You will get a RAG system on your docs: Pinecone/pgvector + LangChain, deployed
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Project details
Most "AI search" demos fall apart the moment you throw real documents at them. Slow retrieval, irrelevant results, hallucinated answers. This project delivers a production-grade RAG pipeline that actually works under real usage.
You bring your documents. I build a system that ingests them, embeds them into a vector store, and serves accurate, source-cited answers through a clean REST API and query interface.
Built on your stack or mine: Next.js, OpenAI, Pinecone, TypeScript, deployed to Vercel. Rate limiting, error handling, and cost controls included from day one, not bolted on later.
I've built this architecture for a production AI platform (under NDA) processing 10,000+ documents daily. The same patterns apply whether you're indexing 50 PDFs or a full knowledge base.
Best fit: SaaS founders adding document search, internal tools teams, legal or compliance products, customer support platforms.
You bring your documents. I build a system that ingests them, embeds them into a vector store, and serves accurate, source-cited answers through a clean REST API and query interface.
Built on your stack or mine: Next.js, OpenAI, Pinecone, TypeScript, deployed to Vercel. Rate limiting, error handling, and cost controls included from day one, not bolted on later.
I've built this architecture for a production AI platform (under NDA) processing 10,000+ documents daily. The same patterns apply whether you're indexing 50 PDFs or a full knowledge base.
Best fit: SaaS founders adding document search, internal tools teams, legal or compliance products, customer support platforms.
AI Development Type
Knowledge Representation, Recommendation SystemWhat's included
| Service Tiers |
Starter
$500
|
Standard
$1,200
|
Advanced
$1,800
|
|---|---|---|---|
| Delivery Time | 7 days | 12 days | 18 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Detailed Code Comments | |||
Knowledge Graph | - | - | |
Model Documentation | - | ||
Ontology | - | - | - |
Source Code | |||
Taxonomy | - | - | - |
Optional add-ons
You can add these on the next page.
Additional Revision
+$100Frequently asked questions
13 reviews
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CL
Chisung L.
Jan 29, 2026
Public Employer Job Board
Great communication. Good team player. With Abdul's expertise, sometimes we would recommend features or changes that we had not thought of. Insightful.
BW
Ben W.
Jan 4, 2026
Tile-Based Contact & Property Search with AI Integration
Working with Abdul was a great experience. Great communication and fast with progress.
CL
Chisung L.
Sep 8, 2025
Build SEO-Optimized Job Board with Next.js (Bullhorn API, JSON-LD Schema, Server-Side Rendering)
This is second project Abdul finished for us and he has already started third project for us. Needless to say we are quite satisfied with his work. Every time we release a new project, we have many developers apply for the project, we do conduct interviews for each project. Yet, we've gone back to Abdul for the third time. What's different about Abdul is his communication, despite our time zone difference and work our differences, he is always responsive and sets the expectation clear. He asks thoughtful questions. As he dives deeper into the project, he provides his insights and recommendations as well.
CL
Chisung L.
Jul 26, 2025
Expert Back-End Developer for Bullhorn API Integration (Serverless Middleware)
I had an urgent request that started on Friday and needed to finish by Monday afternoon. Meeting this strict deadline was top priority without comprising the work. Abdul delivered. Great communication. Now we are going to do few more project with him.
TA
Taimoor A.
Aug 12, 2024
Expert Needed for Electron application
Abdul Rehman did an outstanding job working on our Electron app. He was efficient, knowledgeable, and delivered high-quality work on time. Highly recommend him for any future projects!
About Abdul
AI SaaS Developer | LangChain, RAG, AI Agents | Next.js & Node.js
100%
Job Success
Gujranwala, Pakistan - 8:06 am local time
Every interaction a customer has with your business shapes whether they trust you, buy from you, or go somewhere else.
I help businesses create exceptional digital experiences, from the first mobile visit to post-purchase interactions, while automating repetitive internal work using AI.
💡 Clients Say
“We reduced loading times by 80% after Abdul led a Next.js performance overhaul. He’s now easily in the top tier of developers who understand full-stack performance deeply.”
💡 Recent Results
- 50% productivity boost for a dental group using an Electron app that unified all internal tools.
- 50% faster user experience after migrating an e-commerce and Referral site from .NET → Next.js.
- 70% sales increase for a SaaS by building AI-driven recruitment workflows (resume tailoring, outreach automation, RocketReach + OpenAI).
💡 What I Bring
- AI-powered full-stack applications (Next.js + Node.js + OpenAI)
- RAG-lite and workflow automation systems for sales, operations & recruiting
- High-performance SaaS dashboards optimized for speed and scale
- Backend APIs & microservices with robust architecture and clean data models
- Electron desktop apps for internal productivity and automation
💡 Core Tech Stack
Next.js, React, Node.js, FastAPI, OpenAI APIs, RAG Systems, Supabase, Postgres, Neon, AWS, Vercel, Electron, CI/CD
💡 Ready to Build?
Click “Send a Message” and I’ll review your goals, propose a plan, and outline the highest-ROI path for your product.
Steps for completing your project
After purchasing the project, send requirements so Abdul can start the project.
Delivery time starts when Abdul receives requirements from you.
Abdul works on your project following the steps below.
Revisions may occur after the delivery date.
Scope confirmation and environment setup
I review your documents and requirements, confirm the architecture, and set up the vector store and API connections. You get a confirmation message within 24 hours of purchase.
Document ingestion and embedding pipeline
Your documents are processed, chunked, and embedded into the vector store. I share a staging URL so you can see live progress against your actual data.