From experience to conversion. Autonomously.
We are building an engine that finds the friction costing you conversions, fixes it, tests the result and learns, continuously. Each version ships a capability that stands on its own.
Better UX is your conversion engine. Corexi keeps it running.
Friction is quietly costing you conversions, and nobody measures it.
Teams bounce between analytics, heatmaps, session replays, audit tools and spreadsheets. No shared score, no layer that ships the fix, no feedback loop. AI-assisted development widens the gap: products ship ten times faster, and every friction point they ship is a conversion they lose.
Three shifts make this the moment.
Products now need a layer that looks after their UX on its own: it catches regressions, ships the fix and keeps you compliant, on every release.
- AI-native dev10× more surface, 10× more friction
Agents ship features in an afternoon. Every one adds UX debt no human reviewed, and a conversion nobody measured.
- Inference economicsCost falls ~10× a year
Continuously watching a live product with AI was uneconomic two years ago. Now it's cheap enough to run on every deploy, and getting cheaper.
- Accessibility is lawCompliance is no longer optional
Regulations like the European Accessibility Act make WCAG a legal floor. A layer that monitors and files the proof becomes infrastructure.
Two moments. One layer.
Corexi meets your product at both moments that matter: in the IDE while you build, and on the live product once you have shipped. Each version compounds across both.
- At dev time · MCP in your IDE
- In production · Autopilot runs, live now
From finding the fix to shipping it.
Today it shows what stands between people and your goal, and hands your team the fix. Next, it ships the fix itself, checks it with real users and keeps what works.
- v1.0Live now
Finds it, prices it, hands it over
- Signs in and walks every screen, at desktop or phone width
- Reads each screen through five lenses and weighs it against your analytics
- Prices every finding against the goal you set
- Hands the fix to Jira, GitHub, Slack and your editor over MCP
- v1.xNext
Goes further on its own
- Walks native iOS and Android apps, not only the screens you upload
- Opens the fix pull request itself, for your team to review
- Scores AI readiness: whether AI agents and assistants can use your product, from finding the button to finishing the checkout
- v2.0Where it is going
Self-driving UX
- Ships the fix behind a feature flag, to a slice of your users
- Measures it against your goal with real people
- Keeps it or rolls it back, and tells you which and why
LED-214 · Send invoiceflag · 10%Invoices sent, first weekWithout the fix61%With the fix68%Kept, and rolled out to everyone
v1.0 is what runs today. The later versions are our plan, shown without dates.
Our principles.
- Transparency over black boxes
Every score, every finding, every recommendation comes with evidence you can verify.
- Human oversight by default
AI proposes, you decide. No changes ship without explicit approval.
- Data over opinions
Decisions grounded in real user behavior and measurable outcomes.
- Accessibility is non-negotiable
Including the 15 to 20% of users who think differently. WCAG is the floor, not the ceiling.
Then it goes round again.
Give it your product's URL. We set up the login with you once; from then on it goes round on its own, after every release.