You will get I will build a LangChain RAG system into your Nest.js app
Project details
I'll build a production-grade Retrieval-Augmented Generation (RAG) system inside your Ruby on Rails or NestJS app. Your users ask questions in natural language, the system retrieves relevant context from your data, and an LLM (OpenAI, Claude, or Gemini) returns grounded answers with citations.
What's included:
• Document ingestion pipeline with chunking, metadata, and async processing
• Vector database setup (pgvector, Pinecone, or Weaviate)
• Embedding generation with versioning for zero-downtime re-indexing
• Hybrid retrieval (semantic + keyword) for better accuracy
• LLM integration with cost controls, rate limits, and request caching
• Multi-tenant isolation so users only retrieve their own data
• Basic monitoring: query volume, latency, cost per query
• Documentation and 30-min handover call
Tech: Rails or NestJS, LangChain, OpenAI/Claude/Gemini APIs, pgvector or Pinecone, Sidekiq, Redis, PostgreSQL.
Standard scope: 3 document types, 1 LLM provider, deployed to your infra. Working RAG endpoint in 2 weeks.
Built for SaaS founders who want AI grounded in their own data not generic ChatGPT wrappers.
What's included:
• Document ingestion pipeline with chunking, metadata, and async processing
• Vector database setup (pgvector, Pinecone, or Weaviate)
• Embedding generation with versioning for zero-downtime re-indexing
• Hybrid retrieval (semantic + keyword) for better accuracy
• LLM integration with cost controls, rate limits, and request caching
• Multi-tenant isolation so users only retrieve their own data
• Basic monitoring: query volume, latency, cost per query
• Documentation and 30-min handover call
Tech: Rails or NestJS, LangChain, OpenAI/Claude/Gemini APIs, pgvector or Pinecone, Sidekiq, Redis, PostgreSQL.
Standard scope: 3 document types, 1 LLM provider, deployed to your infra. Working RAG endpoint in 2 weeks.
Built for SaaS founders who want AI grounded in their own data not generic ChatGPT wrappers.
Programming Languages
HTML & CSS, JavaScript, TypeScriptCoding Expertise
Cross Browser & Device Compatibility, Performance Optimization, SecurityWhat's included $400
These options are included with the project scope.
$400
- Delivery Time 7 days
- Number of Revisions Unlimited
- Number of Pages 2
- Design Customization
- Content Upload
- Responsive Design
- Source Code
Frequently asked questions
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DB
Dina B.
Dec 10, 2025
Frontend/Full stack development -
Excellent and timely. Total professional Service.
MP
Michael P.
Oct 13, 2025
Software Developer
Hamza did a great job on our calendar integration and our coupon system for stripe.
JO
Jetrino O.
Jan 19, 2025
Developer for Arduino and App Design
Great guy, nice to work with. Project went long and he accommodated.
Iv
Ilze v.
Feb 15, 2024
Aws deployment /local machine setup and github repo help
JS
John S.
Jan 24, 2024
Small fix needed for issue with saving of data
Thanks Hamza! I look forward to working with you on future milestones!
About Hamza
AI Engineer | LangChain, OpenAI, RAG, Agentic AI | NestJS Backend
64%
Job Success
Lahore, Pakistan - 2:57 am local time
AI agents, and the NestJS backends that make them reliable at scale.
Hi, I'm Hamza. For the last 4+ years I've been building production backend
systems with NestJS, Node.js, and Ruby on Rails. The past year has been heavy
on shipping LLM features for real users not demos, not prototypes, but
features running in production with paying customers.
If you're building an AI-powered SaaS, integrating LLMs into an existing
product, or trying to make your AI features actually reliable and cost-
controlled, that's exactly what I do day-to-day.
𝗪𝗛𝗔𝗧 𝗜 𝗕𝗨𝗜𝗟𝗗:
✅ AI Agents & Agentic Systems: Multi-step LLM workflows with LangChain
and LangGraph, function calling, tool use, structured outputs
✅ RAG Pipelines: Document Q&A, semantic search, contextual chatbots
using pgvector, Pinecone, and OpenAI/Anthropic embeddings
✅ NestJS & Node.js Backends: Multi-tenant SaaS, REST and GraphQL APIs,
PostgreSQL, Redis, BullMQ for async job processing
✅ LLM Cost Optimization: Caching strategies, model routing, prompt
compression. Cut one client's monthly OpenAI bill by ~40%.
✅ Real-Time Systems: WebSocket infrastructure (built one platform that
scaled to 500,000+ monthly active users)
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𝗧𝗘𝗖𝗛 𝗦𝗧𝗔𝗖𝗞:
🔹 AI & LLMs: LangChain, LangGraph, OpenAI (GPT-4o), Anthropic Claude,
RAG, embeddings, function calling, structured outputs, streaming
🔹 Vector DBs: pgvector, Pinecone, ChromaDB
🔹 Backend: NestJS, Node.js, TypeScript, Ruby on Rails, GraphQL,
WebSockets, BullMQ, microservices
🔹 Databases: PostgreSQL, MySQL, MongoDB, Redis
🔹 Cloud & DevOps: AWS (EC2, S3, RDS, Lambda), GCP, Docker, CI/CD
🔹 Frontend (when needed): React.js, Next.js, Tailwind CSS
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𝗥𝗘𝗖𝗘𝗡𝗧 𝗪𝗢𝗥𝗞:
🔸 Meet-ting: AI scheduling platform that reads emails
and books meetings automatically. Built the NestJS backend and the
LLM pipeline using LangChain + OpenAI function calling.
🔸 Emerald Chat: Real-time anonymous chat platform
that grew to 500K+ monthly users. Built WebSocket infrastructure
and content moderation systems.
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
𝗛𝗢𝗪 𝗜 𝗪𝗢𝗥𝗞:
I focus on production quality, not demos. That means proper error handling,
retries for flaky LLM calls, cost monitoring, and async job processing so
your API stays fast even when the model is slow.
I'm based in Lahore (UTC+5) and work overlapping hours with US, UK, EU,
and Middle East timezones. Strong English communication. Clear updates,
no ghosting.
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📩 Send me a message to discuss your project. Happy to do a free
15-minute consultation to scope your AI/backend needs.
Steps for completing your project
After purchasing the project, send requirements so Hamza can start the project.
Delivery time starts when Hamza receives requirements from you.
Hamza works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements & Setup
– Review your SaaS needs (auth roles, billing logic, data model). – Set up Rails project with gems and dependencies.
Authentication & Authorization
– Implement JWT/OAuth2-based login. – Configure role-based access control (admin, user, etc.).