AI that answers from your data, not guesses.
Internal knowledge-base assistants, RAG systems, and small agentic workflows that retrieve from your documents, databases, and approved sources.
Deliverables
What you get, in writing.
Knowledge-base assistant
Ask in plain language; answers cite your docs, wikis, or databases. Web UI, Slack, Teams, or API.
Private / on-prem RAG
Local embeddings, self-hosted vector stores, and approved model access for data that can't leave your network.
Agentic workflows
Multi-step tasks like lookup, summarising, and report drafting, with logged actions and a hard step cap.
Ingestion pipeline
Parse, chunk, embed, and index PDFs, docs, spreadsheets, web pages, and databases.
Where your team works
Slack, Teams, a web app, or an endpoint, so it fits the workflow you already have.
Evaluation harness
A test set of real questions with expected sources, plus a baseline accuracy report.
3wk
Typical MVP build
100%
Fixed scope, fixed price
30d
Support after launch
Tools we reach for · documented, replaceable, no lock-in
Process
How it runs.
Four steps, agreed before any code is written: what the system should do, what it should refuse, and how success gets measured.
- 01
Use case & sources
Users, data sources, success criteria, and what the assistant must refuse, all written down.
- 02
Prototype early
Retrieval flow, model access, and UI shape. You click through a working prototype in week one.
- 03
Build & evaluate
Tested against real questions and failure cases, with eval scores at every milestone.
- 04
Deploy & handoff
On your infrastructure where possible, with docs, environment notes, and 30 days of support.
Need an AI MVP you can actually test?
Tell us the use case, the data, and who will use it. You get a fixed scope and price within 24 hours.