CONTROLLED AI DELIVERY
Decide which AI is worth building, then build it under your own rules.
AI Economics does both halves. We qualify the use case and turn your supervisory obligations into rules an engineering team can enforce. MVP Fabric then builds the application under those exact rules. The first working PoC is delivered in three days.
CONTROL BRIEF
Illustrative
Ready for controlled validation
Customer operations assistant
VALUE
Defined
OWNER
Assigned
POLICY
Mapped
EVIDENCE
Captured
Prove the control model on one application. Scale it across the AI portfolio.
THE OPERATING PROBLEM
AI adoption is scaling faster than the organisation’s ability to control it.
As AI moves from isolated experiments into business processes, organisations face a different challenge: deciding what is worth scaling, what can safely reach production and how each application should operate once deployed.
01
Investment decisions come too late
Business value, production constraints and operating economics are often validated only after significant resources have already been committed.
02
Context breaks across delivery
Business requirements, architecture, security, engineering and governance evolve across different teams and systems, increasing rework and production risk.
03
Governance enters after the prototype
Ownership, permissions, autonomy, cost limits and evidence requirements are often defined too late in the delivery process.
04
Production creates a new control problem
Once deployed, AI introduces variable cost, probabilistic outcomes and autonomous actions that require continuous oversight.
HOW THE TWO HALVES FIT
Rules without enforcement are documents. Enforcement without rules has nothing to apply.
The advisory work produces the rules the platform needs as configuration. The platform produces the evidence the advisory work needs as proof. Each side can be purchased separately and stands on its own. Together, they compound.
TWO WAYS IN
Start where your problem is.
ADVISORY
You do not yet know what to build
For the people who have to defend the decision: risk, compliance, and whoever owns the AI agenda.
- Qualify one use case before funding it
- Translate the requirements of KNF Recommendation D, DORA and the EU AI Act into enforceable engineering rules
- Keep the framework even if you never buy the platform
MVP FABRIC
You know what to build, but not how to control it
For the people who have to ship it: engineering leadership, delivery and architecture.
- One verified specification from requirement to deployment
- A coding agent that cannot merge code breaking a hard rule
- A working PoC in three days from one qualified use case
THE CONTROL QUESTION
Can this AI action be executed, at this cost, under this policy, with this level of evidence?
AI agents propose. Authorised people approve at defined control points. Only approved changes are applied, and every decision becomes part of the evidence trail.
Today that enforcement reaches the engineering work itself. Extending it to runtime is the next step on our roadmap, not a capability we claim now.
EXPERIENCE BEHIND THE COMPANY
Built for complex enterprise environments.
AI Economics brings together experience in financial services, enterprise architecture, AI engineering, business transformation and strategic partnerships. Our team has worked on complex technology programmes and solutions across banking and international enterprise environments, including Credit Suisse and Deutsche Bank.
Team experience spanning 18+ years across core banking, payments, fraud, SEPA, FATCA, KYC and enterprise AI.
Ecosystem and partnerships
Connected to the enterprise AI ecosystem.
FinTech Poland
Member of Poland’s financial technology ecosystem.
AWS Partner Network
AI Economics participates in the AWS Partner Network.
FOUNDER & CEO
Sławomir Bugno
Strategy, economics and controlled AI adoption
Sławomir works with leadership teams on AI strategy, business value, governance and the controlled transition from pilot to production.
FIRST STEP
Bring one use case. Leave with a decision you can defend.
Tell us what you are trying to build, control or bring into production, and which half of the problem you are standing in. We reply with a focused next step, not a proposal deck.