You will get advanced Google Lens replica for your AI agent
Top Rated
Top Rated
Project details
ššØšØš š„š ššš§š¬-ššš²š„š šššš«šš” - šš®š¢š„š šš² šš§š š¢š§ššš« ššš”š¢š§š šš & ššØšØš š„š-šššš¤šš šššš«šš®š©š¬ š
š šš®š¢š„š ššš§š¬-š š«ššš š¬ššš«šš” šš² š¢š¦šš š & ššš±š-ššØ-ššš±š, šš§š-ššØ-šš§š. Seamless integration with chatbot agents as RAG tool via sparse retrieval ā dense retrieval ā reranking.
š šš”šš š²šØš® š šš
ā¢ Search by image: DINOv3 dense matching ā reranking
ā¢ Conversational search engine with semantic searching & RAG
ā¢ Works with agents (OpenAI Assistants, MCP tools) and E2E apps
š§ šššš«š¢ššÆšš„ š©š¢š©šš„š¢š§š
ā¢ Sparse (BM25/keyword) ā Dense (embeddings) ā Cross-encoder rerank
ā¢ Vector database: Qdrant, FAISS, Milvus, ChromaDB, Pinecone
ā¢ Graph reasoning: Neo4j for entity linking, paths, grounding
āļø ššššš¤ & šš±š©šš«šš¢š¬š
ā¢ OpenAI APIs, Ollama, vLLM, lmdeploy, MCPs
ā¢ PyTorch, OpenCV, ONNX/TensorRT; Next.js/FastAPI/Docker
ā¢ Semantic caching, hybrid-search, embedding hygiene
šššāš¬ š¬š”š¢š© š š©š«šØšš®ššš¢šØš§ ššš§š¬ š«šš©š„š¢šš šš”šš ššØš§šÆšš«šš¬. ššš¬š¬šš š š¦š ššØ š¬ššš«š.
š šš®š¢š„š ššš§š¬-š š«ššš š¬ššš«šš” šš² š¢š¦šš š & ššš±š-ššØ-ššš±š, šš§š-ššØ-šš§š. Seamless integration with chatbot agents as RAG tool via sparse retrieval ā dense retrieval ā reranking.
š šš”šš š²šØš® š šš
ā¢ Search by image: DINOv3 dense matching ā reranking
ā¢ Conversational search engine with semantic searching & RAG
ā¢ Works with agents (OpenAI Assistants, MCP tools) and E2E apps
š§ šššš«š¢ššÆšš„ š©š¢š©šš„š¢š§š
ā¢ Sparse (BM25/keyword) ā Dense (embeddings) ā Cross-encoder rerank
ā¢ Vector database: Qdrant, FAISS, Milvus, ChromaDB, Pinecone
ā¢ Graph reasoning: Neo4j for entity linking, paths, grounding
āļø ššššš¤ & šš±š©šš«šš¢š¬š
ā¢ OpenAI APIs, Ollama, vLLM, lmdeploy, MCPs
ā¢ PyTorch, OpenCV, ONNX/TensorRT; Next.js/FastAPI/Docker
ā¢ Semantic caching, hybrid-search, embedding hygiene
šššāš¬ š¬š”š¢š© š š©š«šØšš®ššš¢šØš§ ššš§š¬ š«šš©š„š¢šš šš”šš ššØš§šÆšš«šš¬. ššš¬š¬šš š š¦š ššØ š¬ššš«š.
AI Algorithms
Convolutional Neural Network, Feedforward Neural Network, Large Language Model, Multilayer Perceptron, Transformer Model, Variational AutoencoderAI Applications
AI Chatbot, AI Content Creation, AI Text-to-Image, AI-Enhanced Classification, Conversational AI, Image Processing, Image Recognition, Natural Language Generation, Natural Language Understanding, Object Detection, Sentiment Analysis, Text RecognitionAI Development Language
PythonAI Tools
Azure OpenAI, GitHub Copilot, Gradio, Hugging Face, NVIDIA AI Platform, PyTorch, Replit, Streamlit, TensorFlow, Word2vecAI Models
BERT, ChatGPT, DALL-E, GPT-3, GPT-4, LLaMA, OpenAI Codex, WhisperWhat's included
| Service Tiers |
Starter
$2,499
|
Standard
$6,999
|
Advanced
$9,999
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 28 days |
Number of Revisions | 1 | 1 | 1 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | - | ||
Detailed Code Comments | - | ||
Image Upscaling | - | - | - |
MLOps | - | - | |
Model Deployment | |||
Model Documentation | - | - | |
Model Monitoring | - | - | |
Model Testing & Optimization | - | ||
Model Tuning | - | - | |
Natural Language Processing | |||
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | |||
Setup File | - | ||
Source Code |
13 reviews
(12)
(0)
(1)
(0)
(0)
This project doesn't have any reviews.
SH
Shafee H.
May 26, 2025
Senior Frontend Dev
RH
Romeo H.
Jul 21, 2024
3d scan of face to 3d avatar to AR
Helped me with in incredibly difficult task and without him not sure where I would be with my project!
LL
LoĆÆc L.
May 29, 2024
Outfit detection prototype
SH
Shafee H.
Feb 5, 2024
Machine Learning
Excellent developer, very bright.
SH
Shafee H.
Jan 6, 2024
StyleGAN A.I. Expert
Great work
About Muhammad Abdullah
Senior Computer Vision & ML Consultant | Triton | PyTorch | Postgres
100%
Job Success
Rawalpindi, PakistanĀ - 5:31 pm local time
ššØššš”š ššÆšš«šš š ššØš¦š©š®ššš« šš¢š¬š¢šØš§ šš®š² š
I donāt stop at a cool demo. Shipping LootMart (hyper-local marketplace) taught me the full stack around models: clean APIs, rock-solid data contracts, observability, security, and predictable costs. Thatās why my CV/ML services behave like products, not science projects.
šš”šš š šššš®šš„š„š² ššØ
- Computer Vision & Video Analytics (2D/3D): detection (YOLO/DETR), multi-object tracking (ByteTrack/DeepSORT), segmentation (U-Net), OCR/document AI, pose/re-ID, visual search & face/product matching (Siamese + Triplet Loss), point clouds & geometry.
- High-Throughput Inference: NVIDIA Triton (dynamic batching, concurrent models, HTTP/gRPC), TensorRT (FP16), ONNX Runtime; autoscaling containers with health checks and graceful rollouts.
- Robust Ingestion: multi-RTSP pipelines with back-pressure control using OpenCV, FFmpeg, PyAV/decord so frames donāt mysteriously vanish under load.
- MLOps & Services: FastAPI/Flask gateways, worker queues, CI/CD, Docker + Nginx; W&B for experiments; versioned datasets; reproducible training.
- Data & Integrations: Postgres (schema design, RLS, SQL/PLpgSQL), Redis, vector DBs (Milvus/Qdrant), webhook-driven architectures, n8n workflows for ETL/alerts, and MCP (Model Context Protocol) to wire AI tools into your internal systems.
- Selective Full-Stack Glue (when it helps): Next.js app layers, secure webhooks, auth, real-time updates, and crisp dashboards so stakeholders can see impact.
šš«šØšØš š¢š§ šš”š šš®ššš¢š§š (ššššš§š šš¢š§š¬)
1. Triton-backed real-time CCTV analytics across multiple cameras on commodity GPUs (dynamic batching = buttery latency).
2. Visual matching pipelines (Siamese/Triplet) for search/dedupe with rigorous evals and W&B tracking.
3. Heavy research models ā ONNX/TensorRT ā low-latency services that actually survive production traffic.
4. Production plumbing that lasts: Postgres-first data contracts, webhook fan-out, n8n automations... no brittle glue.
ššØš° ššāš„š„ ššØš«š¤ (ššš š š¢š«š¬š, šš„š°šš²š¬)
1. 30-min discovery ā lock in the KPI (latency, accuracy, throughput, cost).
2. Roadmap & estimate ā phases, risks, acceptance tests.
3. Build & validate ā baselines first, then iterate; measurable deltas each milestone.
4. Handoff & scale ā docs, runbooks, and knowledge transfer so your team owns it.
ššØš«š ššššš¤
Python ā¢ PyTorch ā¢ TensorRT ā¢ ONNX Runtime ā¢ NVIDIA Triton ā¢ OpenCV ā¢ Kornia ā¢ FFmpeg ā¢ PyAV/decord ā¢ Postgres ā¢ Redis ā¢ Milvus/Qdrant ā¢ FastAPI/Flask ā¢ Next.js ā¢ Docker ā¢ Nginx ā¢ Weights & Biases ā¢ Webhooks ā¢ n8n ā¢ MCP
ššÆšš¢š„ššš¢š„š¢šš²
Consulting/part-time (fractional) engagements: architecture reviews, performance tuning, prototypes, or owning a CV/ML workstream. Top-rated on Upwork. Minimum $100/hr.
If you want production-ready computer vision, real-time video, reliable pipelines, and clear ROI, š„ššāš¬ ššš„š¤. Iāll map your goal to a pragmatic plan and ship results you can measure.
Steps for completing your project
After purchasing the project, send requirements so Muhammad Abdullah can start the project.
Delivery time starts when Muhammad Abdullah receives requirements from you.
Muhammad Abdullah works on your project following the steps below.
Revisions may occur after the delivery date.
Finalize the scope of work, timeline and budget via chat.
This helps us share a mutual understanding of what needs to be done as we move forward.
Write a proposal to be approved by you based on our discussion.
The proposal, once approved, marks our commitment and agreement.