You will get AI SaaS RAG Knowledge Base | Document Q&A System
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Project details
I build production-ready AI knowledge bases and document Q&A systems. Your users upload PDFs, Word docs, websites, or wikis and get citation-backed answers from your content — not generic AI knowledge.
This is not a chatbot wrapper. I architect the full RAG pipeline: document chunking, OpenAI embeddings, vector database (Qdrant, Pinecone, or pgvector), semantic search, and streaming responses with source citations. Backend is Python/FastAPI or Node.js. Frontend is React/Next.js. Deployed with Docker on AWS or Vercel.
You get multi-tenant auth, an admin dashboard, usage analytics, and REST API endpoints for integration into your existing product. I architected this exact system for IDEO Lab, a multimodal AI workspace that secured $60M in VC funding.
Best for SaaS founders and product teams who need AI document search, knowledge base AI, or chat-with-PDF built into their platform.
Message me your document types and tech stack. I will reply with a technical direction within 24 hours.
This is not a chatbot wrapper. I architect the full RAG pipeline: document chunking, OpenAI embeddings, vector database (Qdrant, Pinecone, or pgvector), semantic search, and streaming responses with source citations. Backend is Python/FastAPI or Node.js. Frontend is React/Next.js. Deployed with Docker on AWS or Vercel.
You get multi-tenant auth, an admin dashboard, usage analytics, and REST API endpoints for integration into your existing product. I architected this exact system for IDEO Lab, a multimodal AI workspace that secured $60M in VC funding.
Best for SaaS founders and product teams who need AI document search, knowledge base AI, or chat-with-PDF built into their platform.
Message me your document types and tech stack. I will reply with a technical direction within 24 hours.
AI Algorithms
AdaBoost, Convolutional Neural Network, Generative Adversarial Network, Large Language Model, Long Short-Term Memory Network, Multilayer Perceptron, Multimodal Large Language Model, Regression Analysis, Transformer Model, YOLOAI Applications
AI Chatbot, AI Mobile App Development, AI Text-to-Image, AI-Enhanced Medical Imaging, Automatic Speech Recognition, Conversational AI, Image Processing, Machine Translation, Natural Language Generation, Natural Language Understanding, Object Detection, Text RecognitionAI Development Language
PythonAI Tools
Azure OpenAI, Copy.ai, GitHub Copilot, Gradio, Hugging Face, Jasper AI, Microsoft 365 Copilot, NVIDIA AI Platform, Replit, StreamlitAI Models
AlphaCode, BERT, BLOOM, ChatGPT, DALL-E, Jurassic-2, LLaMA, Midjourney AI, OpenAI Codex, Stable Diffusion, WhisperWhat's included
| Service Tiers |
Starter
$500
|
Standard
$1,500
|
Advanced
$3,000
|
|---|---|---|---|
| Delivery Time | 2 days | 5 days | 15 days |
Number of Revisions | 2 | 4 | 6 |
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.
Fast Delivery
+$1,500 - $5,000
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Completed the project successfully.
About Muhammad
AI SaaS Developer | Voice AI Agents | RAG & LLM | MVP to Production
100%
Job Success
Taxila, Pakistan - 9:06 am local time
Most AI projects die between the demo and production. The prototype works on three test files, then falls apart on real concurrency, real edge cases, and real billing logic. Anyone can wire up an LLM API call. I build the version that survives production: secure auth, admin dashboards, Stripe subscriptions, credit metering, multi-tenant architecture, and audit trails that keep compliance teams happy.
WHAT I BUILD
1. AI SaaS Platforms from MVP to production, not wrappers.
NOLMT is live with paying users across four image and video generation workflows, per-feature credit metering, Stripe subscriptions and top-ups, admin revenue analytics, and an S3 asset pipeline. ValueBrand is published on the Shopify App Store, integrating Meta, YouTube, LinkedIn, X, and Google Merchant Center with AI brand health scoring and sentiment analysis. Accelerate runs a real-time voice and text coaching platform gated by Kajabi subscriptions, powered by a RAG-backed knowledge base and ElevenLabs voice synthesis.
2. RAG Pipelines & Knowledge Systems answers grounded in your documents, not generic AI knowledge. IDEO Lab processes multimodal content PDFs, websites, YouTube, social media, audio, and video through a node-based canvas that builds vector stores, runs semantic search, and streams citation-grounded responses with unified token metering. Reviflow serves 5,000+ subscribers with AI drawing review, PDF versioning, interactive markup, and page-level vector citations. I build document Q&A, chat with PDF, and knowledge base AI using Qdrant, Pinecone, and pgvector so non-technical teams can manage the knowledge base without touching code.
3. AI Agents & Voice Automation production agents that handle real telephony, not chatbot toys. Voxr.ai partners with NVIDIA and ElevenLabs to run human-like outbound sales calls at sub-second latency with real-time transcription via Deepgram, per-lead state machines, and Redis-backed retry logic. Chatley handles 24/7 voice automation for 1,000+ US businesses using Twilio, CRM integration, and intelligent call routing. StackFuse orchestrates AI-driven LinkedIn, Email, WhatsApp, and Instagram campaigns with RAG personalization, lead qualification, and queue architecture that processes millions of leads without duplication. JobTalk automates first-round recruitment interviews with LiveKit voice, cutting enterprise hiring cycles from weeks to hours.
4. AI Integration & Prototype Rescue adding LLM features to existing products without destabilizing what works. I rescue vibe-coded MVPs built with Lovable, Bolt, v0, or Base44 and take them to stable production: schema fixes, Supabase auth and RLS hardening, clean architecture, and maintainable code your team can own. Kadastra.ai ingests complex planning certificates and contract PDFs through an industrial-grade FastAPI engine with AWS Textract OCR, concurrent GIS validation, and automated report generation that eliminated 90% of manual legal reviews.
STACK
Frontend: React, Next.js, TypeScript, TailwindCSS, shadcn/ui
Backend: Node.js, NestJS, Express, Fastify, Python, FastAPI
Database: PostgreSQL, Supabase, MongoDB, Qdrant, pgvector, Prisma
AI: OpenAI API, Anthropic Claude API, Gemini, LangChain, LangGraph, RAG, embeddings, function calling, streaming
Voice & Agents: Twilio, ElevenLabs, Deepgram, Vapi, LiveKit, WebSockets, Redis
Integrations: Gmail API, Microsoft Graph, Shopify API, Stripe, Kajabi, Zapier, Unipile
Infrastructure: AWS (S3, Lambda, Textract), Docker, CI/CD, Vercel
HOW I WORK
Scope first: written milestones, fixed deliverables, defined revision limits. I work independently from briefs, ship weekly demos you can click, and provide daily progress updates. AI-assisted development with Claude Code and Cursor, with human architecture, code review, and testing. Speed without slop. Clean, documented code in a repo you own from day one. No lock-in.
BEST FIT
Founders and product teams with funded or revenue-generating products who need AI features that hold up with real users, or an MVP that needs to become production-grade. Long-term technical partnerships, not one-off gigs. Confidential and NDA work welcome. Custom builds start at $5,000; scoped audits and architecture sprints available as smaller entry points.
NOT A FIT
Single-page GPT wrappers, no-code-only builds, or projects without a defined scope.
Send me your product, your current stack, and what you want the AI to do. I will reply with a technical direction and a realistic scope, not a template.
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.
Architecture & vector DB design
I analyze your documents and design the chunking strategy, embedding model, and vector database schema (Qdrant/Pinecone/pgvector). I deliver a technical architecture doc for your approval before writing code.
Backend RAG pipeline development
I build the document ingestion API, embedding pipeline, semantic search layer, and streaming chat endpoint with citation tracking. All grounded in your uploaded documents, not generic AI knowledge.