You will get an AI agent that turns your documents into structured data and answers
Project details
I'll build you a production AI agent that does two things at once:
1. EXTRACTS structured data from your documents — vendor names, contract dates, invoice totals, key clauses, tagged entities, whatever schema you need. Output is clean JSON ready for your CRM, spreadsheet, or downstream pipeline.
2. ANSWERS questions on the same corpus with citations back to the exact source page. So your team queries 1,000 docs the way they'd query Notion: in natural language, with proof.
What you save:
✓ 15–30 hours per week of manual document review and data entry
✓ The cost of building this in-house with engineers who'd rather ship features
✓ The risk of hallucination — every answer is cited, every extraction is validated against ground truth
What you get on handoff:
✓ A working agent against your specific documents
✓ Structured extraction schema tailored to your data
✓ GitHub repo, deployment guide, monitoring setup, 30 days of post-launch support
✓ Honest LLM cost sizing upfront — no surprise OpenAI bills
Reply with your document type (PDFs, web pages, Notion, Confluence, SharePoint), rough volume, and the 3-5 fields you need extracted. I'll quote the right tier within 4 hours.
1. EXTRACTS structured data from your documents — vendor names, contract dates, invoice totals, key clauses, tagged entities, whatever schema you need. Output is clean JSON ready for your CRM, spreadsheet, or downstream pipeline.
2. ANSWERS questions on the same corpus with citations back to the exact source page. So your team queries 1,000 docs the way they'd query Notion: in natural language, with proof.
What you save:
✓ 15–30 hours per week of manual document review and data entry
✓ The cost of building this in-house with engineers who'd rather ship features
✓ The risk of hallucination — every answer is cited, every extraction is validated against ground truth
What you get on handoff:
✓ A working agent against your specific documents
✓ Structured extraction schema tailored to your data
✓ GitHub repo, deployment guide, monitoring setup, 30 days of post-launch support
✓ Honest LLM cost sizing upfront — no surprise OpenAI bills
Reply with your document type (PDFs, web pages, Notion, Confluence, SharePoint), rough volume, and the 3-5 fields you need extracted. I'll quote the right tier within 4 hours.
AI Algorithms
Large Language Model, Multimodal Large Language ModelAI Applications
AI Chatbot, Conversational AIAI Development Language
PythonAI Models
BERT, ChatGPT, GPT-4What's included
| Service Tiers |
Starter
$150
|
Standard
$300
|
Advanced
$700
|
|---|---|---|---|
| Delivery Time | 5 days | 7 days | 15 days |
Number of Revisions | 3 | 3 | 5 |
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 |
Optional add-ons
You can add these on the next page.
Self-hosted LLM deployment
(+ 5 Days)
+$100About Aaqib
AI Agent Developer | LangChain, RAG, OpenAI | Built for Production
Udupi, India - 1:03 pm local time
I've spent the past year building production grade LLM systems end to end:
→ Multi agent LangGraph workflows with a parent ReAct agent delegating to specialized child agents
→ Hybrid retrieval over Qdrant with cross encoder reranking and reciprocal rank fusion
→ Persistent memory via mem0 with hierarchical scoping across conversation, project, and organization
→ LLM as judge plus Ragas evaluation pipelines that catch silent regressions before customers do
→ Multi tenant FastAPI microservices with PostgreSQL row level scoping and Qdrant payload filtering
→ Inference gateway via LiteLLM routing across Vertex AI and self hosted vLLM
→ Document processing with Docling, semantic chunking, and LLM powered metadata extraction across distributed Celery workers
What I build for clients:
• Agentic systems that work in production, not demos that break on the second customer
• RAG pipelines tuned for your specific data with eval frameworks that prove it
• MCP integrations so your agents use real tools instead of stubs
• Migration from prototype LangChain code to multi tenant production deployment
• Document ingestion pipelines (PDF parsing, semantic chunking, structured extraction) that scale past 10K docs
Why I'm different:
Most freelancers stop at the prompt. I treat agentic AI as a distributed systems problem with continuous evaluation, persistent state, and multi tenant isolation. The hardest production problems (silent regressions, memory leakage across tenants, cost runaway from chatty agents) are the ones I've already solved.
Stack: Python, FastAPI, LangGraph, LangChain, LlamaIndex, DSPy, Qdrant, vLLM, LiteLLM, mem0, Ragas, DeepEval, Guardrails AI, MCP, Docling, Celery, PostgreSQL, Redis, Docker.
Open to short engagements (RAG pipeline build, agent prototype, eval framework setup) or longer monthly retainers for ongoing production work.
Quick reply within 4 hours during India business hours. Let's talk about what you're shipping.
Steps for completing your project
After purchasing the project, send requirements so Aaqib can start the project.
Delivery time starts when Aaqib receives requirements from you.
Aaqib works on your project following the steps below.
Revisions may occur after the delivery date.
Kickoff call, scope, accuracy threshold, success criteria locked in writing
Document sample review, audit 5-10 of your docs for OCR, layout, edge cases