Discovery
A short, structured look at the systems, people and constraints already in play.
Service
Generative AI
Retrieval over your own documents, support and sales copilots, document extraction, with evaluation and guardrails from day one.
The corpus decides the outcome
Internal copilots usually fail for an unglamorous reason. The documents are out of date, duplicated across systems, or missing the policy that actually governs the question. No amount of prompt work fixes a corpus that disagrees with itself.
So we start there.
What we build
Retrieval over your own documents, support and sales copilots, and document extraction. Always with citations back to the source, so a reader can check rather than trust.
Access control is part of the retrieval layer, not a filter bolted on afterwards. A system that returns a document the reader may not see is a data breach with a chat interface.
Evaluation from day one
Fifty real questions from last quarter, with answers reviewed by someone who knows the domain, run on every change. Without that you are shipping on impressions.
Governance comes with it
Every system is risk-tiered at design time and ships with a model card. Your data is never used to train shared models and never leaves the region you specify.
What we do
The activities this covers
Design
An approach written in plain language, reviewed with your team before anyone writes code.
Delivery
Built in your cloud and your repository, demonstrated weekly against working software.
Validation
Tests and measurement against the success criteria agreed at the start.
Documentation
Runbooks and decision records written during the work, not reconstructed after.
Handover
Training for the people who will own it, scoped from the first week.
What you get
Deliverables, not slideware
- A repository you ownTransformations, infrastructure and CI configuration, commented and reviewed.
- Test suiteTests running on schedule, with alerts routed to a channel your team reads.
- Cost baselineWhat the work costs to run per team, with a monthly budget alert.
- Architecture diagramCurrent state and target state, kept up to date through the engagement.
- RunbooksWhat to do when something fails at 3am, written for someone who did not build it.
- Two weeks of trainingHands-on sessions with the analysts and engineers who take it over.
Related work
Where this service led the engagement
Ridgeline Bank · Financial services
A month-end close that took eleven days
Finance rebuilt the same figures by hand every cycle. We modelled the ledger in the warehouse, defined forty metrics once, and automated the reconciliation.
11 → 3 days to close
Hollis Logistics · Logistics
A warehouse bill that doubled in a year
Nobody could see which queries cost what. We added cost attribution per team, rewrote the twelve worst models, and set a monthly budget alert.
-46% platform cost
Questions
Questions about generative ai
Still unsure? Call (216) 123 45678 and ask for whoever is on the desk.
Ask a questionNo. Most engagements improve what you already run. We only recommend migration when the current platform cannot meet a requirement you have already committed to.
You do. Everything is written in your repository, under your cloud account, on open tooling. There is nothing to transfer at the end because it was never ours.
That is the normal arrangement. Your engineers review our pull requests and we review theirs. Teams that stay out of the work rarely take it over successfully.
First working increment usually lands inside three weeks. We demo weekly against running software, not plans.
Tell us which number you cannot trust
Send one paragraph about the report that always starts an argument. A data lead, not a salesperson, replies within one business day.