Focused engagements for product teams, built around your stack, your users and your release risk. Start with an assessment, then build on what it finds.
We spend one to two weeks understanding your product, your team and how you release, then tell you exactly where quality is at risk and what to fix first.
The flows that make you money or lose you customers, and how well each one is actually covered today.
Contract gaps, error handling, validation and edge cases in the APIs your product and customers depend on.
What's automated, what isn't, what should be, and which existing tests are flaky or checking the wrong things.
Shared, stale or hand-made test data that makes tests slow, brittle or unrepresentative of production.
What actually runs before a release, what blocks it, and where a broken build can still reach users.
A first pass over any LLM, search or agent features, flagging where a deeper AI Quality Assessment is worth it.
A release risk map: what's most likely to break, how badly, and how you'd find out today.
A prioritised improvement plan your team can act on, with quick wins separated from longer investments.
For companies that have already built an AI feature. We test whichever of these your product ships and give you measured results, not impressions.
Accuracy, hallucination, consistency and prompt robustness. For RAG: is the answer grounded in the retrieved text, are citations correct, and does it say "I don't know" when it should?
We build a set of real queries with known correct answers and measure how often your search finds the right results, and how high it ranks them. We also cover typos, jargon and queries that return nothing, check for regressions when you change embedding models or indexes, and look for results leaking across customers or permissions.
Does the agent pick the right tool with the right arguments, finish multi-step tasks, recover when a tool fails, and ask before risky actions? Measured across repeated runs, not a single lucky one.
Tool names and descriptions models actually understand, argument validation, recoverable errors, OAuth scopes and permission boundaries, safe destructive tools, poisoned tool output, token-heavy payloads, and behaviour across clients such as Claude, ChatGPT and Cursor.
An eval suite wired into CI so every prompt, model or retrieval change is scored before release, with calibrated LLM-as-judge checks and cost and latency budgets.
For classic ML models: data validation, metrics beyond accuracy (precision, recall, AUC), bias analysis and explainability (SHAP, LIME).
You're about to launch an AI feature, agent or MCP integration and need to know how it behaves on real inputs.
You're switching models, rewriting prompts, or changing your embedding model or search stack and need to know what regresses.
Users complain about answer or search quality, but you have no systematic way to reproduce or measure it.
Enterprise customers or investors are asking how you know your AI is reliable.
We take your top 20 critical workflows and build a reliable UI and API automation foundation integrated into CI. Built with your team, in your stack (Playwright, Selenium or whatever fits), so they can extend it after we leave.
A clean, maintainable structure for UI and API tests that new engineers can pick up quickly.
Your top 20 journeys covered end to end, at the right layer, with API tests wherever the UI isn't needed.
Explicit waits, isolated test data and flake triage, so a red build actually means something.
Tests running on every pull request and release, with clear reports and gates that block real regressions.
Documentation and pairing sessions so your QA engineers own and grow the suite.
If your product has AI features, eval checks are added to the same pipeline alongside the functional tests.
Continued help across releases: extending automation, reviewing test strategy for new features, and coaching your team.
We keep your eval suites current as models, prompts, tools and data change, and review results before major releases.
A repeatable evaluation harness your team owns: test sets, scoring rubrics and CI wiring, with documentation.
Tell us what you're building. We'll figure out the right scope together.
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