Building is no longer the bottleneck — understanding what to build is.

• What actually moves the needle on user retention?
• What turns first-time users into power users?
• Where does my AI product produce the "AHA" moment, and when does it create friction?

These answers live in every user interaction pattern, both implicit and explicit. But extracting value from that behavior is fractured and manual — resulting in product teams building blindly, guessing at friction points, missing abandonment patterns, and incorrectly assuming success.

This is the case for two main reasons:

• User data lives in silos across your entire stack: Explicit feedback (NPS surveys, thumbs up/down, customer support) captures a small fraction of users who voluntarily articulate what frustrated them. Implicit behavior (session replays, click patterns, etc.) shows what led users to abandon. System traces show when services failed during their session. Each of these data streams lives in disconnected tools — the complete user story emerges only when you integrate and analyze them together.
• AI is the central workflow but analytics hasn't adapted: Users increasingly interact with products through AI assistants and agents — taking actions and completing tasks through natural language. Current analytics are built for click‑based workflows, not AI‑centric ones. Evaluating AI success requires connecting surrounding context — what the user did before, during generation, and after.

With today's tooling, teams build with incomplete information — guessing at friction points, missing abandonment patterns, and optimizing for the wrong signals.

Grounded Intelligence connects your AI workflows to every surrounding data stream and actively watches them — session replays, customer support conversations, observability traces, payment events, user feedback. It learns patterns from all these data streams, builds cross‑stream insights, and tells you clearly what to improve — both in the product and your AI system.

Generative AI is finally capable of doing this well:

• Handles high‑volume data at scale — processing millions of interactions across multimodal streams (videos, logs, text, timeseries).
• Constructs insights humans can't easily observe — detecting temporal/causal patterns (e.g., "X precedes Y within 30s") and connecting implicit signals to outcomes.
• Recent LLMs can understand temporal and complex data, synthesize multimodal signals, and produce structured output.

With Grounded Intelligence, your team knows exactly what to prioritize—what actually drives retention, reduces churn, and creates user love. Every interaction becomes an intelligence gain.

Once Grounded Intelligence becomes the source of truth for user behavior — the complete, unified picture across every interaction — two things become possible:

• Your product gets hyper‑personalized. Dynamic content, custom funnels, system prompts that adapt continuously to each user's behavior. Not static segments, but real‑time intelligence across every touchpoint.
• Your AI system gets continuously smarter. Models train on production signals daily — learning from implicit user behavior like abandonment after specific responses, confusion patterns in repeat queries, and what drives conversions. Reinforcement learning grounded in real usage, not synthetic benchmarks.

This fundamentally shifts from asking "What happened?" to "How do we get smarter from what just happened?" Your product doesn't just improve for everyone — it gets smarter for each user, every day, grounded in real behavior across every data stream you have.