Real problems, measured outcomes.
Three engagements, told the way we work: the costliest failure, the reliability layer we built for it, and the numbers that proved it keeps working. Client work is proprietary, so these are genericized by industry, but the methods and the metrics are exactly what we do.
Problem, approach, outcome
Each case follows the same shape we bring to a real engagement: find the failure that costs the most, build the reliability layer, and measure the result with an evaluation harness so the outcome is a number, not a story.
A planner, implementer and reviewer pipeline that ships production code
Genericized from delivery work with a product engineering team that needed to move faster without lowering the bar on quality.
The team was drowning in the same repetitive build work: turning well-understood requirements into code, tests and documentation. A single senior engineer could do it well, but not at the volume the roadmap demanded, and a naive "ask the model to write it" approach produced code that looked right and broke in review.
- Split the work into three cooperating roles: a planner that turns a requirement into an explicit, reviewable plan, an implementer that writes the code and tests against that plan, and a reviewer that checks the result before a human ever sees it.
- Constrained every hand-off with structured outputs and validation gates, so a step cannot pass work downstream until it is well-formed and self-consistent.
- Wired the whole pipeline to an evaluation harness that scores each run against real acceptance criteria, so quality is a number the team can watch, not a gut feel.
The pipeline now turns requirements into production-grade code, tests and documentation, with a human reviewing polished output instead of raw drafts. Delivery on the covered workflows is materially faster, most runs succeed end to end, and a meaningful share of changes arrive merge-ready on the first pass.
Grounded retrieval with a golden-task eval set built from real questions
Genericized from a knowledge-assistant engagement where wrong-but-confident answers were the real risk, not slow ones.
A team wanted an assistant that answered questions over their own documents. The first version was fluent and dangerous: it invented details, cited nothing, and nobody could tell whether an answer was grounded in the source or made up. Every model or data change was a leap of faith.
- Built retrieval that grounds every answer in the underlying documents and returns the passages it relied on, so an answer can be traced back to a source rather than trusted blind.
- Collected real questions from actual users and turned the hardest and most representative into a golden-task evaluation set with known-good answers.
- Ran that eval set on every change to the model, prompts or index, so accuracy and grounding are measured before anything reaches users, and quality regressions surface in the harness instead of in production.
Answers are now grounded and attributable, and the golden-task set catches drift early: when a change quietly degrades retrieval or grounding, the numbers move before users ever notice. Updates ship with evidence instead of hope.
A reliability layer that makes a flaky LLM feature measurable
Genericized from reliability work on an existing LLM feature that demoed well but could not be trusted in production.
A live feature worked most of the time and failed unpredictably the rest. Failures were invisible until a user hit one, there was no way to tell whether a change made things better or worse, and the team had stopped trusting their own releases.
- Replaced free-text model calls with structured outputs and validators, so malformed or off-spec responses are caught at the boundary instead of flowing downstream.
- Added fallbacks for the messy cases the demo never showed, so a single bad response degrades gracefully rather than breaking the whole flow.
- Named the ways it fails: a failure taxonomy plus an evaluation harness, so every category is counted and every change is measured against it.
The feature now fails loudly and rarely, and every change is measurable: the team can see whether a release moved the numbers up or down before it goes out. Reliability stopped being a debate and became a dashboard.
These case studies are genericized on purpose. Real client work is proprietary, so we have removed names, logos and anything identifying, and described each engagement by industry instead. The problems, the methods and the metrics are real. On a scoping call we can talk through the specifics of work that is relevant to you, within what our agreements allow.
Bring us the failure that costs you the most
Start with a free 20-minute call. We will look at where an AI workflow is quietly failing, what a reliable version looks like, and how we would measure it. Fixed scope, fixed price, quoted after the call.
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