Data & AIAI that is measured before it is trusted.
Models, agents and extraction pipelines that go live with a measured baseline, an evaluation set and a path to a person for the hard cases.
What we deliver in Data & AI
Open a capability to see what it covers, where to start and the work behind it.
Agents are worth their cost where a process needs judgement across several systems and the rules would never hold. We scope them narrowly, give them a tool surface they cannot exceed, and log every action they take.
Ways to start
Named, fixed-scope engagements. Every one has a duration, a named deliverable and an exit, and you keep what we build whether or not you continue.
Start here
AI Readiness Assessment
Three weeks to separate the processes where AI changes the economics from the ones where it is an expensive way to run a rule.
- Duration
- 3 weeks
- What you keep
- You leave with a defensible position for the board, built or not.
Pilot
Agent Pilot: one process to production
Six weeks, one process, an agent that actually goes live behind guardrails, with the evaluation harness to prove it works.
- Duration
- 6 weeks
- What you keep
- The harness and the runbook stay with your team.
Bars drawn to scale: one block is one week.
Case studies
Accounting & Bookkeeping97%of documents need no correctionAI Invoice Processing AutomationAccounting firm · Bulgaria · 10-15 staff, books kept for 100+ client companiesRead the case
Construction & Infrastructure< 1 dayProjectedmanagement reporting, down from 30ERM Platform for Road ConstructionRoad-construction group · Bulgaria · ~300 staff, €100M+ revenue, own asphalt, concrete and fuel sitesRead the case
Construction & Infrastructure14%of reviewed staff flagged for clocking fraudTimesheet Fraud Detection for a Metro Construction ConsortiumConstruction consortium · Europe · metro line build, 1,200+ people through its site gatesRead the case
What we build on
The platforms behind the practice, and the products we hold experience in.
Explore all technologies
How we see it
We treat AI as an engineering problem, not a demo. Every model, agent or extraction pipeline we put into a process arrives with a measured baseline, an evaluation set and a defined escalation path for the cases it should not decide alone.
That discipline is what separates an AI project that survives its first audit from one that quietly gets switched off. We will tell you when a rules engine or a well-placed query would be cheaper than a model.
Tell us which process hurts.
Talk to a partner