip_model = CLIPModel.from_pretrained(self.CLIP_MODEL_NAME).to(
+        self.clip_processor = CLIPProcessor.from_pretrained(CLIP_MODEL_NAME)
+        self.clip_model = CLIPModel.from_pretrained(CLIP_MODEL_NAME).to(
             self.device
         )
         self.clip_model.eval()
 
-        self.dino_processor = AutoImageProcessor.from_pretrained(self.DINO_MODEL_NAME)
+        self.dino_processor = AutoImageProcessor.from_pretrained(DINO_MODEL_NAME)
         self.dino_model = AutoModel.from_pretrained(
-            self.DINO_MODEL_NAME, trust_remote_code=True
+            DINO_MODEL_NAME, trust_remote_code=True
         ).to(self.device)
         self.dino_model.eval()
 
@@ -69,3 +69,51 @@ def query_embed(self, image_content: bytes) -> List[Dict[str, List[float]]]:
             results.append({name: embedding})
 
         return results
+
+    @torch.inference_mode()
+    def query_embed_batch(
+        self, image_batch: list[bytes]
+    ) -> List[Dict[str, List[List[float]]]]:
+        """
+        GPU batch embedding.
+
+        Returns:
+          [
+            {"clip": [embedding_for_image_0, ..., embedding_for_image_N]},
+            {"dino": [embedding_for_image_0, ..., embedding_for_image_N]},
+          ]
+        """
+        if not image_batch:
+            return []
+
+        # Decode bytes -> PIL images on CPU.
+        # The actual model forward pass (processor->tensor + model) is batched on GPU.
+        images: List[Image.Image] = []
+        for idx, image_content in enumerate(image_batch):
+            try:
+                images.append(Image.open(io.BytesIO(image_content)).convert('RGB'))
+            except Exception as e:
+                logger.error(
+                    f'Error opening image at index={idx}: {e}',
+                    exc_info=True,
+                )
+                raise ValueError(
+                    f'Failed to decode image at index {idx}: {e}'
+                ) from e
+
+        results: List[Dict[str, List[List[float]]]] = []
+
+        for name, embedder in self.embedders.items():
+            inputs = embedder['processor'](images=images, return_tensors='pt')
+            inputs = {k: v.to(self.device) for k, v in inputs.items()}
+
+            # Batched forward pass.
+            image_features = embedder['extractor'](inputs)  # (batch, dim)
+
+            # L2-normalize per-vector.
+            image_features = image_features / image_features.norm(dim=-1, keepdim=True)
+
+            embeddings = image_features.cpu().numpy().tolist()  # batch x dim
+            results.append({name: embeddings})
+
+        return results
diff --git a/wavefront/server/apps/inference_app/inference_app/service/model_inference.py b/wavefront/server/apps/inference_app/inference_app/service/model_inference.py
deleted file mode 100644
index 929ccf3e..00000000
--- a/wavefront/server/apps/inference_app/inference_app/service/model_inference.py
+++ /dev/null
@@ -1,99 +0,0 @@
-import cv2
-import torchvision.transforms as transforms
-from PIL import Image
-import torch
-import numpy as np
-from pydantic import BaseModel, Field
-from inference_app.utils.image_utils import decode_image_from_bytes
-
-
-class PreprocessingStep(BaseModel):
-    preprocess_filter: str
-    values: list = Field(default_factory=list)
-
-
-class ModelInferenceService:
-    def __init__(self):
-        self.device = torch.device('cpu')
-
-    def preprocess_image(
-        self,
-        image_bytes,
-        gaussian_blur_kernel,
-        min_threshold,
-        max_threshold,
-        preprocessing_steps: list[PreprocessingStep],
-    ):
-        """Apply preprocessing steps based on provided flags."""
-        processed_image = decode_image_from_bytes(image_bytes)
-
-        # Define available preprocessing functions
-        preprocessing_functions = {
-            'gray': lambda img, values: cv2.cvtColor(img, cv2.COLOR_BGR2GRAY),
-            'gaussian_blur': lambda img, values: cv2.GaussianBlur(
-                img, (gaussian_blur_kernel, gaussian_blur_kernel), 0
-            ),
-            'canny': lambda img, values: cv2.cvtColor(
-                cv2.Canny(img, min_threshold, max_threshold), cv2.COLOR_GRAY2RGB
-            ),
-            'kernel_sharpening': lambda img, values: cv2.filter2D(
-                img, -1, np.array([[-1, -1, -1], [-1, 9, -1], [-1, -1, -1]])
-            ),
-        }
-        for step in preprocessing_steps:
-            filter_name = step.preprocess_filter
-            values = step.values
-            if filter_name and filter_name in preprocessing_functions:
-                processed_image = preprocessing_functions[filter_name](
-                    processed_image, values
-                )
-            else:
-                continue
-
-        pil_image = Image.fromarray(processed_image)
-        return pil_image
-
-    def model_infer_score(
-        self,
-        model,
-        image_bytes,
-        resize_width,
-        resize_height,
-        normalize_mean,
-        normalize_std,
-        gaussian_blur_kernel,
-        min_threshold,
-        max_threshold,
-        preprocessing_steps: list[PreprocessingStep],
-    ):
-        """
-        Predict overlap score for a single image using the same preprocessing as training
-        """
-        # Define the same transform used during validation
-        normalize_mean = [float(x) for x in normalize_mean.split(',')]
-        normalize_std = [float(x) for x in normalize_std.split(',')]
-        transform = transforms.Compose(
-            [
-                transforms.Resize((resize_width, resize_height)),
-                transforms.ToTensor(),
-                transforms.Normalize(mean=normalize_mean, std=normalize_std),
-            ]
-        )
-        # Apply the same preprocessing as during training
-        preprocessed_image = self.preprocess_image(
-            image_bytes,
-            gaussian_blur_kernel,
-            min_threshold,
-            max_threshold,
-            preprocessing_steps,
-        )
-
-        # Apply transforms
-        image_tensor = transform(preprocessed_image).unsqueeze(0).to(self.device)
-        model.to(self.device)
-        # Predict
-        model.eval()
-        with torch.no_grad():
-            response = model(image_tensor).item()
-
-        return response
diff --git a/wavefront/server/apps/inference_app/inference_app/service/model_repository.py b/wavefront/server/apps/inference_app/inference_app/service/model_repository.py
deleted file mode 100644
index 831dc8a0..00000000
--- a/wavefront/server/apps/inference_app/inference_app/service/model_repository.py
+++ /dev/null
@@ -1,68 +0,0 @@
-from typing import Dict
-import os
-import torch
-import io
-from common_module.log.logger import logger
-from flo_cloud.cloud_storage import CloudStorageManager
-
-
-class ModelRepository:
-    def __init__(
-        self,
-        cloud_storage_manager: CloudStorageManager,
-    ):
-        self.cloud_storage_manager = cloud_storage_manager
-        self.model_storage_dir = os.getenv('MODEL_STORAGE_DIR', './models')
-        os.makedirs(self.model_storage_dir, exist_ok=True)
-        # Cache for loaded models - stores model instances in memory
-        self._model_cache: Dict[str, torch.nn.Module] = {}
-
-    def _is_model_cached_locally(
-        self, model_name: str, file_path: str, expected_local_model_dir: str
-    ) -> bool:
-        """
-        Checks if the model is available in the local persistent storage.
-        """
-        return os.path.exists(
-            expected_local_model_dir
-        ) and f'{model_name}.{file_path.split(".")[-1]}' in os.listdir(
-            expected_local_model_dir
-        )
-
-    async def load_model(self, model_info: dict, bucket_name: str):
-        model_id = model_info['model_id']
-        expected_local_model_dir = self.model_storage_dir
-        model_name = model_info['model_name']
-        file_path = model_info['model_path']
-        model_id = model_info['model_id']
-
-        local_model_filename = os.path.join(
-            expected_local_model_dir, f'{model_name}.{file_path.split(".")[1]}'
-        )
-        local_model_full_path = os.path.join(local_model_filename)
-
-        if self._is_model_cached_locally(
-            model_name, file_path, expected_local_model_dir
-        ):
-            logger.info(f'Model {model_id} found in local persistent storage, loading.')
-            if model_id in self._model_cache:
-                return self._model_cache[model_id]
-            else:
-                with open(local_model_full_path, 'rb') as f:
-                    model_bytes_data = f.read()
-                return torch.load(io.BytesIO(model_bytes_data), weights_only=False)
-        else:
-            logger.info(
-                f'Model {model_id} not found in local persistent storage, loading from cloud storage.'
-            )
-            model_bytes_data = self.cloud_storage_manager.read_file(
-                bucket_name, file_path
-            )
-            model = torch.load(io.BytesIO(model_bytes_data), weights_only=False)
-            # Save to local persistent storage after fetching from cloud
-            os.makedirs(os.path.dirname(local_model_full_path), exist_ok=True)
-            with open(local_model_full_path, 'wb') as f:
-                f.write(model_bytes_data)
-            self._model_cache[model_id] = model
-            logger.info(f'Model {model_id} loaded and cached in memory.')
-        return model
diff --git a/wavefront/server/apps/inference_app/pyproject.toml b/wavefront/server/apps/inference_app/pyproject.toml
index 977aefc8..a914e117 100644
--- a/wavefront/server/apps/inference_app/pyproject.toml
+++ b/wavefront/server/apps/inference_app/pyproject.toml
@@ -18,7 +18,7 @@ dependencies = [
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diff --git a/wavefront/server/modules/insights_module/pyproject.toml b/wavefront/server/modules/insights_module/pyproject.toml
index 487a36f6..4a2f8c90 100644
--- a/wavefront/server/modules/insights_module/pyproject.toml
+++ b/wavefront/server/modules/insights_module/pyproject.toml
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diff --git a/wavefront/server/uv.lock b/wavefront/server/uv.lock
index f7bf7aa7..07c7e11e 100644
--- a/wavefront/server/uv.lock
+++ b/wavefront/server/uv.lock
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