AI Evaluation
Every AI-touched line is reviewed by a human engineer before merge.
Every AI-touched line is reviewed by a human engineer before merge — AI sets the pace, engineers set the standard.
AI sets the pace here, but engineers set the standard — every model output that reaches a real user has passed through an evaluation step and, for anything consequential, a human review gate.
We're deliberately not the studio that quotes you a number and disappears until the deadline. Expect short, regular check-ins, staging access from early on, and a straight answer any time something turns out to be harder — or easier — than it looked at the start. If a piece of this isn't the right fit for your team, we'll say so directly rather than stretching the engagement to fill a quarter.
How we build it
Flag AI-touched code
Every AI-generated diff is tagged for review, never silently merged.
Review against standard
A senior engineer checks it against the same bar as hand-written code.
Gate the merge
Nothing reaches main — or production — without that human sign-off.
Track the review bar
We track how often AI-touched code needs rework, so the process itself keeps improving.
Frequently asked
Structured prompting, retrieval grounding where relevant, and an evaluation suite that runs before every change ships — plus a human review gate on anything user-facing.
Usually yes. Most AI work is integrated into an existing codebase rather than built as a separate app, wired in behind a feature flag so it can roll out gradually and get rolled back instantly if something's off.
We design fallbacks and guardrails up front — the goal is that a bad model response degrades gracefully (a clear 'I'm not sure', a human handoff) rather than silently misleading a user.
We're not tied to one vendor — OpenAI, Anthropic's Claude and open-weight models are all in regular use, chosen per task based on cost, latency and quality trade-offs rather than brand preference.
A concrete evaluation suite specific to the task — pass rate, hallucination rate, latency — run before every prompt or model change ships, not just a subjective 'looks good' check.
No. Your data is used to ground retrieval and evaluate outputs for your own product; it isn't sent off for third-party model training.
A focused AI feature can often reach a working staging version in 1–3 weeks; agentic or multi-tool workflows typically take longer because the evaluation loop takes more iteration to get trustworthy.
We help you estimate and monitor token/API cost as part of the build, and design prompts and retrieval to keep it proportionate to the value the feature delivers.
Who this is for
- Teams with a clear AI use case, not just a hunch that they 'should have AI somewhere'
- Products with an existing user base to layer AI into, where the workflow already exists
- Founders who want AI without the hype — a feature that measurably saves time, not a demo gimmick
- Teams that tried building this in-house and hit reliability or evaluation problems
- Companies that need guardrails and audit trails around AI output, not just a raw model call
What you walk away with
- A written spec of exactly what the model is — and isn't — trusted to decide
- An evaluation suite that runs before every prompt/model change ships
- Guardrails and fallback behaviour for low-confidence responses
- Observability into real usage once it's live
- A rollback plan if quality regresses after a change
Who works on this
AI & Full-Stack Developer · QA · Testing
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