Our Standards
If you can't measure it,
you can't trust it.
The AI industry runs on demonstrations and vibes. We run on numbers. Every system we build is tested against objective, predetermined criteria before it ships — specific accuracy targets, latency budgets, and failure rate ceilings that are agreed with you before we write a line of code. Here's what that actually means in practice.
The Problem
Why "it seems to work" isn't good enough.
Most AI projects are evaluated the same way: a developer builds something, runs a handful of example queries, decides it looks good, and ships it. This is how you end up with a system that impresses in the demo and disappoints in production.
The fundamental problem is that language models produce different outputs under different conditions. A system that answers your ten test questions correctly may still fail 30% of the questions your actual users ask — because no one measured it against a representative sample with known answers.
This is especially dangerous for businesses in regulated industries or high-stakes environments. If your AI knowledge base is giving your staff incorrect information about clinical protocols, compliance requirements, or contract terms, you need to know that before it causes a problem — not after.
Our position is simple: an AI system that hasn't been tested to a measurable standard isn't ready to make business decisions. We build the testing infrastructure as part of every project, not as an afterthought.
How We Work
What a quality-first build process looks like.
Acceptance criteria before we build
Before a line of code is written, we agree with you on what "passing" looks like. Specific targets for accuracy, latency, and reliability. Not "it seems to work pretty well."
Held-out evaluation datasets
We build or help you build a set of test cases with known correct answers — questions or tasks the system has never seen. We run the system against them and measure the results objectively.
Iterative improvement with measurement
When scores fall short of acceptance criteria, we make changes and re-test. Every iteration is measured. We don't guess that a change helped — we confirm it.
Regression testing post-deployment
We don't test once and move on. When a system changes — model update, new data, integration change — we re-run the eval set to confirm nothing broke that used to work.
Documented failure modes
Every system has conditions under which it performs worse. We find them, document them, and communicate them clearly at handoff. You know what the system does well and where its limits are.
Quality report at handoff
When we deliver a project, we deliver a quality report: what we tested, what the scores were, what the acceptance criteria were, and the known edge cases. Not a certificate of quality — evidence of it.
What We Measure
Specific metrics for every system type we build.
Different AI systems have different failure modes. Here are the metrics we use for each, and what they actually mean in plain language.
RAG / Knowledge Systems
Answer accuracy
What percentage of eval-set questions did the system answer correctly?
Groundedness rate
What percentage of responses were fully supported by retrieved source documents, with no fabrication?
Retrieval precision
When the system fetched document chunks to answer a question, how often were the right chunks retrieved?
Hallucination rate
How often did the system state something that has no basis in the source documents?
Latency (p95)
In the 95th percentile of queries, how long did the system take to return a response?
AI Agents
Task completion rate
What percentage of assigned tasks did the agent complete successfully end-to-end?
Error rate
How often did the agent take an incorrect action, call a wrong tool, or produce an unusable result?
Escalation accuracy
When the agent escalated to a human, was it appropriate? Both over-escalation (cautious to the point of useless) and under-escalation (overconfident) are failure modes.
Latency per task
How long does it take the agent to complete a representative task from start to finish?
Workflow Automation
Execution success rate
What percentage of workflow runs completed without an error or exception?
Exception rate
How often did the workflow encounter a condition it wasn't prepared to handle, requiring manual intervention?
Mean end-to-end latency
How long does the workflow take to complete, from trigger to final action?
Data integrity rate
How often did data passing through the workflow arrive at the destination correctly — complete, formatted, and valid?
Quality & Cost
Why higher quality costs more — and when it's worth it.
Building a reliable AI system is not the same cost as building one that passes a demo. The difference is time: time to build the evaluation dataset, time to run testing cycles, time to iterate on retrieval strategy, time to validate that changes don't break things that were working.
Specifically, higher quality requires:
- Evaluation dataset development. Building a set of test questions with verified correct answers takes real work — often including subject matter expert review for domain-specific content.
- Multiple iteration cycles. Test, adjust retrieval strategy, retest, adjust prompt, retest again. Each cycle takes time and compute.
- More sophisticated architectures. Higher accuracy often requires hybrid search, reranking models, or structured approaches like GraphRAG — all of which are more complex to build and tune.
- Regression testing infrastructure. Staying confident that the system is still performing after updates requires test automation that takes time to set up.
Not every project needs to hit the highest bar. A basic internal FAQ assistant can be built at a basic quality level. A system giving clinical staff information they act on cannot. We'll tell you clearly what tier your situation calls for — and what the tradeoffs are if you want to reduce cost by accepting a lower standard.
Talk to Us
Ask us what quality looks like for your specific project.
Every project is different. The right quality target depends on what decisions the AI is informing, who's relying on it, and what happens when it's wrong. That's a conversation worth having before you scope the project.