← Back to home
Public roadmap
What has shipped and what we are building next. Updated regularly. Not a promise — a best-effort snapshot.
Q1–Q2 2026
shipped- OST-native discovery pipeline — Insights → Opportunities → Solutions → Experiments; kanban, drawers, stage transitions
- AI backlog parser — paste raw ideas; AI surfaces themes, deduplicates, and proposes structure
- Reusable scoring library — RICC, ICE, WSJF, and custom formula models; any gate can reference any model
- Gate system — hard blocks and soft warnings on pipeline transitions; evidence thresholds enforced
- Multi-user workspaces — invite teammates, owner/admin/editor/viewer roles, full tenant isolation
- Stakeholder intake portal — public-facing idea submission form per workspace, with AI classification on arrival
- Insight deduplication — similarity search surfaces near-duplicate insights before you save
- Security and compliance baseline — DPA, Privacy Policy, Sub-processors, Security page; RLS and audit log live
Q3 2026
in-progress- Full discovery graph — every insight, opportunity, and solution connected; see what evidence informed what decision
- Discovery health dashboard — funnel overview, bottleneck detection, gate health, team load by owner
- Pipeline editor polish — rename, reorder, and tune stages and gates without leaving the canvas
Q4 2026
planned- Team load heatmap — who is drowning and who is idling, surfaced in the dashboard
- Outcome attribution — tag every solution with a target metric and expected impact; roll up to the group level
- IQS freshness decay — insights that nobody has referenced in 90 days auto-dim; weekly digest flags zombie evidence
- Quarterly discovery digest — auto-generated summary of experiments decided, solutions shipped, and outcome deltas
Q1 2027
planned- Public launch and open signups (currently early access)
- CSV import from Productboard, Aha, or Google Sheets
- Jira two-way integration — link Sorby opportunities to Jira epics; sync status both ways
- Linear native integration
- Hypothesis similarity search — find past experiments with similar hypotheses before you start