The pitch
Conductor is decision execution infrastructure.
The full argument, in deck order. Ten sections, no filler.
A decision made on Tuesday is gone by Friday.
A head of operations decides in a Tuesday meeting: cut the Q3 paid budget by 40%. The rationale lives in her head. The instruction goes into a Slack thread. The tickets never get created. By Friday, marketing is still spending, finance hasn't sent the final number, and the replacement plan is a bullet point in someone's notes app.
This happens constantly, and it's expensive. Not because people are careless — because turning a decision into correctly-assigned, tracked, cross-tool work is manual, inconsistent, and untraceable. Accountability quietly evaporates.
Every tool stops where the decision ends.
Notion stores knowledge. Jira stores work. Slack distributes messages. Zapier moves structured data on deterministic triggers. ChatGPT generates ungoverned text. Each is good at its job — and each stops exactly where accountable action starts.
Decision execution infrastructure is the layer between a decision being made and the work it causes being tracked, owned, and done. This is a structural gap, not a feature gap. Nobody owns it because the underlying capability — reliable structured output from messy human intent — only recently became real.
The timing is real, not narrative filler.
Structured output from frontier models crossed a usability threshold in roughly the last twelve months. Unstructured intent can now become typed, executable structure reliably enough to trust with real actions. Every incumbent category was designed before that was true.
And as AI agents proliferate, organizations will need a governed execution plane between intent and action — one with guardrails and audit trails. Conductor is MCP-native from day one, built to be that plane.
One directive in. Owned work out.
This is the live loop, working today. A real directive, decomposed by the pipeline — sanitize, triage, synthesize, guardrail, execute, learn.
“We're cutting the Q3 paid budget by 40% — tell marketing why, get finance to send the final spend number, and draft the replacement plan for review.”
directive · via SlackMessage to #marketing explaining the cut and the reasoning behind it.
Request to finance for the final Q3 spend number, due before the plan review.
Replacement plan drafted and flagged for the requester's review, with a deadline.
The whole loop, end to end, in 70 seconds: watch the demo →
Within seconds of approval, the Slack messages exist and the review card has an owner and a deadline. Two days later, the Morning Briefing summarizes what happened, what's still open, and why the decision was made — the memory loop, not just the execution loop.
Not another AI wrapper.
Drafts vs. executes
Adjacent tools summarize meetings and suggest next steps. Conductor's guardrail pipeline actually creates the ticket and sends the message. The work exists in the system of record, not in a paragraph.
Ungoverned vs. guardrailed
A single prompt acts on hope. Conductor runs every action through a policy check first — internal work can auto-execute after approval, external communication always waits for a human.
Stateless vs. decision memory
Every approved or rejected card teaches the system this team's judgment — who owns what, what gets escalated, what “urgent” means here. Per-workspace, compounding, and it doesn't transfer on switch.
A buyer who feels this pain weekly, by name.
Operations leads, engineering managers, and chiefs of staff at 20–150 person companies running Linear/Jira + Slack, where cross-functional dependencies currently get lost between meetings, threads, and tickets.
Focused, not “everyone who manages a team.” This buyer already has budget for coordination tooling — Conductor displaces existing workflow-automation and manual-ops spend rather than creating a new budget line.
Land with one lead. Grow seat by seat.
Conductor lands with one ops or eng leader routing their own decisions. Teammates start receiving Delegate cards. Then they start submitting their own directives. That's organic, usage-driven expansion — not a top-down enterprise sale.
Once decision-memory volume is real, the roadmap layers in org-wide rollup briefings, approval policies, and cross-team dependency detection — moving upmarket into Ops/PMO. The long-range destination is full intent infrastructure. That's the five-year story, sold on evidence, not on day one.
A thesis being tested — stated as one.
The moat is not the AI, and not the integrations. Those are copyable within a quarter by any well-funded competitor.
What compounds is per-workspace decision memory: every approved or rejected card teaches the system a specific team's judgment. That memory gets more valuable the longer a team stays, and it doesn't transfer if they switch tools. Combined with an execution history that becomes the org's audit trail, that's a real switching cost.
The second, longer-horizon bet: if the agent ecosystem needs a trusted execution plane, being the MCP-native plane other agents call through is a network-effect position. Both are described as theses under test — the current raise exists to measure the first one.
What's de-risked, and what isn't.
De-risked
- The pipeline executes end-to-end today — real tickets and messages in Linear, Slack, Notion, and Gmail.
- Decision memory is live: pgvector + HNSW, sub-50ms retrieval.
- Unit economics look workable at $0.0004 average model cost per directive.
- The why-now is structural, not narrative.
Not yet de-risked
- Product-market fit. No design partners against the new ICP yet — recruiting five to eight now.
- The moat. Decision-memory compounding is a thesis until retention data exists.
- The hours-reclaimed figure. 15+ hrs/week is modeled, not measured.
- Team. Solo founder today, one engineering hire planned as first use of funds.
The single highest-leverage thing happening at this company is converting the modeled claims into measured ones with real design partners. That is exactly what the raise funds.
$50,000, sized to the proof it's buying.
A small pre-seed round, and the size is deliberate. It funds eight weeks: ship the core loop into five to eight design partners' real workflows, instrument decision-memory usage and retention, and convert the result into a one-page proof doc — measured retention, time-to-first-value, seat-expansion signal.
What “yes” gets you: entry into a decision-execution infrastructure company at the proof stage, and the measured data that replaces every modeled claim on this site. A seed round sized to evidence comes after the proof exists — not before, and not pitched alongside Series-A-in-6-months language that a $50K ask can't support.