From hunch to validated bet
Generate net-new product bets, not just synthesize the signal you already have. Capture a hunch, let AI expand it into testable hypotheses, pre-filter with synthetic research, and gate every idea on real evidence and feasibility before it earns a place on the roadmap.
Hypothesis Engine
BetaTurn a hunch into a portfolio of testable bets
Capture a raw hunch and let AI expand it into a portfolio of testable hypotheses—each with an assumed problem, target segment, and predicted impact. Score them against your evidence corpus before committing a single sprint.
AI idea expansion
One hunch becomes a structured set of distinct hypotheses—variations on problem, segment, and mechanism you wouldn't have brainstormed alone.
Evidence-gated scoring
Each hypothesis is scored against your existing signal corpus, so ideas with real supporting evidence rise and pure speculation is flagged as unvalidated.
Assumption surfacing
Every bet ships with its load-bearing assumptions made explicit—so you know exactly what to test before you build.
Hunch capture
Drop a one-line idea, a Slack thread, or a strategy memo—Framewerk structures it into a hypothesis object.
Generative expansion
AI proposes adjacent and contrarian variants, each with problem statement, target segment, and predicted outcome.
Corpus-grounded confidence
Cross-references each hypothesis against ingested evidence to assign a confidence band: supported, mixed, or speculative.
Promote to opportunity
Validated hypotheses graduate into the Discover pipeline as ranked opportunities with their evidence trail attached.
Assumption Lab
BetaPre-validate in hours, confirm with humans
Pressure-test a hypothesis against AI personas modeled on your real customer segments—then auto-generate the research plan to confirm it with real humans. Synthetic is the pre-filter; real evidence still gates the ranking.
Synthetic pre-filter
Run a hypothesis past personas grounded in your account and signal data to kill obvious losers before spending real research cycles.
Auto research plans
Promising bets generate a ready-to-run interview guide, screener, and survey—targeted at the exact segment the hypothesis names.
Evidence discipline preserved
Synthetic results are clearly labeled as directional. Only real human evidence can move a bet up the priority ranking.
Segment-grounded personas
Personas are built from Account Intelligence profiles and real signal, not generic archetypes.
Synthetic interview runs
Ask follow-up questions and watch personas react, surfacing objections and unmet needs in minutes.
Research plan generation
Auto-draft interview guides, screeners, and surveys scoped to the hypothesis and its target segment.
Confidence labeling
Every synthetic finding is tagged directional and excluded from ranking until corroborated by real evidence.
Feasibility Scanner
BetaKnow the cost of a bet before you place it
Before a net-new bet earns a spot on the roadmap, scan it for feasibility—rough effort, codebase fit, and dependency risk—so generative ideas inherit the same effort-awareness as evidence-backed ones.
Instant effort sizing
Get a T-shirt-size estimate and risk flags for a brand-new idea without a planning meeting.
Codebase-aware fit
Scans your connected repos and architecture notes to flag where a net-new bet fits—or fights—your current system.
Dependency surfacing
Highlights upstream work, integrations, and unknowns that would gate delivery.
Effort estimation
AI proposes a size band from comparable past work in the Outcome Ledger.
Architecture fit check
Cross-references the bet against connected codebase context to flag friction and reuse.
Risk & dependency map
Lists blocking dependencies, integration needs, and the biggest unknowns to de-risk first.
Feeds prioritization
Pushes its effort estimate straight into Priority Intelligence so net-new bets rank on the same axes as everything else.
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