AI infrastructure
The AI layer sits on top of your analytics. I build it on a foundation I control, and I stay on retainer to keep it trustworthy as the numbers underneath it move.
In your environment. I work inside your cloud and your warehouse. The AI layer runs against your own infrastructure, so financial data never leaves systems you own. Where an AI model is used it runs against your environment, on a provider in your region configured for no training and no retention, agreed before access.
Your region, your rules. Your data stays in the region you choose, the EU, the UK, or elsewhere, under the regime that applies to you, GDPR or UK GDPR. I work region-pinned to where you operate and treat financial data as sensitive from day one.
Agreed before access. An NDA and a data processing agreement are signed before any access is granted, scoped to the engagement and revoked when it ends.
Why it works
Point a capable model at a stack where one metric means three different things and it will answer fluently, confidently, and with a number nobody can defend. The fix lives underneath: a governed layer, owned by the same person who built the warehouse.
This is the top floor of the same building. The agent reads from the definitions you have already signed off, so its answers reconcile with the board pack.
Assisted reporting. Board commentary and recurring write-ups drafted from your actuals, tied back to the model. You review and ship.
Operational agents. Nightly reconciliation and anomaly alerts that fire before the meeting, built against the same definitions.
Living documentation. A copilot over the semantic layer, so a metric means one thing and a new hire onboards in days.
InteractiveSee it run
Ask the agent. Flip the switch to pull the layer out.
Enter the BI · agent
Sample data, illustrative only.
An AI layer is not a delivery you walk away from. The numbers underneath it change, definitions get revised, models get deprecated. Maintenance is the job.
It stays tied to the definitions
When a KPI definition changes, the agent and the documentation change with it. The answer never quietly drifts away from the board pack.
It is checked, not assumed
Outputs are evaluated against known answers on a schedule, so a regression surfaces in a check rather than in a meeting.
It survives the model moving
Models get replaced. The layer is built so swapping the model underneath does not mean rebuilding the trust on top of it.
Three AI builds, each walked through end to end: the problem, the architecture, what it refuses to do, and the numbers.
A day and a half of manual assembly, and the prose sometimes disagreed with the model. An assisted-reporting layer drafts it now; a person reviews and sends.
Orders, payments and fulfilment lived in four systems that quietly disagreed. A nightly agent reconciles them and posts only the exceptions to Slack.
Three teams quoted three different completion rates. A copilot over the semantic layer answers what a metric means, grounded in the live model.
Tell me what your numbers do today and where an agent would actually help. I'll figure out scope on a 30-min call before either of us commits to anything.
Talk about the AI layer