RAG development: AI answers grounded in your own data
Answers from your own knowledge, with the source one click away.
Retrieval augmented generation systems that answer from your own documents and data, with citations, permissions and measured accuracy.
- Can contractors expense home office equipment?
- Answer cites §4.2
- Outdated FAQ ranked down
- No source for laptops says so
- Category
- AI & Automation
- Deliverables
- 04 core
- Core stack
- Retrieval Augmented Generation (RAG) · Vector Databases · Hybrid Search (BM25 + embeddings)
- Typical first phase
- Discovery
Retrieval Augmented Generation Systems Grounded in Your Own Data
We build RAG (retrieval augmented generation) systems that let an LLM answer from your documents, tickets, wikis and databases instead of its training data. That means ingestion and chunking, hybrid search and reranking, per user permissions, cited answers, and an evaluation set that proves retrieval and answers are good enough before anyone relies on them.
- People search three tools and still ask a colleague for the answer.
- A chatbot pilot sounded confident and was wrong, so nobody trusts it now.
- Documents, tickets and wikis change every week, and nothing keeps up.
- An assistant that answers from your current documents and shows where each answer came from.
- Retrieval that respects who is asking, so nobody sees what they should not.
- A measured accuracy number on real questions, re-checked on every change.
Knowledge ingestion & chunking pipeline
Your sources (PDFs, wikis, tickets, databases, SaaS tools) cleaned, chunked with structure kept, and kept in sync as they change.
- 1Connectors & incremental sync
- 2Structure aware chunking
- 3Metadata & access labels
Hybrid search & reranking
Keyword and semantic search combined, then reranked, so the right passage reaches the model, including exact terms like product codes.
- 1Embeddings & vector store
- 2BM25 + vector hybrid search
- 3Reranking & query rewriting
Permission aware, cited answers
Answers built only from retrieved passages the user may see, with citations, and an honest "I don't know" when the sources are silent.
- 1Per user permission filtering
- 2Inline citations
- 3Refusal & fallback rules
Retrieval & answer evaluation
A test set of real questions with known answers, scored for retrieval hits and answer faithfulness on every change.
- 1Golden question set
- 2Retrieval & faithfulness metrics
- 3Regression checks in CI
AI is in the room from day one.
We don't bolt AI on at the end of a project. It's part of how we research, design, build, test and document every engagement, which is how a small senior team moves quickly without cutting corners. A person reviews everything the models produce.
We build the golden question set from your real tickets and documents in week one, so retrieval and answer quality are scored, not guessed, on every change.
- A person signs off. No model output reaches your users or your codebase without review.
- Your data stays yours. Zero-retention API settings or private models when your data requires it.
- Model-agnostic. We pick models by measured quality, cost and privacy, not by vendor.
Discover
Requirements mapped with LLMsWith your permission, briefs, tickets, call notes and analytics exports go into a private workspace. LLMs map requirements, edge cases and open questions, so nothing said in a workshop gets lost. People run the workshops; the model keeps the record.
Prototype
Something real to react to, earlyFirst-pass screens, flows and copy variants are generated from the spec, then shaped by our designers. Decisions happen on a clickable prototype instead of a document, and the final design system stays human-made.
Build
AI pair programming, human reviewEngineers work with AI coding assistants for scaffolding, tests and refactors. Every change is read by a person and scanned by Caudilysis, our own static analysis platform, before it merges.
Verify
Evals and generated tests in CIAI features get an evaluation set built from real examples, and every model or prompt change is scored against it. Everything else gets generated test cases that an engineer reviews and keeps in the pipeline.
Ship and learn
Docs, runbooks and monitoringDocumentation, runbooks and release notes are drafted with AI and checked by the team. After launch, monitoring and usage data feed straight into the next iteration.
How a RAG development engagement runs.
The indicative shape of a first engagement. Phases overlap on purpose, so we start building while discovery is still sharpening the plan.
- 01DiscoverySources, users, questions and permissions mapped.
- 02PrototypeRetrieval on your real documents, scored on real questions.
- 03Production buildSync, permissions, citations, UI or API.
- 04Launch & tuneMonitoring, feedback loop, eval regressions.
This is indicative only. Every engagement is scoped with you before it starts.
Is RAG development right for you?
- 01
Your team or customers ask the same questions of documents that already exist.
- 02
Answers must cite a source and respect who is allowed to see what.
- 03
Your knowledge changes often, so retraining a model is not practical.
Not sure it fits? A free assessment will tell you
# Service manifest service: RAG Development category: AI & Automation deliverables: • Knowledge ingestion & chunking pipeline • Hybrid search & reranking • Permission aware, cited answers • Retrieval & answer evaluation stack: • Retrieval Augmented Generation (RAG) • Vector Databases • Hybrid Search (BM25 + embeddings) • Reranking • Embeddings • pgvector phases: 4 next_step: free assessment
RAG development questions, answered.
What is RAG?
Retrieval augmented generation (RAG) is a pattern where an application first searches your own content for passages relevant to a question, then gives those passages to a large language model to write the answer. The model answers from current, specific sources instead of its training data, and the answer can cite them.
Should we use RAG or fine tuning?
Use RAG when the problem is missing or changing knowledge: policies, product docs, tickets, records. Use fine tuning when the problem is behaviour: a fixed format, tone or narrow task the model gets wrong even with good context. Many production systems use RAG first and fine tune later, if evals show a need.
Which vector database do you use?
It depends on scale and what you already run. For many projects PostgreSQL with pgvector is enough and keeps data in one place; larger or more specialised workloads may justify a dedicated vector store. We decide by measuring retrieval quality and cost on your data.
How do you stop the assistant from making things up?
Answers are built only from retrieved passages, every answer cites its sources, the assistant is told to say it does not know when sources are silent, and an evaluation set checks faithfulness on every change. That reduces made up answers; no method removes them entirely, so we also measure and monitor them.
From the Caudify Journal on RAG development.
- AI · 6 min readRAG vs fine-tuning: how to choose for your LLM applicationRAG gives a model the right knowledge at the moment it answers. Fine-tuning changes how the model behaves. Most teams need the first, some need both, and the way to decide is an evaluation set, not a debate.
- Security · 7 min readOWASP Top 10 for LLM applications (2025): the risks and how to fix themThe OWASP Top 10 for web apps doesn't cover what goes wrong when a language model sits in your product. The LLM list does. Here is each risk in plain English, with the controls that actually reduce it.
Start with a free assessment.
Tell us what you're building, improving or automating. We'll come back with an honest view of where RAG development fits and the fastest path to production.