Evaluation baseline
3–5 weeks
Build the first useful dataset, scoring system and review workflow around one product decision.
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Estma service / Evaluation systems
AI evaluation and observability
Turn quality from a subjective demo reaction into a repeatable operating system of datasets, evaluators, trace review and production signals.
Where this starts
A good fit when…
- AI output quality is discussed but not measured
- Prompt or model changes create unpredictable regressions
- Teams collect traces but do not know what to review
- A release needs defensible quality evidence
The operating problem
The difficult part is rarely the headline technology.
The engagement focuses on the surrounding system: boundaries, evidence, permissions, exceptions, people and the decisions the implementation must support.
- 01
Evaluation examples do not represent real traffic or failure modes
- 02
Scores exist without a decision threshold or owner
- 03
Observability tools are installed but not operationalised
- 04
Human review and automated evaluation disagree without a resolution path
What leaves the engagement
Concrete output, not advisory residue.
- 01Evaluation strategy tied to product decisions
- 02Representative datasets and failure taxonomy
- 03Deterministic, model-based and human evaluation workflows
- 04Trace instrumentation and dashboards, including Langfuse where appropriate
- 05Regression gates, review cadence, alerts and ownership
Ways to start
Choose the smallest engagement that resolves the next decision.
Observability implementation
2–4 weeks
Instrument traces, metadata, costs and failure signals so the system can be operated.
Evaluation operations
Monthly
Extend coverage, calibrate evaluators and turn production failures into regression tests.
What Estma needs from your team.
- Examples of good and bad output
- Access to prompts, traces or application code
- Someone accountable for product quality
- A representative test environment or traffic sample
Service questions
What teams usually ask.
Is this just setting up Langfuse?
No. A tool can collect traces, but it cannot decide what good means for your product. We connect the instrumentation to datasets, evaluators, review decisions and release gates.
Can evaluation work before production?
Yes. A pre-production baseline is often the fastest way to expose unclear requirements and risky assumptions before a larger build.
Start with context
Is this the work you need?
Describe the current system, the decision ahead and the constraint that is making progress difficult.