Important decisions get lost in Slack, email, docs, meetings, and people's heads. Teams forget what was agreed, why it was agreed, and who needs to know. AI makes this worse — acting on stale or contradictory context faster than humans can catch it. Setlyr gives teams a shared, machine-readable record of what was decided, why, and who needs to act on it.
Important decisions are scattered across chat, email, docs, meetings, and people’s heads. Teams act on different versions, forget why something was agreed, and discover contradictions too late. Once AI joins the workflow, bad context travels faster.
Decisions captured across many channels, but no canonical record — only scattered traces.
Stakeholders act on different interpretations of the same commitment.
AI systems execute on outdated context because no governance layer exists.
AI amplifies bad context faster than humans can detect or correct it.
Setlyr is building a system that helps teams keep important decisions from getting lost, contradicted, or acted on unsafely. The goal is a decision memory teams can actually operate from.
Pull decisions from channels people already use: messages, voice, email, documents, and system events. Normalize them into structured decision objects with provenance.
Compare decisions across time, assumptions, constraints, and dependencies. Surface silent reversals, policy collisions, and resource conflicts before they propagate.
Render the same decision differently for executives, operators, clients, and AI systems. Add approval gates, permissions, and machine-readable context for safe execution.
Setlyr is for teams where wrong context creates real cost — especially when decisions move across multiple people, tools, and AI systems.
Headcount, initiatives, and cross-functional dependencies expand faster than shared context can. Priorities move across Slack, docs, tickets, and planning rituals, while AI copilots amplify the cost of stale context and silent reversals.
Decisions move between calls, deliverables, stakeholder updates, and follow-up tasks. Contradiction becomes visible to clients fast.
Teams that want deeper AI deployment but need provenance, governance, and accountable routing before they automate more aggressively.
We're working with a small number of teams to understand where important decisions get lost, contradicted, or acted on unsafely — especially in environments where AI is starting to take on real operational work.
How important decisions move through your workflow today, where contradictions or version drift show up, and whether a shared decision memory layer would actually help.
Teams already deploying AI into real operations, where wrong context creates real cost.
We are much more interested in where this breaks than in polite validation.
Decision Infrastructure reframes decision health as avoided damage — contradictions caught, reversals surfaced, stale commitments flagged before they became rework. Every metric traces back to a specific incident you can inspect.
"3 conflicts detected before stakeholder delivery." Surfaced before damage, not buried in a retrospective.
"2 reversals detected within 48 hours." Decisions that quietly changed direction, now visible.
"12 decisions past review date." Outdated context caught before it drove wrong execution.
Every conflict caught is rework that never happened. The platform estimates avoided hours from each detection.
Instead of asking people to switch systems or maintain another knowledge base, Decision Infrastructure creates a reliable decision layer across your existing workflow, with traceability, governance, and better context for action.
Capture decisions from messages, email, voice, and operating systems without forcing the team into a new daily workflow.
Turn decisions into structured records with owners, evidence, constraints, and follow-through instead of burying them in notes.
Give humans and AI systems better context, approval gates, and audit trails before decisions become downstream actions.
Every captured decision improves context retrieval, conflict detection, and comparable-outcome recall for the next one. Value compounds — the tenth decision captured is more useful than the first.
Dashboards reframe metrics as damage prevented: contradictions caught before deployment, reversals surfaced before stakeholder confusion, stale context flagged before costly rework.
Routine decisions flow with a single click. Consequential ones get full evidence, review scheduling, and approval gates. The system matches ceremony to stakes.
We'll map where important decisions move, where contradictions show up, and whether a shared decision memory layer would actually help.
The memory, contradiction-detection, and calibration layer for mixed human–AI operating environments.