Structure
Who does what? And who knows about it?
Every department has its own workflows. There is no map, no standard, no handover. This is the next generation of data silos, being built right now.
You brought AI into your organisation. We bring organisation into your AI.
From process analysis to sovereign AI operations. Independent, data-driven, compliant.
Sounds familiar?
Every team builds its own workflows, its own tools, its own prompts. None of it is documented.
It all rests on one or two people. When they leave, the knowledge leaves with them.
Nobody really knows what your AI costs, what it delivers, or whether it is compliant.
Every month brings new models, new tools and new features. Nobody in the building is keeping track.
What is your biggest problem with AI?
Data
Does what you need even exist yet?
The use case is clear, the data for it sits in three systems. But without a clean data position, every prototype stays a demo.
Spend
What is AI costing you right now?
The bill climbs every month and nobody can say why. Three teams use the same model for completely different things. It is billed as one number on the credit card.
Compliance
Who answers when an auditor asks?
The EU AI Act applies. Which system falls into which risk class, and which model made which decision: the answer is in no log.
The path: four stages, one principle. Measure first, then decide.
- 1
Readiness Check
Where do you stand on AI today? Full inventory, EU AI Act risk classification, potential map across all departments. It ends with a roadmap you can actually hold yourself to.
- 2
Analysis & baseline
We measure the status quo: time, cost, quality per process. Then the cost-benefit case and the decision between local AI, European inference and a frontier model. We only move on when the numbers convince.
- 3
Prototype with kill criteria
Real process, real data. Our evaluation framework compares models on efficiency, speed, cost and accuracy. It ends with a go or a no-go. “This process is not worth it” is a legitimate outcome here, not a sales failure.
- 4
Implementation & operations
Production systems with cost tracking, tracing and audit trails built in. Cost optimisation through routing, caching and context management. Tokenometrics dashboards. Handover to your team.
- 5
After that: you become an AI-native champion
Training, playbooks and runbooks stay with you. We keep you current with model re-evaluations, compliance audits and process checks. On request and as needed, not out of dependency.
- 1
Readiness Check
Where do you stand on AI today? Full inventory, EU AI Act risk classification, potential map across all departments. It ends with a roadmap you can actually hold yourself to.
- 2
Analysis & baseline
We measure the status quo: time, cost, quality per process. Then the cost-benefit case and the decision between local AI, European inference and a frontier model. We only move on when the numbers convince.
- 3
Prototype with kill criteria
Real process, real data. Our evaluation framework compares models on efficiency, speed, cost and accuracy. It ends with a go or a no-go. “This process is not worth it” is a legitimate outcome here, not a sales failure.
- 4
Implementation & operations
Production systems with cost tracking, tracing and audit trails built in. Cost optimisation through routing, caching and context management. Tokenometrics dashboards. Handover to your team.
- 5
After that: you become an AI-native champion
Training, playbooks and runbooks stay with you. We keep you current with model re-evaluations, compliance audits and process checks. On request and as needed, not out of dependency.
Your data. Your infrastructure. Your decision.
Our recommendation is always: inference in Europe, with providers headquartered and hosting in the EU. Where a use case demonstrably needs a frontier model, we make the trade-off transparent, with a zero-data-retention agreement and a documented decision. And where on-premise AI makes sense, we run the numbers on hardware, setup and operations, honestly.
- Zero Data Retention
- EU inference
- EU AI Act compliant
- What matters to you?
The EU AI Act is not a hurdle. It is the blueprint.
Risk classification, human oversight, traceability, audit trails: what the AI Act demands is exactly what well-built AI systems need anyway.
So we think about compliance and build it at the very start, not at the end. Documentation, logging and oversight are part of the architecture from day one.
Pseudonymisation before data reaches the model. Role-based filtering before results reach people. Need-to-know, enforced by the system rather than wished for by policy.
The result: systems you can trust. And a full answer to auditors at any time.
We don't talk about it. We have built it.
- projects delivered
- 50+projects delivered
- products built in-house
- 9products built in-house
We come from projects where mistakes are expensive.
Our Insights.
From the assessments
What the bill does not say
Your model invoice knows tokens and models. It knows nothing about workflows, teams or outcomes. Which is why it cannot answer the one question the board asks.
From practice
The first meter hung on our own AI
Tokenometrics did not come out of a workshop. It exists because we could not explain our own invoice, and then found the same gap at every client we looked at.
From the assessments
AI and vibe coding: what of it survives production
Five claims about AI in software development, held against what we actually use. Including where the answer is uncomfortable.
Ready for numbers instead of gut feeling?
Start with an assessment: fixed price, clear outcome, no obligation. You get the current status of your AI, your compliance gaps and your biggest opportunities. All at a glance. We'll get back to you within 24 hours.