Ensure your AI applications deliver accurate, reliable, and production-ready responses through automated and human evaluation.
From prompt-level scoring to full agent testing, we cover every layer where AI quality can break down.
Systematic scoring of prompt variants for clarity, consistency, and output quality across your use cases.
Expert-authored reference answers that define what a correct, production-ready response looks like.
Structured, weighted scoring criteria tailored to your domain so evaluation stays objective and repeatable.
Trained reviewers grade model outputs for correctness, tone, safety, and helpfulness at scale.
Head-to-head comparisons across models and providers, scored against your rubrics and golden set.
Automated and human-verified checks that flag fabricated facts, unsupported claims, and citation errors.
Catch quality drops before they ship with evaluation suites that run on every prompt or model change.
Retrieval precision, context relevance, and groundedness testing for your retrieval-augmented pipelines.
Multi-step task completion, tool-use accuracy, and reasoning trace evaluation for autonomous agents.
We evaluate and compare outputs across proprietary and open-source models, so you can choose with evidence, not guesswork.
Every engagement follows the same rigorous eight-stage process, so results stay consistent and comparable over time.
We map your use case, risk areas, and success criteria alongside your team.
Real and synthetic inputs are collected and cleaned to reflect production traffic.
A structured, versioned set of prompts covering edge cases and core scenarios.
Domain experts author reference-correct responses for every prompt in the set.
Weighted scoring criteria are defined for accuracy, safety, tone, and format.
Automated scoring runs your outputs against golden answers and rubrics.
Trained evaluators verify edge cases and grade what automation can't catch.
A clear, shareable report with scores, failure patterns, and recommendations.
Automated scoring for scale, human judgment for nuance, safety, and edge cases.
Prompt libraries and golden answers built around your product, not generic benchmarks.
Structured process and dedicated reviewers keep evaluation cycles measured in days.
Your prompts, data, and model outputs stay confidential under signed agreements.
Executive-ready benchmark reports built for stakeholders, not just engineers.
Identify where cheaper models perform just as well, without sacrificing quality.
Every engagement ends with concrete, reusable artifacts your team can act on immediately.
Model-by-model scores mapped to your rubrics and goals.
A breakdown of which prompts underperform and why.
Flagged fabrications with severity and frequency.
Reusable, versioned reference answers for future testing.
Documented scoring criteria you can apply internally.
Concrete next steps to close the gaps we found.
Everything you need to know about our AI evaluation and benchmarking services.
It's the structured process of testing an AI application's outputs against defined criteria — accuracy, safety, tone, and task completion — using a combination of automated scoring and human review, so you know how it performs before it reaches users.
We evaluate chatbots, RAG applications, AI agents, copilots, and any LLM-powered feature, across single-turn responses and multi-step workflows.
We benchmark across OpenAI, Claude, Gemini, Llama, DeepSeek, Qwen, Mistral, and self-hosted Ollama models, and can add others on request.
Automated evaluation scores outputs quickly against rubrics and golden answers at scale. Human evaluation catches nuance, tone, safety issues, and edge cases automation misses. We use both together for reliable results.
A golden dataset is a set of prompts paired with expert-verified correct answers. It becomes your ground truth, letting you measure any model or prompt change against a consistent standard over time.
We cross-check model outputs against source documents and golden answers, flag unsupported claims, and have human reviewers verify borderline cases before they're reported.
Yes. We test retrieval precision, context relevance, and whether generated answers are actually grounded in the retrieved content, not just fluent-sounding.
A typical benchmark cycle takes 1-3 weeks depending on dataset size and the number of models compared. Regression testing suites, once built, can run continuously.
All engagements start under NDA. Prompts, outputs, and datasets are handled under strict confidentiality and are never reused across clients.
Yes. Every benchmark report includes concrete recommendations, and we can help implement prompt fixes, rubric-based guardrails, or regression suites afterward.
Often, yes. Benchmarking frequently reveals that a smaller or cheaper model performs comparably for specific tasks, which can meaningfully cut inference costs.
Book a consultation and share your use case. We'll scope a pilot evaluation, usually against a sample dataset, so you can see the process and reporting before committing to a full engagement.