You will get a custom AI chatbot with RAG trained on your documents and data
Project details
Stop making your team dig through documents. I build a Retrieval-Augmented
Generation (RAG) AI chatbot that lets anyone chat with your business data
(PDFs, Docs, spreadsheets, websites) in plain English.
Why this RAG setup:
• Grounded in your data. Every answer returns a citation you can click and
verify. If the answer is not in your documents, it says so instead of
guessing.
• Tuned, not guessed. I test retrieval against your real example questions
before handover and show you the results.
• You own it. Full source code on every tier. No lock-in, no monthly fee
to me.
Stack: Python, LangChain, OpenAI / Claude / Gemini, vector databases
(Pinecone, Qdrant, Chroma, pgvector), hybrid retrieval, re-ranking, and a
chat UI or embeddable widget.
Typical uses: internal knowledge base, customer support, employee
onboarding, policy and compliance lookup, sales enablement, technical
documentation.
Not sure which tier fits? Message me with your document count and where you
want the chatbot to live. If RAG is not the right tool for your problem, I
will tell you that too.
Generation (RAG) AI chatbot that lets anyone chat with your business data
(PDFs, Docs, spreadsheets, websites) in plain English.
Why this RAG setup:
• Grounded in your data. Every answer returns a citation you can click and
verify. If the answer is not in your documents, it says so instead of
guessing.
• Tuned, not guessed. I test retrieval against your real example questions
before handover and show you the results.
• You own it. Full source code on every tier. No lock-in, no monthly fee
to me.
Stack: Python, LangChain, OpenAI / Claude / Gemini, vector databases
(Pinecone, Qdrant, Chroma, pgvector), hybrid retrieval, re-ranking, and a
chat UI or embeddable widget.
Typical uses: internal knowledge base, customer support, employee
onboarding, policy and compliance lookup, sales enablement, technical
documentation.
Not sure which tier fits? Message me with your document count and where you
want the chatbot to live. If RAG is not the right tool for your problem, I
will tell you that too.
Programming Languages
JavaScript, Python, TypeScriptCoding Expertise
Cross Browser & Device Compatibility, PSD to HTML, Performance OptimizationWhat's included
| Service Tiers |
Starter
$119
|
Standard
$329
|
Advanced
$659
|
|---|---|---|---|
| Delivery Time | 3 days | 7 days | 14 days |
Number of Revisions | 2 | 3 | 4 |
Number of Pages | 125 | 350 | 900 |
Design Customization | - | ||
Content Upload | |||
Responsive Design | - | ||
Source Code |
Frequently asked questions
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SI
Sheza I.
May 7, 2025
MERN Project Management with AI Integration
Delivered high-quality work on time and communicated clearly throughout the project. Highly recommended!
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Juan Camilo G.
Jan 8, 2025
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CS
Christian Angelo S.
Nov 1, 2024
React Native Developer
AT
Awb T.
Apr 6, 2022
Full-stack MERN developer
Good communication and delivering quality work on time.
About Ayaz
AI Agent Developer | AI Automation Expert | RAG, LLM, MCP | Claude n8n
100%
Job Success
Lahore, Pakistan - 11:05 am local time
I help startups and growing businesses ship real AI apps: LLM-powered AI agents, RAG chatbots trained on their own data, AI automation workflows, and AI integration into existing web and mobile products. OpenAI, Claude, Gemini, LangChain, LangGraph, and the full stack around them.
If you want an AI developer who owns the whole thing (model choice, retrieval, evals, cost control, deployment, and the app it lives in), you're in the right place.
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
𝗪𝗛𝗔𝗧 𝗜 𝗕𝗨𝗜𝗟𝗗
𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 & 𝐌𝐮𝐥𝐭𝐢-𝐀𝐠𝐞𝐧𝐭 𝐒𝐲𝐬𝐭𝐞𝐦𝐬
- AI agents with tool calling, memory, and orchestration
- LangChain, LangGraph, CrewAI, OpenAI Agents SDK
- Sales, support, research, and internal ops agents
- Multi-agent systems for workflow automation
- n8n, Make, and Zapier wired into agent logic
𝐀𝐈 𝐂𝐡𝐚𝐭𝐛𝐨𝐭𝐬 & 𝐑𝐀𝐆
- RAG chatbots over PDFs, docs, knowledge bases, and databases
- Embeddings, semantic search, hybrid retrieval, re-ranking
- Vector databases: Pinecone, Qdrant, Chroma, pgvector
- Deployed to web widgets, WhatsApp, Slack, and voice
- AI chatbot development with real context handling, not toy demos
𝐀𝐈 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 & 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧
- OpenAI, Claude, and Gemini API integration into existing systems
- AI features inside SaaS platforms, CRMs, and internal tools
- Document processing, data extraction, classification, summarization
- Prompt engineering, structured outputs, function calling
- Evals and regression testing so output quality does not drift
𝐀𝐈 𝐀𝐩𝐩 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 (𝐅𝐮𝐥𝐥 𝐒𝐭𝐚𝐜𝐤)
- AI SaaS platforms and AI MVPs from idea to production
- Next.js, React, Node.js, Python, FastAPI
- Streaming responses, usage metering, multi-tenant billing (Stripe)
- React Native and Flutter apps with AI built in
- AI-powered dashboards, copilots, and recommendation systems
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
𝗪𝗛𝗬 𝗖𝗟𝗜𝗘𝗡𝗧𝗦 𝗞𝗘𝗘𝗣 𝗠𝗘
Most AI builds break after the demo. Retrieval returns the wrong chunk. Token costs triple. The agent loops. I build for the part after the demo:
- Retrieval tuned and measured, not guessed
- Token and latency budgets tracked from day one
- Guardrails, fallbacks, and error handling on every LLM call
- Clean architecture your next developer can actually read
- Production monitoring so you know when quality slips
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
𝗧𝗘𝗖𝗛 𝗦𝗧𝗔𝗖𝗞
𝐌𝐨𝐝𝐞𝐥𝐬 & 𝐀𝐏𝐈𝐬: OpenAI (GPT-4o, GPT-4.1), Claude, Gemini, Llama, Mistral
𝐀𝐈 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬: LangChain, LangGraph, LlamaIndex, CrewAI, Vercel AI SDK
𝐕𝐞𝐜𝐭𝐨𝐫 𝐃𝐁𝐬: Pinecone, Qdrant, Chroma, pgvector, Weaviate
𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧: n8n, Make, Zapier, Celery
𝐁𝐚𝐜𝐤𝐞𝐧𝐝: Python, FastAPI, Node.js, Express, NestJS, .NET Core
𝐅𝐫𝐨𝐧𝐭𝐞𝐧𝐝: React, Next.js, TypeScript, Tailwind
𝐌𝐨𝐛𝐢𝐥𝐞: React Native, Flutter
𝐃𝐚𝐭𝐚: PostgreSQL, MongoDB, Redis, Supabase, Firebase
𝐈𝐧𝐟𝐫𝐚: Docker, AWS, GCP, Vercel, serverless
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𝗧𝗬𝗣𝗜𝗖𝗔𝗟 𝗣𝗥𝗢𝗝𝗘𝗖𝗧𝗦
✔ AI agent that handles inbound leads end to end
✔ RAG chatbot over an internal knowledge base
✔ AI SaaS MVP, idea to paying users
✔ AI integration into an existing product
✔ Document AI: extract, classify, summarize at volume
✔ AI automation replacing a manual ops workflow
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
𝗛𝗢𝗪 𝗜 𝗪𝗢𝗥𝗞
Scope first, then a small paid pilot, then build. You see working software early and often. No black boxes, no month of silence.
Looking for an AI agent developer, AI chatbot developer, RAG engineer, LLM integration specialist, or someone to build your AI app from scratch? Send me a message with what you're trying to automate. I'll tell you straight whether AI is the right tool for it.
Steps for completing your project
After purchasing the project, send requirements so Ayaz can start the project.
Delivery time starts when Ayaz receives requirements from you.
Ayaz works on your project following the steps below.
Revisions may occur after the delivery date.
Data Ingestion & Vectorization
Processing your documents into clean chunks and embeddings, indexed in a vector database.
RAG Pipeline & Chat Interface
Building the retrieval pipeline (LangChain, LLM, re-ranking) and the chat UI, wired to your data.