Evaluation & Managed Operations
A model that works correctly on launch day can fail silently 90 days later. We notice first, not your customer.
AI systems fail differently
They don’t crash. They slowly get worse. There’s no alarm, just slowly declining quality that someone eventually sees in the business results.
The data shifts, a model provider updates in the background, a changed prompt template degrades the output in an edge case no one tests.
The only thing that helps is measurement. Systematic, automated, continuous.
Evaluation
Five measurements that make silent quality decay visible
Quality checks
Automated assessment of correctness, evidence (are the statements backed by the stored sources), consistency and meeting the intended goal.
Hallucination detection
Outputs are checked against the source data. Unsubstantiated statements are flagged or blocked.
Regression tests
Every change to model, configuration or instructions runs against a fixed test set before going live.
Red teaming
Targeted attacks on the system: prompt injection, bypassing guardrails, data exfiltration via tools.
Cost audit
Where costs arise, which use case pays for itself and which doesn’t.
Managed Operations
Three service tiers with defined response times
| Monitor | Operate | Evolve | |
|---|---|---|---|
| Monitoring & alerting | |||
| P1 response time | 8 h | 4 h | 2 h |
| Incident response | Notify | Resolve | Resolve |
| Quality-decay check | monthly | weekly | continuous |
| Cost reporting | |||
| Full evaluation run | quarterly | monthly | continuous |
| Further development | Quota |
Honestly: we’re a small, experienced team, not a night-shift factory. That’s why we only take on systems whose operation we can genuinely support. Escalation, cover and handover are put in writing before day one, so that no response-time promise hangs on a single person.
Project example
Four hours instead of eleven weeks to detection
Quality decay after 90 days
A client ran a support agent that worked reliably at first. After nearly three months complaints piled up without any error showing in the log. The cause: the model provider had updated in the background, and an edge case in the instructions that no one had tested led to invented prices. We set up a quality check that tests daily against a fixed test set and measures whether the statements are backed by the stored sources, plus regression tests that run before every change. The next silent model change was detected after four hours, not eleven weeks.
Silent model change detected in hours, not weeks
Answer quality measured and evidenced continuously
No more invented information in operation
Seven more building blocks to a production system
AI Readiness Assessment
Inventory, vulnerability analysis and roadmap to production. In 10 working days.
Learn morePrivate AI & On-Premises
Run AI inside your own building: local models, your own hardware, full data sovereignty.
Learn moreAI Security & Compliance
Hardening, ISO-oriented evidence and data protection for the AI operation.
Learn moreProduction Hardening
Make the existing pilot fit for operation: error handling, edge cases, human-in-the-loop.
Learn moreAgent Governance & Guardrails
Permissions, tool access, approval workflows and complete audit logs.
Learn moreAI Infrastructure & Deployment
Cloud, data pipelines, version control and deployment with rollback.
Learn moreEnterprise System Integration
Connect agents to ERP, CRM and legacy. With permission model and rollback.
Learn moreSystem in production?
We take on monitoring, evaluation and incident response, so that silent quality decay surfaces before your customer sees it.