You will get AI Chatbots & RAG Systems Built with NestJS, LLMs & Vector DBs
Project details
I'll integrate AI into your app — from a simple LLM-powered chatbot to a full RAG system that answers from your own data — with clean, production-ready code.
I've built and shipped real AI systems into scalable NestJS backends for genuine business use — including AI calling platforms for hospitals and law firms — using vector databases, embeddings, and LLMs.
What you'll get:
LLM integration (OpenAI, Anthropic, Groq, or open models)
Full RAG pipeline — your docs chunked, embedded, and stored in a vector DB (Qdrant, Pinecone, or pgvector)
Retrieval with source citations
A clean API your app can call directly
Built with LangChain or a custom pipeline on Node.js / NestJS
Integrated into your existing app — no separate service required
I focus on what actually matters in production: accurate retrieval, low hallucination, and cost-efficient token usage. You get documented, production-ready code and a full walkthrough at handover.
I've built and shipped real AI systems into scalable NestJS backends for genuine business use — including AI calling platforms for hospitals and law firms — using vector databases, embeddings, and LLMs.
What you'll get:
LLM integration (OpenAI, Anthropic, Groq, or open models)
Full RAG pipeline — your docs chunked, embedded, and stored in a vector DB (Qdrant, Pinecone, or pgvector)
Retrieval with source citations
A clean API your app can call directly
Built with LangChain or a custom pipeline on Node.js / NestJS
Integrated into your existing app — no separate service required
I focus on what actually matters in production: accurate retrieval, low hallucination, and cost-efficient token usage. You get documented, production-ready code and a full walkthrough at handover.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI-Generated Code, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Tools
Azure OpenAI, Hugging FaceAI Models
AlphaCode, ChatGPT, GPT-3, GPT-4, LLaMA, OpenAI CodexWhat's included
| Service Tiers |
Starter
$300
|
Standard
$900
|
Advanced
$2,200
|
|---|---|---|---|
| Delivery Time | 2 days | 5 days | 10 days |
Number of Revisions | 2 | 3 | 4 |
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 |
Frequently asked questions
About Muhammad
Senior Full-Stack Developer | SaaS, AI & Payments (Next.js/NestJS)
Sialkot, Pakistan - 9:43 am local time
I specialize in taking products from architecture to production — not just writing code, but making the technical decisions that keep a platform scalable as it grows.
Recent work includes:
• Built an enterprise HRMS and multi-tenant SaaS platform (Next.js, NestJS, GraphQL, RabbitMQ, MongoDB) — implemented organization management, role-based access control, and scalable backend services.
• Built a SaaS court booking platform (NestJS, Next.js, Prisma, PostgreSQL, Stripe, AWS) — including scheduling logic, staff/business availability handling, and subscription billing.
• Built multi-tenant booking platforms (React admin panel, NestJS, MongoDB) with an embeddable booking widget that any business site can integrate — letting customers book based on staff and business availability, used across service-industry clients including salon/barber-style booking.
• Built a social/professional networking platform with real-time features, calendar integrations (Google/Outlook), and Stripe/PayPal payments.
• Built an AI-powered risk analysis SaaS using RAG pipelines and vector search.
I also build content-driven platforms using Payload CMS and Strapi — headless CMS setups, custom admin panels, and API-driven content management.
I'm also actively deepening my AI engineering skills — building hands-on with RAG pipelines, LLM integrations, and vector databases (Qdrant, pgvector) beyond client work, so I stay current as the AI tooling landscape moves fast.
Technical stack: Next.js, React, TypeScript, Node.js, NestJS, GraphQL, PostgreSQL, MongoDB, Redis, Prisma, Payload CMS, Strapi, Stripe/PayPal, OpenAI API, RAG/embeddings/vector databases, Docker, AWS.
If you're building or scaling a SaaS product and need someone who can own the technical side end-to-end — architecture, backend, integrations, and deployment — let's talk about your project.
Steps for completing your project
After purchasing the project, send requirements so Muhammad can start the project.
Delivery time starts when Muhammad receives requirements from you.
Muhammad works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery & AI design
I review your use case, data, and goals, then design the right AI/RAG approach and model choice.
Data pipeline
I chunk, embed, and index your documents into a vector database for accurate retrieval.