MCP servers over your model
Your governed metrics exposed as tools an AI assistant can actually call — so Claude or Copilot answers from the warehouse definition instead of guessing from a pasted export.
03Service
Analytics Intelligence
Applied AI and custom software on top of a governed data platform — shipped in weeks, owned by you.
Proprietary technology has never been this accessible. The work that needed a dev team and eighteen months now takes a competent operator and a few weeks — agents that do the manual step, MCP servers that let your own AI tools query governed data, internal software built for exactly one workflow. What hasn't changed is that all of it is worthless on data nobody trusts.
Custom internal software
Proprietary tech to Now yours
Software that used to mean a dev team and a year is a few weeks of work once the data layer exists — and the source sits in your repository.
When this is the right service
Your board is asking about AI and your data is finally in a state to answer.
The pilots your team ran last year didn't survive contact with production, because the model was reasoning over spreadsheets four people maintained by hand. With one governed source, the same ideas become ordinary engineering — and ordinary engineering now moves at a speed that changes what's worth building.
If the numbers are still disputed between departments, this isn't the next thing — the platform is, and we'll say so rather than sell you an agent that hallucinates from bad inputs.
What this looks like in practice
Agentic workflows
The recurring manual step, automated end to end with a human approving the output: variance commentary, exception chasing, invoice and document extraction, board-pack assembly.
Custom internal software
Software built for one workflow you run and nobody sells: configuration tools, pricing calculators, scenario screens, operator consoles. Cheap enough now to be disposable if the process changes.
Unstructured data made usable
Contracts, leases, service notes, email threads — parsed into the governed model so they can be reported on alongside everything else, not read one at a time.
Forecasting and unit economics
Demand, capacity, cash and margin per unit — simple methods first, backtested against your own history, with the error published rather than hidden.
Evaluations and guardrails
Every AI step gets a test set, a measured accuracy rate and a defined failure behavior before it goes near a decision. This is the part that separates production from a pilot.
How it runs
One workflow at a time, in production before the next is scoped.
The speed is the point. If something can't be prototyped inside a fortnight, it's the wrong first thing to build.
01Week 1
Pick the task
Which recurring manual step costs the most and tolerates a machine doing the first draft. Named person, named task, measurable today.
02Weeks 2–3
Prototype on real data
Working, not mocked, running against your governed model — and measured against how the person does it today.
03Weeks 4–8
Production with guardrails
Deployed in your tenant with evaluations, monitoring, an approval step and a documented failure mode. Then the task leaves someone's week.
Then the next one
Compound
Each workflow reuses the last one's plumbing, so the second is faster than the first. Continue as a project or under retainer.
Where this line stands
This is the newest of the four services and the one with the least published evidence. The platform work below is real and client-accepted. The AI and custom-software work built on it is either in flight or not yet cleared for publication.
Ask on a call and we'll walk you through what's running today, under NDA if it needs to be.
The foundation it builds on
19 → 1
System instances across 28 locations, in one governed model
72 nights
Consecutive automated rebuilds, no missed refresh and nobody watching it
13 days
From credentials to production for a newly acquired company's system
Config tool
Custom software built so a client's own team could onboard a new location
Every figure traces to a delivery artifact — acceptance documents, refresh logs, or a reconciliation the client ran themselves. Clients are blinded until they've signed off on being named.
What this isn't
Not another pilot
Nothing here ends in a demo and a slide. If it can't be put in front of the person who does the task today, we haven't finished.
Not a chatbot on your documents
Retrieval over a folder is a demo anyone can run in an afternoon. The value is in governed data, measured accuracy and a workflow that changes.
Not software we own
Source in your repository, running in your tenant, on models you contract for directly. No per-seat fee to us and nothing to renew.
Name the task nobody should still be doing by hand.
Thirty minutes, no deck. If your data isn't ready to support it, we'll tell you that instead of selling you a pilot.