Fixed scope
One outcome, one price, one date. Best when the requirements are settled and the risk is known.
S3 — 4–8 weeks
AI & automation integration
Retrieval, document processing and copilots wired into existing workflows, evaluated against your data rather than a demo dataset.
What you get
Deliverables you keep.
LLM APIsRAGPythonFastAPI
- Use-case scoring before we build
- Evaluation set from your own documents
- Guardrails, logging and fallbacks
- Cost-per-request model
A good fit for
Who this is built for.
Teams buried in documents — invoices, claims, contracts, reports
Support desks answering the same questions from a knowledge base
Products that want a copilot that actually knows their data
The approach
How an engagement actually runs.
Every step ends in something you can see — a document, a deployed environment or working software.
Score
Rank candidate use cases by value, risk and data readiness.
Evaluate
Build an eval set from your real documents before writing features.
Integrate
Wire the model into the workflow people already use.
Operate
Monitor quality, cost and drift — with human fallbacks.
Engagement models
Ways to work together.
Dedicated squad
Two to five senior engineers embedded in your rituals, with a lead who owns delivery.
Support retainer
Monitoring, fixes and small features on a standing budget after go-live.
Questions
Before you ask.
Which models do you use?
Whichever scores best on your eval set at an acceptable cost. We design so the model can be swapped without rewriting the product.
What about data privacy?
We default to providers and deployments that do not train on your data, and can run models inside your own cloud tenancy.
Start a project
Let's build something that stays up.
Send a short brief and we will come back with scope, timeline and a straight answer on whether we are the right team.