Shipping fast is table stakes. Shipping right is what separates leaders from noise. We've built AI systems that help tech companies optimize product usage, reduce churn, improve developer experience, and ship features that actually get adopted. We understand the technical and business constraints of building software for other builders.
We help SaaS companies and tech platforms optimize product adoption, reduce churn, accelerate API adoption, and make data-driven roadmap decisions using AI-powered product analytics and recommendations.
You ship a feature. Users don't find it. Or they find it, try it once, then never come back. The problem isn't the feature—it's that your generic onboarding doesn't match how different user types learn. We've built AI-driven product guides that adapt to user behavior—showing prompts at the moment of need, skipping tutorials for experienced users, providing extra scaffolding for new users. One project management SaaS saw adoption of a new collaboration feature jump from 12% to 41% within 4 weeks using adaptive guidance.
You notice a customer unsubscribe at billing time. You didn't see the warning signs—declining usage, unmet expectations, feature confusion. We've built predictive churn models that flag at-risk accounts based on usage patterns, engagement drop-off, and support interactions. Your customer success team reaches out with targeted interventions before they cancel. One SaaS reduced churn by 2.1 percentage points—6-figure revenue impact—by catching at-risk accounts early.
Developer adoption of APIs depends on documentation, SDKs, and learning curves. Most companies stop at README files. We've built intelligent API platforms that generate examples in the user's preferred language, predict which endpoints they'll need next, surface rate-limit warnings before they hit them, and provide contextual troubleshooting. Developers ship faster. Support volume drops. API revenue accelerates.
Most companies prioritize features based on customer feedback or founder intuition. It's noisy and biased. We've built product analytics systems that track feature usage, flag friction points, measure impact on retention and revenue, and surface opportunities. Your roadmap is data-driven. Feature prioritization stops being a gut call.
A B2B accounting software platform was losing $800K annually to churn. We built a model tracking 40+ engagement signals—login frequency, feature usage breadth, support ticket volume, time-to-value on key workflows. The model predicted churn 30 days before it happened with 78% precision. Customer success used the predictions to intervene—calls offering training, custom workflows, priority support. Churn dropped from 6.2% to 4.1%. Annual customer lifetime value improved significantly.
A project management tool shipped a new timeline view. 8% of users tried it. 2% used it regularly. The feature was good but hidden and confusing. We built a system that analyzed user behavior, showed prompts to users working on timeline-adjacent tasks, and provided contextual tutorials. Adoption jumped to 34%. The timeline view became a core part of the platform's competitive advantage. Revenue-per-user improved.
A cloud infrastructure platform had thousands of developers who built on its APIs. Documentation was comprehensive but dense. Developers struggled to find examples in their preferred language, and support tickets for API issues were expensive. We built an intelligent API platform that: surfaced relevant endpoints based on user's current workflow, generated code examples on-the-fly in Ruby, Python, Node, Go, and more, and provided proactive warnings about rate limits and deprecated endpoints. API adoption accelerated 40%. Support tickets dropped 25%.
A collaboration tools company had 200 feature ideas in the backlog and no data to prioritize them. We built a product analytics system that tracked feature usage, user segment preferences, correlation with retention, and impact on revenue. Data showed that new users who completed collaboration tutorial within 2 days retained at 78%, but most never took the tutorial. A tutorial redesign became priority #1. It drove measurable retention improvement.
Event management app with high adoption optimization
Platform with advanced product analytics and recommendations
A/B testing. We show AI recommendations to 50% of users and baseline behavior to the other 50%. Then we measure adoption, engagement, retention, and revenue impact. You see the delta. If impact is positive, we scale. If not, we iterate.
Absolutely. We can run multiple concurrent experiments—different recommendation algorithms, different guidance timings, different messaging—and analyze impact on each metric that matters to you. We help you navigate statistical significance and interaction effects.
Depends on your product and user base. Fast-growing products should retrain monthly or quarterly. Stable products might retrain annually. We monitor model performance in production and alert you when accuracy drifts below acceptable thresholds. Retraining is usually automated.
That's the point. Data-driven product development sometimes shows that features you built aren't driving value. We help you interpret that honestly and make the hard calls to pivot, double down, or sunset features. Your users will tell you what's working if you listen.
We'll assess your current product telemetry, your success metrics, and your organizational readiness for data-driven decisions. Then we'll show you where AI creates the biggest product and revenue wins.