Setlyr
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Your team makes commitments across meetings, messages, and calls—but without a single authoritative record, the result is rework and misaligned execution. Setlyr automatically captures what was decided, catches conflicts before they happen, and ensures everyone acts from the same version of the truth.
Unmanaged decisions have always carried a cost: silent reversals, cross-team contradictions, and teams acting on stale context. Now, AI agents operating in this fragmented environment accelerate every one of those failure modes. AI didn't create decision amnesia, but it makes the operational cost impossible to ignore.
AI systems and teams operate from fragmented context — scattered across channels, tools, and memory — with no reliable way to verify what was actually decided, or when, or by whom.
No canonical record of what was decided, why, who approved it, or what the organization is now paying for getting wrong.
AI recommendations and downstream actions often lack provenance, approval context, and a clear governance layer.
AI increases the speed at which conflicting commitments spread across teams, tools, and workflows, turning weak decision management into visible operational cost.
Setlyr helps teams keep important decisions from getting lost, contradicted, or acted on unsafely. The goal is simple: 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 fits teams where decisions carry downstream consequences — where a silent reversal, a missed constraint, or a misaligned briefing creates rework, client exposure, or governance liability.
Headcount, initiatives, and dependencies expand faster than shared context can. Priorities move across Slack, docs, tickets, and planning rituals. AI copilots amplify the cost of stale context and silent reversals.
Decisions move between calls, deliverables, stakeholder updates, and follow-up tasks. Contradictions become visible to clients fast.
Teams that want deeper AI deployment but need provenance, governance, and accountable routing before they automate more aggressively.
Decision Infrastructure reframes decision health as avoided damage. Contradictions caught. Reversals surfaced. Stale commitments flagged before they become 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.
Most teams track what they decided. Setlyr also tracks whether they were right. Predictions are attached at decision time, scored when outcomes are recorded, and fed back into future conflict detection — so accuracy is measurable, not just claimed.
Attach an expectation to a decision at the time it is made: what outcome do you expect, and how confident are you? Predictions are timestamped and linked to evidence.
When the outcome is known, record it against the decision. Predictions are automatically flagged as due when their review date passes — no manual tracking required.
Variance between prediction and outcome is computed and scored. Systematic overconfidence, blind spots, and domain-level assumption errors become visible patterns over time.
Calibration history feeds back into conflict detection and context retrieval. Future decisions start with assumptions grounded in what actually happened — not just what was hoped for.
The share of predictions whose recorded outcome matched expectation. A well-calibrated team converges above 65% over time and can see themselves improving.
How far stated confidence diverged from reality on average. Trending toward zero means the team is learning from evidence rather than intuition.
A per-prediction score from −1 to +1. Aggregated by decision domain, it surfaces where assumptions are consistently off — before those assumptions compound.
Each conflict caught before execution is rework that never happened. The calibration dashboard tracks the cumulative estimate, decision by decision.
Most teams already have a notes tool, a project tracker, and at least one AI assistant. None of them own decision memory. Setlyr fills that gap: a governed record layer that feeds context to everything else without becoming the thing people resent maintaining.
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.
Predictions captured at decision time are scored against recorded outcomes. Calibration history — hit rate, mean confidence error, variance by domain — feeds back into conflict detection and retrieval weighting. The system is measurably more accurate after 50 decisions than after 5.
When team members transition or communication tools change, context usually leaves with them. Setlyr ensures the organizational memory stays intact, queryable, and independent of individual inboxes.
Routine decisions flow with a single click. Consequential ones get full evidence, review scheduling, and approval gates. The system matches ceremony to stakes.
Setlyr is a structured record layer, not an ambient monitoring tool. It captures what your team chooses to share — not what it can read.
Setlyr does not request OAuth access to inboxes or calendars. Context enters through explicit team actions: structured input, Slack, webhooks, and document uploads.
Every record, query, and AI-assisted action is scoped to a single workspace. Cross-organization data access is architecturally impossible, not just a policy.
No briefing reaches an external stakeholder and no status transition executes without a logged human approval step. AI suggests; people decide.
All connections are HTTPS-only with HSTS enforced. Sensitive fields — briefing drafts, stakeholder identifiers, channel credentials — are encrypted with AES-256-GCM before storage. Secrets are managed via AWS Secrets Manager, not environment variable files.
Workspace admins can export a full audit log at any time in CSV or PDF format. Complete workspace data export — decisions, evidence, stakeholders, and briefings — is in development. We will not hold your records hostage.
We're working with a small number of teams to understand where important decisions get lost, contradicted, or acted on unsafely. We care most about environments where AI is starting to shape 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.
We're selective about fit. We want conversations that surface real friction, not polished validation.
A working session is 60 minutes, no setup required on your side. We map two or three real decisions your team has made recently, trace where context was lost or contradicted, and show what the record layer would have looked like.
If your team is feeling the operational friction of fragmented context and unmanaged commitments, grab some time to explore whether decision infrastructure makes sense for your workflow.
45 minutes with the founder. We've had this conversation with software teams, advisory firms, and operations leads. If it's not a fit, we'll tell you.