AI Agents That Cite Their Sources: Custom RAG Systems
Service Package
Skills
Roles
Tools
What You Can Expect
Evaluation Report
A before and after breakdown of retrieval quality on your actual data, showing what the agent gets right, where it struggles, and what would improve it further.
Deployed AI Agent
A working, hosted AI agent your team can use from day one, containerized with Docker and deployed to your platform of choice.
Custom Knowledge Base and Retrieval Pipeline
Your documents ingested, chunked, and indexed into a vector database tuned for your content. Includes structure aware chunking and reranking so the agent retrieves passages
REST API and Integration
A documented FastAPI endpoint so the agent can be dropped into your existing product, internal tool, or workflow. Includes streaming responses so long answers appear as they generate.
Source Code, Tests, and CI
The full repository, yours to keep, with a pytest suite covering the core logic and a GitHub Actions pipeline that runs on every push. You are never locked into me to change it later.
Documentation and Handover Walkthrough
A written setup and architecture guide plus a live walkthrough session covering how it works, how to add documents, and how to run it yourself.
Process
1. Scoping call We work out what you actually need and, just as importantly, whether AI is the right tool for it. You leave with a clear scope, timeline, and price. If I am not the right fit, I will say so. 2. Data and requirements audit I look at the documents or data the system will run on before writing any code. Data quality decides the ceiling on quality of results, so I flag gaps and problems here rather than three weeks in. 3. Retrieval prototype I build the retrieval layer first and show you real answers on your real data early. This is the cheapest point to find out the approach needs to change, and you see progress before committing to the full build. 4. Build The full agent, tools, and API, developed in short cycles with something you can look at at the end of each one. No silent stretches where you have no idea what is happening. 5. Evaluate and harden I test against baselines and edge cases, then handle the unglamorous parts: failure paths, rate limiting, session isolation, and streaming. This is the difference between a demo and something real users can touch. 6. Deploy and hand over Deployed, documented, and walked through live with your team. You get the repository, the tests, and the CI pipeline, plus a support window for questions and fixes.
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