You will get an agentic AI system with multi-step reasoning and tool use
Project details
Most "AI agents" built on Upwork are glorified chatbots with an if-else router. A real agentic system reasons across multiple
steps, uses tools, remembers context, and recovers gracefully when something goes wrong.
I built JUVO — a production agentic platform for Pakistan's informal economy. It runs a multi-agent pipeline: an Intent Agent extracts what the user needs in any language, a Discovery Agent finds nearby providers using geospatial queries, and a
Booking Service confirms the reservation with ACID database triggers that eliminate 100% of double-booking conflicts. Users interact through natural conversation. The system handles the rest.
That's the standard I bring to every agentic project.
What you get:
→ Proper multi-agent architecture — not a single prompt chain
→ Tool use: database, APIs, search, maps, or whatever your system needs
→ Human-in-the-loop workflows where decisions need user input
→ Persistent memory so agents know what happened earlier
→ Production FastAPI backend with auth, rate limiting, background tasks, and deployment
→ Full documentation and walkthrough video on delivery
If you're building something that needs to think across multiple steps.
steps, uses tools, remembers context, and recovers gracefully when something goes wrong.
I built JUVO — a production agentic platform for Pakistan's informal economy. It runs a multi-agent pipeline: an Intent Agent extracts what the user needs in any language, a Discovery Agent finds nearby providers using geospatial queries, and a
Booking Service confirms the reservation with ACID database triggers that eliminate 100% of double-booking conflicts. Users interact through natural conversation. The system handles the rest.
That's the standard I bring to every agentic project.
What you get:
→ Proper multi-agent architecture — not a single prompt chain
→ Tool use: database, APIs, search, maps, or whatever your system needs
→ Human-in-the-loop workflows where decisions need user input
→ Persistent memory so agents know what happened earlier
→ Production FastAPI backend with auth, rate limiting, background tasks, and deployment
→ Full documentation and walkthrough video on delivery
If you're building something that needs to think across multiple steps.
AI Development Type
Deep Learning, Knowledge Representation, Software MaintenanceAI Tools
Keras, MLflow, OpenCV, PyTorchAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$200
|
Standard
$450
|
Advanced
$750
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 20 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Detailed Code Comments | - | ||
Knowledge Graph | |||
Model Documentation | - | ||
Ontology | - | - | - |
Source Code | |||
Taxonomy | - | - | - |
Frequently asked questions
About Asghar Qambar
AI Engineer | RAG, AI Agents & Automation | Python, LangChain, CrewAI
Karachi South, Pakistan - 5:38 am local time
Over the past two years, I've designed and deployed AI systems across legal tech, computer vision, speech AI, and multi-agent automation. My experience combines independent, hands-on AI engineering with corporate development workflows, allowing me to build systems that are structured, reliable, and production-ready.
I can help you with:
• AI agents for research, support, and operational workflows
• RAG systems over documents, databases, and knowledge bases
• Custom LLM applications with memory, tool use, and structured outputs
• Workflow automation through APIs, webhooks, MCP servers, and business systems (CRM, ERP)
• Production backends using Python, FastAPI, and vector databases
• Domain-specific AI systems for legal, medical, and internal operations
Some Projects
▸ Fine-tuned Llama 3 for a legal AI assistant and built a RAG pipeline on MongoDB Vector Atlas, achieving 0.98 retrieval accuracy and 200ms response times.
▸ Developed a multi-agent service platform using Gemini, FastAPI, and PostgreSQL/PostGIS, eliminating booking conflicts through an intelligent reservation system.
▸ Built a real-time computer vision pipeline achieving 98% accuracy and 0.3ms latency.
▸ Developed an Urdu voice cloning system that improved benchmark performance by 25% and achieved a 92% user preference score.
Technologies
Python • FastAPI • LangChain • LangGraph • CrewAI • OpenAI • Gemini • Anthropic • PostgreSQL • MongoDB Vector Atlas • Pinecone • ChromaDB • Docker • n8n • AWS • GCP • REST APIs • GraphQL
If you're planning an AI product or need to move an existing prototype into production, send me a message. I can quickly identify the right architecture, potential bottlenecks, and the fastest path to a reliable deployment.
Steps for completing your project
After purchasing the project, send requirements so Asghar Qambar can start the project.
Delivery time starts when Asghar Qambar receives requirements from you.
Asghar Qambar works on your project following the steps below.
Revisions may occur after the delivery date.
System Design & Agent Architecture
Define agent roles, tool list, memory strategy, and data flow. Deliver a written architecture plan for your approval before any code is written.
Tool & Integration Setup
Build and test each tool the agent will use — database queries, API connections, search, or geospatial lookups — as standalone verified components.