Composed AI gives product teams a continuous evidence layer — detecting user friction patterns, tracking conversation signals, and grounding roadmap decisions in real data.
User interviews are time-consuming. Ticket tagging is inconsistent. Analytics show what happened — not why.
Interviews give depth but take weeks. By the time research is done, the sprint is over. Most product decisions happen without any direct user evidence.
Support tickets contain the most honest user feedback available. Product teams almost never see it. Customer success paraphrases it in weekly calls.
Sprint planning becomes a negotiation between stakeholders. Without a shared evidence layer, whoever speaks loudest shapes the roadmap.
By the time user research reaches the product team, it has been filtered, summarised, and partially forgotten. Context is lost at every handoff.
Dashboards, user interviews, and tagged tickets are all backwards-looking. They measure events that already occurred. They don't surface emerging patterns before they become problems.
Amplitude, Mixpanel, and GA tell you drop-off rates. They don't tell you that the checkout confusion is caused by a missing field label your design team missed six months ago.
Support labels are inconsistent. A feature request tagged 'billing' in one ticket is tagged 'product bug' in another. Pattern detection requires clean signals, not noisy labels.
Qual research illuminates intent in a moment. It doesn't monitor what users are struggling with right now, this week, continuously.
Composed AI connects your product documentation, support conversations, user feedback, and behaviour data — then monitors it continuously for patterns your roadmap should respond to.
Not tags. Not labels. Composed AI reads across your connected sources and surfaces recurring themes as structured Signals with evidence, frequency, and impact scoring.
When a stakeholder challenges a priority, open Ask Composed. Ask 'What are users struggling with most in checkout?' and present a cited, sourced answer.
Instead of scheduling a quarterly research cycle, Composed AI surfaces new patterns the moment they emerge from your connected data.
No separate tool. The same platform your support and customer success teams use — applied to product operations.
Composed AI continuously monitors your connected sources: support tickets, user feedback forms, NPS responses, product documentation, and usage logs. When a pattern emerges — a recurring friction point, a documentation gap, a feature request cluster — a Signal is created with evidence, frequency, and suggested owner.
Ask Composed lets your team investigate before committing to a roadmap decision. 'What are users asking about most in onboarding?' 'Which features generate the most confusion questions?' 'What changed last sprint that drove the support spike?' Every answer is traced to specific sources.
Deploy a customer-facing AI chatbot grounded in your approved product guides and specs. The assistant resolves routine pre-sale and onboarding questions automatically, while analyzing conversation logs to identify and monitor user friction signals for the product team.
Composed AI detects that questions about 'workspace invitations' have increased 3x over the past two weeks across support tickets and user feedback channels. A Signal is created with evidence, frequency, and an assigned product owner.
The product manager opens Ask Composed and asks: 'What exactly are users struggling with in workspace invitations?' The answer traces to 15 specific support tickets and 4 user feedback entries describing the same broken email flow.
Ask Composed reveals that the email invitation flow was updated two weeks ago in a minor release. The release notes mention the change but the help documentation was never updated.
The PM updates the Knowledge Base with clarified documentation and files a bug ticket for the broken email flow. Both actions are logged in the investigation thread.
Volume drops over the following week. The investigation is saved as a persistent thread. The team now has a documented case study for how undocumented UI changes create support spikes.
Every prioritisation discussion can be grounded in real signals from real users, not stakeholder opinions or gut feeling.
Instead of waiting for quarterly research cycles, product teams see emerging user friction patterns within hours of them forming.
Support, customer success, and product teams all see the same signals from the same shared Knowledge Base. No more telephone game.
Investigation threads persist. When a similar issue returns six months later, the team has the full evidence trail from the previous investigation.
Connect your product documentation, support ticket exports, user feedback forms, NPS data, and internal wikis. Composed AI structures everything into a shared Knowledge Base.
Composed AI surfaces the first patterns from your existing data. Most teams see their first actionable signal within the first session.
Before the next sprint planning, open Ask Composed and investigate the top three signals. Present evidence, not opinions.
Every deployment generates new signals. Every signal generates new investigations. The evidence layer compounds over time.
Connect your user data, surface recurring patterns, and make roadmap decisions grounded in real signals.