RAG development
AI knowledge systems grounded in your own data
Systems that answer from the company information you trust: policies, procedures, documents and past tickets. Each answer carries its source, respects who may see what, and says so when the answer isn't there. RAG (retrieval-augmented generation) is the technique underneath; the outcome is people finding the right answer without asking around.
Definition
What RAG actually solves
A model on its own answers from what it was trained on. RAG makes it answer from what your business actually knows.
- 01
Finds the material
Searches your documents, policies and records by meaning, not just by matching keywords.
- 02
Answers from it
The model writes its answer from the passages it found, not from general knowledge.
- 03
Shows its sources
Each answer links to the documents it came from, so anyone can check it.
- 04
Admits the gaps
When your material doesn't hold the answer, it says so rather than producing a confident guess.
The value is not the model. It is the right answer, from the right document, reaching the person who needs it.
Fit
When RAG is the right tool, and when it isn't
Plenty of knowledge problems are solved more cheaply another way. The audit will say so.
Something simpler is enough when
- People know the exact document or term to search for
- The answer is a single field in one system, such as an order status
- The material is small and rarely changes
- The real problem is that nothing has been written down yet
RAG earns its place when
- Answers are spread across many documents, versions and formats
- People ask in their own words, not the document's terminology
- Each answer has to be traceable to a source someone can check
- Staff ask a colleague because finding it themselves is slower
Use cases
Where knowledge systems pay
Questions your team asks every day, answered from material you already have.
- Internal knowledge assistants: one place to ask how things are done, answered from your own documentation.
- Policy and SOP lookup: the current version of a policy or procedure, quoted with its source, not a half-remembered one.
- Support knowledge: answers from past tickets, product documentation and known fixes while the customer is waiting.
- Document retrieval: the contract, report or clause that answers the question, found across folders and systems.
- Research across internal data: what the business already knows on a topic, gathered with every source listed.
- Knowledge access with permissions: each person gets answers only from the material they are allowed to see.
How we build the knowledge layer
The free AI Ops Audit starts with the questions people actually ask and where the answers live today. We agree which sources are in scope, how they stay current, who may see what, and how we will measure whether answers are right. Scope and price are fixed before anything is built.
30 minutes on the questions your team asks most. You keep the written plan either way.
Controls
Sources, permissions, freshness and citations
How we build knowledge systems, so an answer can be trusted and checked.
Controlled sources
Only the sources you approve are indexed. Nothing is answered from the open web unless you choose it.
Permissions respected
Answers draw only on documents the person asking is allowed to see, mirroring the access rules you set.
Kept current
Sources are re-indexed on a schedule or when they change, so a superseded policy stops being quoted.
Citations on every answer
Every answer links back to the passage it came from.
Reliability
Evaluation, not optimism
No retrieval system is right every time. The work is knowing how often, and catching it when it isn't.
Tested on your questions
Before launch, answers are checked against real questions whose correct answers your team has confirmed.
Retested on every change
The same set runs again when sources, prompts or the model change, so quality is compared, not assumed.
Weak matches go to people
Where nothing relevant enough is found, it says so or routes the question to a person.
Feedback that sticks
People can flag a wrong answer, and flagged answers feed the next round of fixes.
Evidence
Relevant systems experience
Built at Koenig Solutions
Prior professional experience. Our founder built these systems as an employee at Koenig Solutions, who own them.
A retrieval layer at library scale
A retrieval layer across 8,000+ courses that grounded 350,000+ generated questions, each quality-checked before publication. Every question was drawn from the course material it belonged to rather than from a model's general knowledge.
What you own
What you are actually buying
It won't break quietly
Error handling, retries and alerting on every workflow. When an upstream API changes, we know before you do — not when someone notices the invoices stopped going out on Tuesday.
You'll own it
Built in your accounts, on your credentials, documented. If you stop working with us, nothing stops working.
Related
Related services
Custom AI beyond retrieval: generation at volume and systems built into daily operations.
When an answer should lead to action: a system that decides the next step and carries it out within limits.
Recurring, rules-based processes that should simply run on a schedule.
Questions
About RAG development
What does “grounded in your data” actually mean?
The system retrieves from your own material and answers from what it found, with the source attached. Where the answer is not in your material it says so, rather than producing a confident guess. That is the difference between something you can put in front of staff and something that demos well.
Will it ever give a wrong answer?
Sometimes, yes. Grounding and citations make wrong answers rarer and quick to check, and evaluation tells you how often it happens on your own questions. Where the stakes are high, an answer can go to a person to confirm before anyone acts on it.
How is this different from a chatbot or our existing search?
Search returns documents and leaves the reading to you. A general chatbot answers from what it was trained on. A knowledge system reads your documents and answers from them, with the source attached, so the person asking gets the answer and the evidence together.
Is this only for large organisations?
No. Enterprise RAG solutions are built for very large document estates, but the same approach pays off for any business whose answers are spread across more documents than people can remember. The audit tells you whether your volume justifies it.
Where does our data go?
It stays in accounts you own. The index, the hosting and the model provider are set up in your name and billed to you directly, and you choose a provider whose data terms you are comfortable with.
Find out what you shouldn't be doing.
30 minutes, no pitch, and a written plan you keep either way.
Get your free AI Ops Audit