AI Reliability & Evaluation

Evaluate an existing AI workflow with representative test cases, answer-quality checks, failure analysis, model comparisons, and practical recommendations for improving reliability, latency, and cost.

AI EvaluationReliabilityRegression Tests
Starting from $1,000Optional recurring evaluation and monitoring

The problem

An AI prototype can look impressive in a demo while failing unpredictably on real questions. Without repeatable evaluation, prompt changes and model upgrades become guesswork and old failures quietly return.

Who it's for

Companies that already have an AI assistant, RAG system, document workflow, or AI-powered feature and need evidence about where it works, where it fails, and what should be improved next.

The outcome

A repeatable view of AI quality: representative tests, documented failure patterns, measurable before-and-after comparisons, and a prioritised improvement plan instead of subjective demo impressions.

What STYD delivers

STYD builds or refines a representative evaluation set from the client's real use cases, runs controlled tests against the current system, categorises failure modes, compares relevant prompt/model/retrieval changes where useful, and delivers findings plus recommended fixes and regression checks.

Pricing

Starting at: Starting from $1,000
Monthly option: Optional recurring evaluation and monitoring

Typical starting point — final scope and price are confirmed after a short review of your setup.

Available after scoping

This service is available — tell us about your setup and we'll confirm scope, hosting, and pricing.

Available as project-based setup with optional STYD hosting, maintenance, and monthly support.

FAQ

Can you guarantee the AI will never hallucinate?

No. The goal is to measure failures, reduce them, add safer behaviour where needed, and make future changes testable rather than pretending uncertainty can be removed completely.

Do we need an existing test set?

No. STYD can build a practical evaluation set from real examples, known questions, edge cases, and expected behaviour with your team's input.

Can you compare different models?

Yes, where model choice is material. Comparisons can include answer quality, consistency, latency, and usage cost on the agreed evaluation set.

Is fixing the system included?

The evaluation identifies and validates improvements. Larger architecture or application changes are scoped separately unless explicitly included.

Interested in AI Reliability & Evaluation?

Tell us about your setup and we'll confirm fit, scope, and pricing.