For consumer apps
Your AI coach. Your AI matchmaker.Engineered properly this time.
The launch bump faded because the harness around your AI feature was good enough to ship and not good enough to scale. We fix that.
The pattern is everywhere
You shipped, the curve flattened, and you can't tell why.
The team launched the AI feature. It made the deck for the board meeting. It made the App Store update notes. The first month of usage data was great, then the curve flattened. Six months later, the feature is still in the product, technically functional, and quietly underperforming the projection.
This isn't a feature problem. It's almost never a feature problem. The problem is the version that shipped was the version your team had time to build, not the version that actually works for real users at scale.
The harness around the model was assembled prompt-by-prompt under deadline pressure. The retrieval works for the cases your team imagined and breaks on the cases real users send. None of these failures show up in the dashboard because the dashboard is looking at outputs, not inputs.
We work on the inputs.
What this looks like for consumer products
AI conversational coaches.
Common harness failures: Tone drift across turns, conversation memory that loses the thread, generic responses on the third or fourth interaction.
AI-generated content at signup or onboarding.
Common harness failures: The magic moment works in 70% of cases, the other 30% see something that breaks the spell. Usually a retrieval or grounding problem.
AI personalization in the core loop.
Common harness failures: Silent quality degradation as user behavior shifts or models change, with no eval set to catch it.
AI customer support.
Common harness failures: Hallucinated information stated confidently, usually because retrieval is missing or under-grounding the response.
What you ship at the end of the engagement
Three concrete artifacts.
A rebuilt harness as a PR
Same feature, same look, same vibe. The retrieval is sharper, the prompt assembly is principled, the fallbacks actually fall back, the output validation is robust. Drop-in replacement for what's currently shipping, ready for an A/B test.
A benchmark report
Side-by-side comparison of the new harness against your current baseline on the metric you came to us to move. Confidence intervals, sample sizes, failure-mode breakdown. The kind of artifact you can show your CEO without flinching.
An evaluation set you keep forever
A set of 200–500 graded test cases drawn from your real user interactions, plus the grading rubrics. Use it the next time someone wants to swap models, add a feature, or change the prompt. Most consumer teams have never had one of these.
Most common engagement for consumer-app teams
Rebuild
$35,000Four weeksAudit plus rebuilt harness as a PR your team owns, evaluation set, A/B test plan. Money back if the new harness doesn't beat baseline.
Who this is for
You're a Head of Product, Head of Growth, or founder at a consumer app with at least one shipped AI feature, real users, and a metric you're trying to move. You can name the metric. You'd rather pay $35K and have a rebuilt harness in four weeks than spend the next quarter on it internally.
Who this isn't for
You haven't shipped yet. You don't have user logs. The “AI feature” is actually a chatbot wrapper that nobody uses. That's a different conversation, and not one we can help with.
How every engagement starts
The audit is two weeksand fifteen thousand dollars.
If we don't find at least three things to fix in your harness, you don't pay. The audit is also how every Progressical engagement starts. Rebuild and operations follow from it.
Start with a diagnostic. Commit once you see the findings.
Two weeks, three findings minimum, or no charge. That's the audit. Rebuild and operations are available once you see what we found.