You will get an AI system that answers questions from your PDFs and documents
Project details
I build RAG-based document Q&A systems that let teams upload PDFs, documents, and knowledge files, then ask natural language questions and get grounded answers instantly.
Instead of manually searching through files, your users can query the system in plain English and receive accurate responses powered by retrieval-augmented generation, vector search, and LLM integration.
I focus on clean architecture, reliable document ingestion, accurate retrieval, and a smooth chat experience that works in real business workflows. The system can be customized for single or multi-document sources, with options for manual approval, source citations, authentication, and cloud deployment.
If you need an AI knowledge base, internal document assistant, or customer-facing document chatbot, I can build a production-ready solution tailored to your use case.
Instead of manually searching through files, your users can query the system in plain English and receive accurate responses powered by retrieval-augmented generation, vector search, and LLM integration.
I focus on clean architecture, reliable document ingestion, accurate retrieval, and a smooth chat experience that works in real business workflows. The system can be customized for single or multi-document sources, with options for manual approval, source citations, authentication, and cloud deployment.
If you need an AI knowledge base, internal document assistant, or customer-facing document chatbot, I can build a production-ready solution tailored to your use case.
Programming Languages
JavaScript, Python, TypeScriptCoding Expertise
Cross Browser & Device Compatibility, Performance Optimization, SecurityWhat's included
| Service Tiers |
Starter
$199
|
Standard
$699
|
Advanced
$1,459
|
|---|---|---|---|
| Delivery Time | 7 days | 11 days | 19 days |
Number of Revisions | 2 | 5 | 7 |
Number of Pages | 2 | 4 | 6 |
Design Customization | - | ||
Content Upload | |||
Responsive Design | |||
Source Code |
About Amit
AI Engineer | RAG | LLM | MERN | Azure | AWS | Vector DB
Panchkula, India - 10:51 am local time
I'm Amit. I work on AI-heavy web applications — RAG pipelines, LLM integrations, vector search, the stuff that requires you to actually understand how these models behave before wiring them into a product. Underneath that sits solid full stack engineering: MERN, Java backend, cloud infra on Azure and AWS, Kubernetes, Terraform — because an impressive AI layer on a broken backend is just an expensive demo.
I don't take briefs at face value. I look at what you're actually trying to build, where it'll break, and how to make it not do that.
What I Build
AI-powered SaaS platforms with LLM and RAG capabilities
RAG pipelines using Vector Databases (Pinecone, Weaviate, pgvector) and LangChain
OpenAI, Claude, and custom LLM integrations into production applications
AI chat systems, document Q&A tools, and intelligent automation workflows
Admin dashboards, CRM and ERP systems, client portals
Multi-tenant platforms, marketplace and subscription products
Real-time WebSocket applications, workflow automation, API-heavy business tools
AI & LLM Engineering
I build systems beyond a basic OpenAI API call — production-grade pipelines with proper retrieval architecture, context handling, prompt engineering, chunking strategies, embedding workflows, and cloud deployment on Azure and AWS.
Stack: LangChain, LlamaIndex, OpenAI API, Azure OpenAI Service, AWS Bedrock, vector databases, semantic search, hybrid retrieval systems.
I know how RAG breaks in production — poor chunking, weak embeddings, irrelevant retrieval, latency at scale — and I design around those failure points from day one.
Full Stack Engineering
Frontend: React.js, Next.js, Redux, TypeScript, Tailwind CSS, Material UI — optimized rendering, SEO-friendly architecture, responsive layouts, dashboard interfaces built for complex real-world data.
Backend: Node.js, Express.js, Java backend systems, REST APIs, GraphQL, RBAC authentication, middleware architecture, microservice-friendly patterns, WebSocket integrations, webhook systems. Backends that stay clean six months after launch.
Databases: MongoDB, PostgreSQL, MySQL, Firebase, Supabase, Vector DBs. Schema design, aggregation pipelines, query optimization, indexing — relational and non-relational at scale.
Cloud, Infra & DevOps
AWS, Azure, DigitalOcean — with Kubernetes for orchestration, Terraform for infrastructure-as-code, Docker, Nginx, PM2, GitHub Actions CI/CD, Linux environments. A well-built app on broken infrastructure is still a liability. I handle both.
Integrations
Stripe, PayPal, Twilio, Google APIs, Firebase, Supabase, OAuth 2.0, CRM integrations, email services, analytics platforms, webhooks, and custom third-party API layers.
What I Prioritize
Clean architecture from day one, not after the rewrite
AI pipelines that work in production, not just staging
Backend systems that scale without structural changes
Frontend performance with real SEO value
Secure APIs and authentication systems
Infrastructure that doesn't need babysitting
Code the next developer can actually read
Who I Work With
Startups, founders, product teams, and agencies that need someone who thinks technically, communicates clearly, and owns full product development without hand-holding. If you've been burned by a developer who vanished post-delivery or handed over something unmaintainable — that's exactly the gap I fill.
Core Skills
AI Engineering, RAG Pipelines, LLM Integration, Vector Databases, LangChain, OpenAI API, Azure OpenAI, AWS Bedrock, MERN Stack, React.js, Next.js, Node.js, Express.js, Java Backend, MongoDB, PostgreSQL, TypeScript, GraphQL, REST APIs, SaaS Development, Multi-Tenant Architecture, Kubernetes, Terraform, Docker, AWS, Azure, CI/CD, WebSocket Systems, Authentication Systems, Automation Engineering, CRM & ERP Systems, Cloud Deployment.
Steps for completing your project
After purchasing the project, send requirements so Amit can start the project.
Delivery time starts when Amit receives requirements from you.
Amit works on your project following the steps below.
Revisions may occur after the delivery date.
Document Review & Setup
I'll review your uploaded documents, confirm the file structure, and set up the RAG pipeline for indexing and retrieval.
Testing, Tuning & Handover
I'll test real questions, refine answer quality and retrieval accuracy, then deliver the source code with a walkthrough.