AI Services
AI Model Evaluation
Evaluation workflows that measure model quality, catch regressions, and show where your AI system fails before users do.
What's Getting in the Way
Many teams ship AI features without knowing whether the model is accurate, stable, safe, or getting worse after changes. Manual spot checks do not scale. Without evals, every prompt change, model switch, retrieval update, or data change becomes a guess.
- Teams cannot tell whether a new model or prompt is actually better
- Hallucinations, bad extractions, and weak answers are discovered by users after launch
- There is no regression test suite for AI behavior
- Quality, cost, latency, and reliability are tracked separately or not at all
What You Get
Evaluation Dataset
We create or refine a representative test set of real user inputs, expected behaviors, and difficult edge cases.
Scoring Rubrics
We define what good output means for your use case, including accuracy, completeness, format, tone, citation quality, and safety.
Automated Eval Workflow
We build a repeatable evaluation process that can compare prompts, models, retrieval settings, and product changes.
Failure Analysis
We identify the patterns behind weak outputs and recommend fixes in prompts, data, retrieval, guardrails, or product flow.
Reporting and Handover
You get a clear evaluation report and the workflow needed to keep measuring quality after launch.
The Process
Define Quality
We turn fuzzy expectations into measurable criteria tied to the user task and business risk.
Build the Test Set
We collect examples, edge cases, expected outputs, and labels needed for repeatable evaluation.
Run Evaluations
We compare current behavior across prompts, models, data, retrieval settings, and common user paths.
Fix and Monitor
We turn the findings into practical changes and hand over the evaluation process.
Pricing
$3K-$9K
AI Model Evaluation
Timeline: 2-4 weeks
Final price depends on scope. We confirm exact pricing after a free scoping call.
Evaluate your AI systemQuestions About This Service
What can you evaluate?
Chatbots, RAG systems, extraction workflows, summarizers, classifiers, agents, voice transcripts, and LLM product features.
Do evals require labelled data?
Labelled examples help, but we can start by building a focused evaluation dataset from real use cases.
Can you compare different models?
Yes. We can compare models on quality, latency, cost, reliability, and failure patterns.
Is this only for launched products?
No. Evaluation is useful before launch, during development, and after deployment.
Can this connect to AgentMetrics?
Yes. For agent or LLM applications, evaluation and observability can feed into an ongoing quality loop.
Often Combined With
Let's build it.
Tell us your context. We'll scope it in one call.