STATUS -> ACTIVE BUILD
Decisions don't disappear.
They just never get recorded.
We detect decisions as they emerge across chat, code, and incidents - and turn them into durable execution context automatically.
•Decision Detection
•Decision Artifacts
•Incident Context
•Execution Alignment
Decisions don't disappear.
What we're building toward
Core capabilities that turn informal decisions into durable execution.
Signal Gathering
Continuously monitors organizational signals across chat, code, and workflows to detect when decisions are forming - not just when they're announced.
Decision Artifacts
Automatically creates structured decision records from real activity - capturing intent, authority, context, and timeline without manual documentation.
Incident Context
Preserves decision context during outages by reconstructing what changed, who decided, and why - even under pressure.
Task Coordination
Routes follow-up work based on decision context and team structure, so execution matches what was actually decided.
Built for teams using
Decisions don't fail loudly.
They fail by being forgotten.
In modern teams, critical decisions emerge in Slack threads, GitHub comments, and incident calls.
They're agreed on implicitly, executed unevenly, and rarely recorded when it matters.
Weeks later, no one remembers:
- 1.why something was done
- 2.who approved it
- 3.what tradeoffs were accepted
- 4.or whether it's still valid
So teams slow down, repeat debates, mis-handle incidents,
and ship changes without shared context - even though the decision already happened.
As teams scale, this isn't a tooling problem.
It's an execution problem.
How it works
Detects decisions as they form — without interrupting your team.
1
Aggregate organizational signals
Continuously analyzes conversations, code activity, and workflow events to understand what's being discussed — and who's driving it.
2
Detect emerging decisions
Evaluates authority, traction, and cross-team propagation to determine when a proposal is becoming an accepted decision.
3
Formalize with review
Creates structured decision artifacts with full context, participants, and timeline — always reviewable before they drive follow-up work.
4
Coordinate follow-through
Links decisions to the right people, artifacts, and next steps so execution reflects what was actually decided.
Still building.
We're in active development. The product is taking shape, but there's still work to do. If you're interested in following along or getting early access, we'd love to hear from you.
Questions?
Here's what you need to know
How does this work, at a high level?
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We observe signals your team already produces - chat messages, code changes, and references between them. When those signals indicate that a decision has actually converged, we generate a structured decision record linked to its context. During the pilot, the system stays read-only and requires no changes to how your team works.
What does it connect to during the pilot?
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For pilots, we connect to Slack and GitHub in read-only mode. We don't post automatically, modify data, or take actions without explicit approval.
Will it create noise or record things that aren't real decisions?
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The system is intentionally conservative. It stays silent unless confidence is high. During the pilot, it's better for us to miss a decision than to record the wrong one.
Who can see or edit decision records?
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By default, records are visible to the same people involved in the original context. During the pilot, records can be edited or dismissed by humans if something looks wrong.
Does this replace Jira, docs, or Slack?
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No. It doesn't replace existing tools. It sits alongside them to capture decisions that usually fall between systems.
Is this doing anything automatically in production?
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No. During the pilot, the system is observational only. There are no automatic changes, deployments, or remediations.
What kind of teams is this a good fit for right now?
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Teams that make frequent decisions in chat, care about clarity later, and are comfortable reviewing early output. This isn't a task manager or a fully autonomous system - it's an execution layer that formalizes decisions already being made.
How much effort is required from us?
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Initial setup takes under an hour. After that, the system runs passively. Feedback during the pilot helps us the system tune accuracy to your company culture.
What happens to our data?
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Pilot data is used only to run the system and for your team. We don't train cross-customer models on your private data.
What happens after the pilot?
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We review what worked, what didn't, and whether this meaningfully reduced coordination overhead for your team. There's no obligation to continue.