Setlyr Request a working session
The verifiable decision layer for AI-native teams

Before your agents act, one check: is this decision still true?

When agents act, they act on whatever context they can find. Setlyr is the verifiable decision layer: one query tells your people — and your agents — whether a decision is still true, conflict-free, and human-confirmed before anyone acts on it.

Agents can ask: is this still true? Conflict checks run on rules, not guesswork Calibration loop that gets sharper over time
Request a working session How it works
The agent-era problem

AI is starting to act on operational decisions. The record behind those decisions is still scattered.

Unmanaged decisions have always been expensive: silent reversals, cross-team contradictions, teams acting on stale context. AI adds pressure because it can act on that fragmented context much faster than people can reconcile it. Without an authoritative decision record, there is nothing stable for humans or agents to check against.

Scenario

“Your procurement agent committed to a vendor. The decision to freeze that vendor relationship was made in a Slack thread last Tuesday. The agent didn’t know.”

Procurement
Decision drift

Stale, conflicting, or undocumented assumptions lead teams and agents toward the wrong operating instructions. One outdated commitment can spread across automated work before anyone notices.

Decision drag

Approvals still move through meetings, tickets, and follow-ups while automated work moves in seconds. When governance cannot keep up, teams either wait, improvise, or work around the process.

Decision debt

The accumulated weight of untracked commitments across Slack, calls, documents, and email. Old context keeps influencing new work, even when the business has moved on.

Decision exposure

When automated action crosses a boundary — unauthorized commitment, stale constraint, missed approval — the organization carries the consequence. Accountability cannot be delegated to a system that had no record to check against.

What we're building

The record humans stand behind and agents query before they act

Setlyr connects human leadership boundaries to autonomous execution loops. Capture what was decided, trace every mandate back to evidence, and enforce limits across every channel.

Conflict check

Catch contradictions before they execute

Compare decisions across time, assumptions, constraints, and dependencies. Surface reversals, policy collisions, and resource conflicts early enough to stop work from branching in the wrong direction.

  • Deterministic conflict checks State, constraint, and timeline rules run before model reasoning.
  • Priority drift detection Highlights when a new action contradicts the policy or commitment it depends on.
  • Readable explanations Reviewers can see which rule fired and which decision records were involved.

Capture decisions where work happens

Pull decisions from channels people already use: meetings, Slack, email, documents, and system events. Normalize them into structured decision candidates with provenance.

  • Decision-first canonical objects
  • Evidence-linked records instead of loose text
  • Human confirmation for consequential promotions

Route the right decision to the right actor

Render the same decision differently for executives, operators, clients, and AI systems. Add approval gates, permissions, and machine-readable context for safe execution.

  • Role-aware briefing generation
  • Governed human + AI action permissions
  • Calibration loops that improve future judgment
Operating Loop

From fragmented signals to governed execution.

CAPTURE channels → candidates NORMALIZE structure + provenance COMPARE conflicts + drift ! ROUTE briefings + governed capture normalize compare route
Capture Extract candidate decisions from real work without changing the team’s daily flow.
Normalize Turn raw signals into decision objects with status, constraints, ownership, evidence, and confidence.
Compare Detect contradiction, overlap, drift, and reversal before work branches in the wrong direction.
Route Deliver the right briefing or machine-readable context to each actor with approval and auditability.
The check before action

Your agent asks one question. Setlyr answers before any work starts.

Expose your decisions as an endpoint agents and copilots can query — over MCP or a simple API. Before it acts, an agent asks whether the decision it's about to execute is still true, conflict-free, and human-confirmed. If it isn't, the agent is told why — and stops. This verifies decision validity; it is not a runtime access gate.

Agent asks

Is this decision still safe to act on?

POST /mcp/decisions.check
{
  "action": "commit_vendor",
  "vendor":  "Acme Logistics",
  "agent":   "procurement-copilot"
}
Setlyr answers

Not yet — here's why

{
  "act": false,
  "status": "BLOCKED",
  "reason": "conflicts_with_canonical_decision",
  "conflicts_with": "Freeze Acme vendor relationship",
  "decided": "2026-05-19 · approved by H. Okafor"
}
Who it's for

Built for teams deploying AI into real operations

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.

Scaling software and product teams

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.

Hypergrowth alignment Priority drift Engineering rework AI coordination strain

Professional services and advisory firms

Decisions move between calls, deliverables, stakeholder updates, and follow-up tasks. Contradictions become visible to clients fast.

Client handoff quality Decision traceability Briefing accuracy

AI-native edge teams

Teams are standing up fast autonomous workflows beside core operations. Without a shared decision memory, those edge systems can act on stale pricing, old vendor guidance, or assumptions the business has already changed.

Edge context drift Stale operating rules Unauthorized commitments Control gaps
What good looks like

Measure what you avoided, not just what you produced.

Decision infrastructure reframes decision health as avoided damage — contradictions caught, reversals surfaced, stale commitments flagged before they become rework. These are the metrics a team running Setlyr would track; each one traces back to a specific incident you can inspect.

Contradictions caught

e.g. three conflicts caught before stakeholder delivery — surfaced before damage, not buried in a retrospective.

Silent reversals surfaced

e.g. two reversals detected within 48 hours — decisions that quietly changed direction, now visible.

Stale commitments flagged

e.g. twelve decisions past review date — outdated context caught before it drove wrong execution.

Coordination cost avoided

Every conflict caught is rework that never happened. The platform estimates avoided hours from each detection.

Calibration Loop

Every decision improves the next one.

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.

Predict

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.

Observe

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.

Score

Variance between prediction and outcome is computed and scored. Systematic overconfidence, blind spots, and domain-level assumption errors become visible patterns over time.

Improve

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.

Hit rate

The share of predictions whose recorded outcome matched expectation. A well-calibrated team converges above 65% over time and can see themselves improving.

Confidence gap

How far your stated confidence diverged from what actually happened, on average. Trending toward zero means the team is making calls grounded in evidence, not optimism.

Domain accuracy

Accuracy broken down by decision type — hiring, product, pricing, vendor. Shows you which areas of the business are producing reliable judgment and which are systematically off.

Avoided cost estimate

Each conflict caught before execution is rework that never happened. The calibration dashboard tracks the cumulative estimate, decision by decision.

What makes this different

Bring decision clarity into the tools your team already uses.

Most teams already have a notes tool, a project tracker, and at least one AI assistant. Notes tools remember what was said; memory layers store and recall but won't tell you a decision was reversed. Setlyr fills that gap: a governed record layer that feeds context to everything else without becoming the thing people resent maintaining.

Works across existing channels

Capture decisions from messages, voice, documents, and system events without forcing the team into a new daily workflow.

Built for action, not just storage

Turn decisions into structured records with owners, evidence, constraints, and follow-through instead of burying them in notes.

Safer AI and clearer accountability

Give humans and AI systems better context, approval gates, and audit trails before decisions become downstream actions.

Gets smarter the more you use it

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.

A decision record that outlasts team churn

When team members transition or communication tools change, context usually leaves with them. Setlyr keeps the decision record intact, queryable, and independent of individual inboxes.

Proportionate friction, not uniform overhead

Routine decisions flow with a single click. Consequential ones get full evidence, review scheduling, and approval gates. The system matches ceremony to stakes.

Enterprise & IT

Designed to work within your existing controls.

Setlyr is a structured record layer, not an ambient monitoring tool. It captures what your team chooses to share — not what it can read.

No email or calendar permissions

Setlyr does not request OAuth access to inboxes or calendars. Context enters through explicit team actions: structured input, Slack, webhooks, and document uploads.

Workspace isolation by design

Every record, query, and AI-assisted action is scoped to a single workspace. Cross-organization data access is architecturally impossible, not just a policy.

Human approval gates on consequential actions

No briefing reaches an external stakeholder and no status transition executes without a logged human approval step. AI suggests; people decide.

Encrypted in transit and at the field level

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.

Your data is yours to take with you

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. Your decision records should remain portable.

Design partners

We're looking for design partners

We're working with a small number of teams to understand where consequential decisions get lost, contradicted, or acted on unsafely. We care most about environments where AI is starting to shape real operational work.

What a working session covers

How consequential decisions move through your workflow today, where contradictions or version drift show up, and whether a shared decision infrastructure layer would actually help.

  • Decision flow through your current workflow
  • Where contradictions and stale context show up
  • Where AI is creating coordination or governance risk

Best fit

We're selective about fit. We want conversations that surface real friction, not polished validation.

  • Operational AI in live workflows
  • Cross-team decisions with real consequences
  • Need for sign-off and contradiction detection

What the pilot looks like

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.

  • No integration or IT involvement to get started
  • We use your real examples, not generic demos
  • You leave with a concrete view of where decision drift is costing you
Next step

Let's look at your decision flow

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.

Thanks — we'll be in touch shortly.
No pitch. Just a conversation.

60 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.