A direct walk through the Product Intelligence platform, capability by capability. Each capability is a feature of the new category — not a bolt-on to a pre-AI product stack. Every section describes what's shipped and how it fits into the unified product brain.
Every voice note, decision, and capacity call your team has ever shared lives in the system. Months from now, when someone asks why a call was made, the original context surfaces — before you repeat the mistake.
Open full-screen →Ownership, capacity, recent work, customer fit — weighed in seconds. The ticket lands on the right desk before your next standup.
Open full-screen →AethrionX reads every pull request and turns the diff into a diagram and a summary, attached to the ticket. The reviewer opens the PR already knowing the shape of the change.
Open full-screen →Describe the problem out loud. The system writes the ticket — clean, classified, routed. No fields, no menus, no follow-up edits.
Open full-screen →Talk to your board. Filter by domain, customer, priority, or anyone on the team — watch it narrow in seconds, no menus, no clicks.
Open full-screen →Move tickets between sprints, ask about team capacity, replan around blockers — all without leaving the dock. Sprint planning collapses from an hour to a conversation.
Open full-screen →AethrionX reads merged pull requests on GitHub and GitLab and turns them into product-readable change summaries. Every merged PR becomes a record that names the shipped features, surfaces detected risks, and links back to the tickets it closes. Ticket status updates automatically on merge. Product managers can read what shipped in a release without reading the diff, and engineering leaders can audit what was claimed versus what was actually changed.
AethrionX renders change as a product narrative, not a diff. The change-visualization layer takes the raw output of pull-request analysis and structures it for the people who don't read code — product managers, founders, customer success, and account owners. Every release becomes a readable artifact: what shipped, what risk was flagged, what tickets closed, and what claims should be tested in QA before the change reaches customers.
AethrionX's institutional memory is active, not passive. Before a mutating action is committed — assigning a ticket, changing priority, deleting work, creating a ticket with an assignee or priority — the system intercepts the call and surfaces relevant memory if a prior decision conflicts. The user sees the conflict, the source memory, and a suggested alternative before the action lands. Memory becomes a guardrail that catches mistakes at the moment they would happen, not a search box people forget to query.
Voice is a primary interface in AethrionX, not a button-bound add-on. On every authenticated page, you can dictate a full ticket, filter a sprint board, reassign work, navigate the product, or run a standup without touching the keyboard. Voice is built on real-time streaming transcription with confidence indicators on every auto-filled field, so the dictation is editable in place rather than locked behind a confirmation modal.
X chat is AethrionX's conversational interface for the product brain. Open it as a side panel on the tickets page or as a full-page workspace, and ask questions in natural language. X chat reads from the unified store of tickets, signals, decisions, and customer context — so answers cite the underlying tickets and memories rather than guessing, and a question becomes a starting point for action rather than a dead-end search result.
AethrionX routes incoming tickets to owners based on ownership history, recent work, capacity, and account fit. Every assignment shows its reasoning — which signals weighed in, which prior decisions were considered, and which alternative owners were ruled out — so reviewers can override with full context, and every override teaches the system.
AethrionX finds related work without exact-keyword matches. Tickets, decisions, customer messages, and pull-request summaries are all embedded into the product brain, so a search for a concept surfaces the related work even when the wording differs. Semantic search powers cross-ticket de-duplication, customer-history lookup, and the institutional-memory recall that runs before mutating actions.