01LLM INTEGRATION · RAG

An LLM that answers from your data

RAG, document search, and answers with citations. We connect an LLM to your knowledge: it answers from your data and shows the source — instead of making things up.

Query
question · intent
Embedding
query vector
Search
vector database
Rerank
relevance
Context
your documents
LLM
answer · sources
Eval
accuracy · source
Answers from your data
02WHERE IT FITS

Answers from your data in your product

One RAG setup — for many tasks. The source and the interface change; the accuracy and the source citation stay.

/01

Document search

Ask in natural language — get an answer from your files, wikis, and databases, with a source citation.

/02

Support

An assistant answers customers and agents from your knowledge base, not with vague phrases.

/03

In-product assistant

An in-UI copilot: hints, drafts, and explanations grounded in the user’s data.

/04

Knowledge base

Internal search for your team: onboarding, policies, project answers in seconds.

03QUALITY IN NUMBERS

Quality you can see

We measure with evals on a set of real questions. Relevance, the share with citations, and cost — on a dashboard you can access.

01

Answer relevance by eval

02

Answers with a source citation

03

Answer with retrieval over your data

04

Cost per query — cache and routing

04DATA SOURCES

Data around the product

The product at the centre, sources around it. We connect what you already have: files, wikis, CRM and databases. Data stays with you — answers rely on the current version.

Documents
files · contracts · PDF
Knowledge base
wiki · policies
CRM · tickets
customers · threads
Databases · API
product data
Your product
05FAQ

About LLM integration the questions we usually get

RAG means the model retrieves the relevant parts of your documents before answering, then answers from them rather than from memory. Answers stay grounded in your data, come with a source citation, and refresh the moment you update the documents.