I will develop a custom AI agent or RAG app on your own data

I will develop a custom AI agent or RAG application trained on your own data — a private assistant that answers from your documents, takes actions, and ships to production.

Stop pasting your knowledge base into a generic chatbot and hoping for the best. I build retrieval-augmented (RAG) applications and tool-using AI agents grounded in your content — your PDFs, support tickets, product docs, database, Notion, spreadsheets, or website — so answers are accurate, traceable, and tied to sources instead of hallucinated. You get a real deployed system, not a demo notebook.

What you get

• A custom AI agent or RAG app built on your data — ingestion of your documents and data sources (PDF, DOCX, Markdown, HTML, CSV, websites, Notion, Google Drive, SQL/Postgres, and common APIs), cleaned and chunked for retrieval.
• A vector search pipeline — embeddings + a vector store (pgvector, Pinecone, Qdrant, Chroma, or Weaviate) with semantic + keyword (hybrid) retrieval and re-ranking so the right context reaches the model.
• Grounded answers with citations — every response can cite the source chunk/document so users (and you) can verify where an answer came from.
• Your choice of model — Claude, GPT, Gemini, or an open-weight model (Llama, Mistral, Qwen) via API or self-hosted; I help you pick based on accuracy, cost, latency, and privacy needs.
• Agent capabilities (optional) — tool/function calling so the assistant can do more than answer: query a database, call your API, search the web, file a ticket, send an email, or run multi-step workflows.
• A usable interface — a chat widget you can embed on your site, a standalone web app, a Slack/Discord/WhatsApp bot, or a clean REST API your own product can call. Your pick.
• Deployment to production — shipped to your cloud (AWS, GCP, Azure, Cloudflare, Vercel, or your VPS) or mine, with environment config, secrets handling, and a documented runbook.
• Source code + handover — you own the code. I deliver a clean repository, README, architecture notes, and a walkthrough so your team can run and extend it.
• Evaluation & guardrails — a small test set of real questions, prompt hardening against injection, refusal behavior for out-of-scope queries, and basic logging so you can see what users ask.

Plans

	Basic	Standard	Premium
Best for	A focused proof-of-concept on one data source	A production RAG assistant for your team or customers	A full tool-using agent integrated into your stack
Data sources	1 source (e.g. a folder of PDFs or one site)	Up to 3–4 mixed sources	Many sources + live/database connectors
Retrieval	Vector search with citations	Hybrid search + re-ranking + metadata filters	Hybrid + re-ranking + query routing across sources
Agent / actions	Q&A only	Q&A + 1 simple tool/action	Multi-tool agent, API calls, multi-step workflows
Interface	API endpoint or simple chat page	Embeddable widget or one bot integration	Custom web app or multiple integrations
Deployment	Deployed, single environment	Deployed with secrets/env config + runbook	Production deploy with monitoring & eval suite
Revisions	1 round	2 rounds	Multiple rounds until accepted
Code handover	Yes	Yes + README & architecture notes	Yes + full docs & team walkthrough

Optional monthly hosting / maintenance retainer is available on any tier — I keep the app running, update dependencies and models, monitor usage, refresh the index as your data changes, and handle small tweaks. Discuss it before we start so scope is clear.

How it works

1. You send a brief. Tell me your goal, who'll use it, what data it should answer from, and any must-have integrations. Sample documents or read-only access help me scope accurately.
2. We agree on scope. I confirm the tier, data sources, model, interface, and deployment target in writing — no surprises, no scope creep.
3. I build the data pipeline. I ingest, clean, chunk, and embed your content into a vector store, then wire up retrieval and test that the right context comes back for real questions.
4. I build the agent/app. Prompting, citations, any tools or actions, and the interface (API, widget, bot, or web app) you chose.
5. I evaluate and harden. I run your real questions against it, tune retrieval and prompts, and add guardrails against prompt injection and off-topic use.
6. I deploy and hand over. The app goes live on your target environment; you receive the repository, docs, a runbook, and a live walkthrough. Revisions follow per your tier.

Why choose this

• Grounded, not guessed. Answers are tied to your sources with citations, so you can trust and verify them — the whole point of RAG over a vanilla chatbot.
• You own everything. Source code, prompts, and infrastructure are yours. No lock-in to a closed platform you can't export from.
• Model-agnostic and honest. I'm not selling you one vendor. I recommend the model that fits your accuracy, latency, cost, and privacy constraints — including private/self-hosted options when your data can't leave your environment.
• Production-minded. Secrets handling, logging, evaluation, and a runbook are part of the build, not an afterthought — so it survives contact with real users.
• Direct work, clear communication. You work with the person writing the code. Plain-English updates, no agency middle layer.

Who it's for / use cases

• SaaS teams wanting an in-product assistant or "ask the docs" feature grounded in their own documentation.
• Support & ops teams building an internal helpdesk bot over tickets, policies, and SOPs to cut repetitive questions.
• Consultancies, law, and finance firms that need a private assistant over confidential reports and contracts — with self-hosted options.
• Course creators, publishers, and communities turning their content library into a chat-with-your-content experience.
• Founders validating an AI feature who need a working, deployed prototype on real data to show users or investors.
• Companies with messy internal knowledge — Notion, Drive, wikis, spreadsheets — that want one assistant to search across all of it.

FAQ

Q: Is this an API key I just plug in, or do you actually build something? I build and deliver a custom application for you. It's a scoped development project — I design the pipeline, write the code, deploy it, and hand it over. It is not a self-serve subscription.

Q: Will it use my private data and keep it confidential? Yes. The system answers from the data you provide, and I'll sign an NDA on request. For sensitive data I can deploy entirely within your own cloud and use self-hosted open-weight models so nothing leaves your environment.

Q: Which AI model do you use? Whichever fits best — Claude, GPT, Gemini, or open models like Llama, Mistral, or Qwen. I'll recommend based on your accuracy, speed, cost, and privacy needs, and you make the final call.

Q: How accurate is it, and what about hallucinations? RAG dramatically reduces hallucination by grounding answers in your retrieved content with citations. I tune retrieval against your real questions and add guardrails so the assistant says "I don't know" instead of inventing answers when context is missing.

Q: What data sources can you connect? PDFs, Word, Markdown, HTML, CSV/Excel, websites, Notion, Google Drive, and SQL databases like Postgres — plus many APIs. Tell me what you have and I'll confirm feasibility before we start.

Q: Do I get the source code? Yes. You own the complete repository, prompts, and configuration, with documentation so your team can run and extend it. No platform lock-in.

Q: Can you maintain it after launch? Yes — an optional monthly retainer covers hosting, monitoring, dependency and model updates, re-indexing as your data changes, and small tweaks. It's arranged separately from the build.

Q: How do we start and what do you need from me? Send your goal, intended users, data sources, and any required integrations — ideally with a few sample documents. I'll confirm scope, then begin the build.