Knowledge work
An AI assistant that answers from your own documents
A search and question tool grounded in your contracts, policies and manuals - with citations, so answers can be checked.
An illustrative build, not a client case study. Timelines and scope are typical ranges - yours gets estimated properly after discovery.
The problem it solves
The answer is in a PDF somewhere on the shared drive. Finding it takes twenty minutes, and the person who knows where to look is the bottleneck for everyone else.
How we approach it
- 1
Start with a feasibility check on a sample of your real documents before anyone commits to a build.
- 2
Index the documents with proper chunking and metadata, because retrieval quality decides whether this works.
- 3
Make every answer cite the source passage, so nobody has to take it on trust.
- 4
Build an evaluation set from real questions and test every change against it.
What it includes
- Document ingestion and indexing pipeline
- Question answering with source citations
- An explicit 'I don't know' rather than a guess
- Access control matching your document permissions
- Evaluation harness and quality monitoring
- Cost and usage reporting
Also possible
Other things we build
Stack
Technologies we'd use
We choose the right tool for the job — not a fixed stack. Below are typical choices for this type of build.
Process
How this engagement typically runs
Six steps from discovery to a system your team can own. We adapt the depth of each step to your context.
- 01
Discovery
Feasibility check on 50-100 real documents — test retrieval quality, define citation format, and build evaluation set.
- 02
Architecture
Choose embedding model, chunking strategy, vector store, and LLM (API vs self-hosted) based on privacy/cost.
- 03
Design
Evaluation harness from real questions; guardrails (I don't know, citations, cost ceiling); access control model.
- 04
Build
Ingestion pipeline, retrieval + reranking, citation extraction, eval runs on every change — with full lineage.
- 05
Launch
Canary with power users, shadow traffic vs human answers, latency/cost/quality dashboards, explicit fallback.
- 06
Evolve
Drift detection, re-indexing schedule, A/B prompt testing, new document source onboarding, or clean handover.
FAQ
An AI assistant that answers from your own documents - common questions
Do our documents leave our infrastructure?
How accurate are the answers?
What document formats are supported?
How do you handle permissions?
What's the ongoing cost?
Have a project in mind?
Tell us what you are trying to build. You will get a straight answer on feasibility, timeline and cost - and if we are the wrong fit, we will say so.