Your company knows the answer. Finding it takes too long.
RAG development and AI document search
Retrieval-augmented generation for internal knowledge bases, document assistants, and search experiences that need answers grounded in your own information.
The situation
RAG connects a language model to relevant information at the time a question is asked. It is a fit when the answer depends on your documents, changes regularly, or needs a source someone can inspect. An internal policy assistant and a technical documentation search tool both fit this pattern, but they need different evaluation questions and access rules.
The difficult part is often the information pipeline. A scanned PDF, a revised policy, and three contradictory versions of the same page do not become a reliable knowledge base by putting them in a vector database. I scope ingestion, retrieval, permissions, answer behavior, and maintenance together.
How I approach it
Audit the source material
We identify the authoritative systems, document formats, update frequency, and access restrictions. A small representative set is enough to expose extraction problems before committing to a full import.
Design retrieval around the questions
Exact identifiers, product names, and policy questions require different retrieval behavior. We compare keyword and semantic retrieval on examples from your business and add reranking only if the results justify it.
Preserve identity and permissions
Source identifiers and access metadata travel through the pipeline. Retrieval must respect the signed-in user's permissions before material reaches the model, and answers should point back to the source and version used.
Measure retrieval and answers separately
A wrong answer might come from missing evidence or from misuse of correct evidence. Separating those checks makes failures diagnosable. The evaluation includes unanswered questions, contradictory sources, stale content, and restricted documents.
What gets delivered
- Document ingestion and update pipeline
- Search or conversational interface with citations
- Permission-aware retrieval design
- Representative retrieval and answer evaluation set
- Source deletion, refresh, and maintenance procedures
What it costs to get wrong
- Deleting a source file leaves its extracted content searchable.
- An answer cites a document that does not actually support the claim.
- A successful demonstration hides poor retrieval on the questions employees really ask.
Why you can believe this
I document my retrieval approach in the RAG explainer linked below. For implemented AI controls, Sentinel and ai-os are the independent projects I can walk through; they demonstrate the boundaries and verification principles applied here.
Questions buyers ask
- Should we use RAG or fine-tune a model?
- If the main need is current, attributable company knowledge, start by evaluating retrieval. Fine-tuning addresses a different problem, such as consistent behavior or task performance. The decision should follow the failure you need to solve, rather than a preference for a particular technique.
- Can you work with an existing vector database?
- Yes. I first assess its ingestion, metadata, deletion behavior, and retrieval quality. Replacing the database is not necessarily the useful change; improving source quality or evaluation may matter more.
- Can company documents stay private?
- The architecture can restrict access and use local or managed infrastructure according to your requirements. We agree the data flow, model provider, retention settings, and permitted users before uploading material. Privacy depends on those choices, not on the label RAG.
- What do you need to scope a document search project?
- A sample of documents, examples of real questions, the expected number of users, and an explanation of who may access which information. Redacted examples are enough for an initial conversation.
Related writing
- RAG Explained: How to Make AI Actually Useful With Your DataRetrieval Augmented Generation is the key to building AI features that are accurate and grounded in your business data. Here's how it works.
- AI Chatbot for Your Business: The Complete Guide to Custom Chatbot DevelopmentEverything you need to know about building a custom AI chatbot for your business. Covers costs, features, platforms, and when to build vs buy.
Other capabilities
- Agentic automationYour team spends hours a week on work a system should be doing.
- Guardrails and auditYou want AI in the business but cannot defend it to a client, an auditor, or yourself.
- AI product developmentYou need the product itself shipped, not a proof of concept.
- AI systems reviewYou already have AI in production and are not sure what it is doing.
- Custom AI chatbot developmentYour customers need answers your team keeps repeating.
- LLM API integration for existing productsYou have an application. Now you need AI that fits it.
- Freelance iOS and SwiftUI app developmentYou need someone who can take an iPhone app all the way to release.
- Freelance Next.js and React developmentYour web product needs more than a landing page.
- Creative WebGL and Three.js developmentYour website should be remembered after the tab closes.
- AI prototype rescue and production hardeningYour AI-built app works in the demo. You need it to work for customers.