Quality & evaluation
We define what “good enough” means for your actual process and measure it against realistic cases.
- Task success and correctness
- Groundedness and citations
- Regression tests and edge cases
AI Production Readiness & Reliability Sprint
Is your AI system ready for production?
I evaluate RAG and agentic AI systems systematically for quality, failure behaviour, security, cost and operability.
You do not get another demo or an abstract governance presentation. You get realistic tests, a traceable failure analysis and a prioritised fix and go-live plan.
Independent · stack-neutral · remote across DACH
Outcome after 2–4 weeks
Measurable quality and release criteria
Prioritised root causes instead of vague symptoms
Concrete fix, guardrail and go-live plan
Repeatable tests for future changes
From pilot to production
A convincing demo is not yet a reliable system.
LLM applications can look finished on the happy path. Expensive problems emerge at the edges: ambiguous cases, changing data, incorrect tool calls, weak permission boundaries and outputs nobody can evaluate consistently.
Warning signs
- The team cannot state reliably how well the system performs.
- Hallucinations or incorrect sources appear only sporadically.
- An agent uses tools or data differently than expected.
- Model or prompt changes create unnoticed regressions.
- Latency and cost fluctuate without a clear cause.
- Traceable release criteria are missing for go-live.
Evaluation scope
Test more than answers – evaluate the whole system.
The exact scope depends on the use case. The sprint combines domain quality, agent behaviour, technical reliability and effective security boundaries.
Agents & tool calls
For agents, the final result is not enough. The steps and actions leading there matter as well.
- Tool selection and parameters
- Trajectory, abort and retry
- Human approval and escalation
Reliability & operations
We show whether the application remains stable, observable and economical under realistic conditions.
- Latency and cost per task
- Logging and failure analysis
- Fallbacks and recovery
Guardrails & security
Controls are placed where data, tools and autonomous actions create actual risk.
- Permissions and least privilege
- Prompt injection and data leakage
- Limits, approvals and kill switches
Process
A bounded sprint instead of open-ended analysis.
We start with your existing system and real business tasks. Every step produces a result your team can use.
01
Clarify goals, risks and release criteria
We isolate the critical workflow and translate business expectations into measurable quality, security and operational goals.
02
Build the test set and baseline
Real cases become a representative evaluation set. The current version establishes the baseline for quality, latency and cost.
03
Investigate failures and attack paths
We test edge cases, regressions, tool calls, permissions and relevant prompt-injection scenarios, tracing causes rather than symptoms.
04
Hand over the fix and go-live plan
You receive prioritised actions, guardrail recommendations, residual risks and clear criteria for fixing, piloting, launching or stopping.
Deliverables
What remains with your company after the sprint.
The results are structured so your team or implementation partner can continue immediately – without creating a new dependency.
- Documented quality and risk criteria
- Reusable evaluation set with representative test cases
- Baseline for quality, latency, cost and critical failures
- Failure map with root cause, impact and reproduction path
- Guardrail and observability recommendations matched to the architecture
- Prioritised action plan with go-live decision points
Vendor-neutral and sovereign
The sprint is not tied to a particular model cloud or observability platform. Open-source, EU-cloud and on-premise stacks are considered equally.
Fit
For existing systems with a concrete production problem.
The greatest leverage comes when a pilot, RAG system, copilot or agent already exists and a sound decision is due.
The sprint fits when …
- an AI system is approaching go-live or rollout
- quality, hallucinations or tool calls are unclear
- a pilot works in the demo but fluctuates in everyday use
- model, provider or architecture changes need to be de-risked
The sprint does not fit when …
- no concrete use case or prototype exists yet
- only a general AI workshop is needed
- a legal conformity assessment is expected
- a complete enterprise rollout without a bounded scope is requested
Frequently asked questions
Questions about evaluating production AI systems.
Which systems can be evaluated?
The sprint suits RAG and knowledge systems, internal copilots, document-based workflows and agents with tool or API access. Scope is limited to one commercially relevant workflow.
Does the system need to be live already?
No. The evaluation is most useful before go-live, for a stalled pilot or ahead of a substantial model, provider or architecture change.
How is this different from a security audit?
The sprint combines domain quality, agent behaviour, reliability and selected AI-specific security risks. It does not replace a comprehensive penetration test or legal and regulatory assessment.
Do test data and results remain under our control?
Yes. Data access and the test environment are agreed in advance. If sensitive data must not be processed externally, the sprint can run in your existing EU, private-cloud or on-premise environment.
Next step
Replace gut feeling with defensible release criteria.
Briefly describe the system, its current state and the largest uncertainty. You receive an honest assessment of whether a 2–4 week sprint can be scoped effectively.