Automation & AI
AI Agent & RAG Integration
We connect conversational assistants to your real documentation, with the source cited in every answer and an evaluation set that measures quality before and after each change. Not a lucky demo.
0 spreadsheetsOperations left Excel · Ulai Pilates
Sound familiar?
- Your team answers the same questions about internal documentation every week.
- Someone retypes data from invoices, contracts or forms by hand.
- You tried a generic chatbot and it confidently answered things that were not true.
- You have years of documentation nobody consults because searching costs more than asking.
Why a generic chatbot fails inside a company
Because a language model without access to your documents answers from what it learned on the internet, and it does so in the same confident tone whether it is right or wrong. For a general question that is fine. For “how much notice does our lease require”, it is not: the correct answer is in a PDF the model has never seen.
That is not a model failure, it is an architecture failure. The step that retrieves the right passage before answering is missing.
What retrieving before answering means
Your documentation is split into fragments, turned into vectors and stored in an index. When someone asks, the system first finds the fragments that genuinely cover the topic, and only then asks the model to compose an answer using and citing them.
The practical effect is twofold: the answer rests on your material, and it is verifiable, because the reader can open the source and check.
How do you know it still works in six months?
Because there is an evaluation set. A bank of real questions with their expected answers, run on every change to the documentation, the model or the system. Without it, the only way to notice a degradation is a customer complaining, and by then it has been happening for weeks.
What you get
- Assistant wired to your real sources, citing the source in every answer.
- Vector index over your documentation, with ingestion that refreshes itself when material changes.
- Structured extraction from documents - invoices, contracts, forms - into the fields you actually use.
- Evaluation set with questions and expected answers, to measure before and after each change.
- Written boundaries - what the system answers, what it hands to a person, and what it never touches.
- Conversation dashboard flagging low-confidence answers for human review.
How we work
- 01
Source inventory
1 weekWhat documentation exists, where it lives, which version is current and who decides that. This phase drives final quality more than any other.
- 02
Evaluated prototype
2 to 3 weeksIngestion, vector index and a first assistant, measured against the evaluation set from day one. Whoever answers those questions today takes part.
- 03
Integration
2 to 4 weeksDeployed into the channel where your team or your customers already are, with boundaries, logging and handoff to a person.
Investment
Scoped quote
A costed proposal within 3–5 business days
Scope drives price. Send us the project: within one business day we tell you whether it fits, and the proposal — what is in, what is out and what it costs — arrives within three to five business days.
The range depends on how much documentation there is, how many sources need connecting and whether the agent only answers or also acts. The number comes out of the scoping call.
Proof of work
Frequently asked questions
How do you stop it from making things up?
Three things working together. First, the model can only answer from fragments retrieved out of your documentation, and it cites which ones - with no supporting passage, it says it does not know. Second, explicit boundaries on which topics are out of scope. Third, an evaluation set with expected answers that runs on every change, so a regression is caught before it reaches production.
Do my documents leave my company?
Only the fragments needed to answer each question, and only to the model provider we agree on. The vector index can live on your own infrastructure, and no configuration trains the model on your content.
What happens when the documentation changes?
Ingestion detects the change and reindexes only what moved. If a document is withdrawn, its fragments stop being retrievable in the same cycle - an assistant quoting last year policy is worse than no assistant.
Can it do more than answer?
Yes, and that is where to move slowly. An agent can open a ticket, book an appointment or write to your CRM, but each action with a side effect is defined one by one, logged, and gated behind human confirmation to begin with.
How do you measure whether it works?
The evaluation set covers answer quality, and two business numbers cover the rest - questions resolved without a human, and response time. If those two do not move, the project is not working however well the demos go.