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Learning Engine

Your company's institutional memory. Completed experiments, decisions, deals, and hires become learning events; the engine finds similar past actions, distills lessons, and proposes source-linked recommendations you accept or reject.

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Written by Baltej Singh

What it does

Your company's institutional memory. Whenever you finish something with a real result — a GTM experiment, a logged decision, an investor deal that closes, a hire, a completed outcome workflow — it becomes a learning event. The engine finds similar past actions, distills the lesson, and proposes source-linked recommendations. Every claim links back to the record it came from — the engine never makes an unsourced claim.

Who can use it

  • Read permission to browse; update to accept/reject recommendations and record realized outcomes.

  • Scoped to your organization only — it never learns from other companies' data.

How to get here

From the sidebar, open Strategy & Planning and click Learning Engine.

Step-by-step: find your way around

  • Recommendations — source-linked proposals waiting on your decision; the badge shows how many are pending.

  • Learning Events — what happened, and the lesson.

  • Stat chips: Pending, Events, Worked (recommendations you later confirmed paid off).

Step-by-step: feed the engine

You don't create anything here directly — events flow in automatically when you: complete a GTM experiment with a result, record a decision outcome in Decision Loop, close an Investor CRM deal, hire or reject a candidate, or complete an Outcome Workflow. Each event shows the action, Expected vs Actual, the Metric, a distilled Lesson, a Next step, and a "View source in …" link.

Step-by-step: review learning events

Filter by Action type (Campaign, Pricing change, Investor outreach, Product launch, Hiring, Runway action, Experiment, Decision) or Outcome (Win, Loss, Mixed, Inconclusive). Click an event to see its lesson and Similar past actions — matched events with a "% match" score.

Step-by-step: act on a recommendation

  1. Open a proposed recommendation. Check the rationale and the Sources box linking to the exact records it's based on.

  2. Click Accept or Reject — nothing is executed automatically either way.

  3. Later, click Record realized outcome: add an optional note and choose It worked or Didn't work. That answer becomes a new learning signal.

Tips & limits

  • Recommendation statuses: Proposed, Accepted, Rejected, Superseded.

  • Lessons are distilled by a short background AI call (small AI-credit cost). If the AI toggle is off or credits run out, events still arrive with their raw details.

  • The module is quiet at first — the more experiments, decisions, and deals you close with recorded results, the smarter it gets.

FAQ

Why is everything empty?

The engine only speaks once you've completed actions with recorded results. Start by finishing a GTM experiment with a result.

Does accepting a recommendation do anything automatically?

No — it just records your decision.

Where do lessons come from?

Your own completed work. You can't write standalone lessons — every lesson traces to something that actually happened.

Why record whether a recommendation worked?

It closes the loop — your answer is stored as a fresh learning event, so future recommendations get sharper.

Can I trust a recommendation?

Check its Sources box — every recommendation links to the past events it's based on, with match scores. Not convinced? Reject it.

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