Automatic learning events
Events flow in from GTM experiments, Decision Loop reviews, Investor CRM deals, Jobs & Recruitment outcomes and completed Outcome Workflows.
Learning Engine: what your company learned, with the receipts
Every finished GTM experiment, reviewed decision, closed investor deal, hiring outcome and completed outcome workflow becomes a learning event. The engine matches similar past actions, distills the lesson, and proposes recommendations that link back to the records they came from. It learns only from your own organization's data.
- Event sources5 modules
- Action types8
- Data scopeYour org only
Learning Engine
Planning & Strategy
Active
12
This month
+24%
Status
On track
What is Learning Engine?
Institutional memory software keeps what a company learned, so the next decision starts from evidence instead of someone's recollection. Nautis Learning Engine records finished work from five modules as learning events, matches similar past actions, distills lessons, and proposes recommendations that link to the records behind them. You accept or reject, then record whether it worked.
Startups forget fast. The person who ran last spring's pricing test has left, the notes sit in a doc nobody can find, and the team runs the same test again. The Learning Engine doesn't ask anyone to write lessons. It collects them from work that already has a result: a GTM experiment completed with a result, a decision reviewed in Decision Loop, an Investor CRM deal that closes won or passed, a candidate hired or rejected in Jobs & Recruitment, and a completed Outcome Workflow.
Each learning event records the action, what you Expected against what was Actual, the Metric, a distilled Lesson and a suggested Next step, with a link back to the original record. Open one and the Similar past actions panel shows matched events with a "% match" score. Filter events by action type, from Campaign and Pricing change to Hiring and Runway action, or by outcome: Win, Loss, Mixed or Inconclusive.
Recommendations appear only when the match is strong enough. Each shows a summary, a rationale and a Sources box with the exact records it's based on, which is the main reason to trust it or not. The engine never makes an unsourced claim, and it learns only from your own organization's data, never from other companies.
Built for
- Founders who want to stop repeating the same mistakes
- Growth teams running many experiments
- Teams bringing new hires up to speed on past calls
Capabilities
What you can do with Learning Engine
Everything ships out of the box. No setup, no plugins, no extra tools.
Expected vs actual
Each event shows the action, the expected and actual result, the metric, a distilled lesson and a suggested next step.
Similar past actions
Every event lists matched past events with a % match score, their outcome, their lesson and a link to the source.
Source-linked recommendations
Each recommendation has a rationale and a Sources box linking to the exact records it's based on.
Closed feedback loop
Record whether an accepted recommendation worked, and that answer becomes a new learning signal.
Use cases
How startups use Learning Engine
Before repeating a channel test
Planning another paid experiment in the GTM Planner? Filter Learning Events by Experiment or Campaign and read what similar tests taught. If the last attempt was a Loss, the lesson and its source show why before you spend the budget again.
Checking your own judgement
Decisions reviewed in Decision Loop arrive with expected and actual outcomes side by side. After a few months, the pattern across your hiring or pricing calls is more honest than memory.
Learning from investor passes
When a deal closes as passed, it becomes an event. Several passes with a similar lesson is a signal to act on before the next batch of meetings; the guide to handling investor passes covers what to do with it.
Closing the loop on a recommendation
Accept a recommendation, act on it, and come back weeks later to click Record realized outcome. Choose It worked or Didn't work, add a note, and the answer becomes a new learning event. The Worked chip counts the ones that paid off.
Quick start
From zero to using Learning Engine
- 1
Finish work elsewhere
Complete an experiment with a result, review a decision, close a deal or a hire. Nothing is created here by hand.
- 2
Review learning events
Filter by action type or outcome, and open an event to see its lesson and similar past actions.
- 3
Accept or reject
Read the rationale and sources on a recommendation, then decide. Nothing runs automatically either way.
- 4
Record the realized outcome
Come back later, choose It worked or Didn't work, and the engine learns from your answer.
How AI is used in the Learning Engine
A short AI call in the background distills each lesson, using a small amount of AI credits automatically. If the Learning Engine's AI toggle is off for your organization, or credits run out, events still arrive with their raw details. You just don't get the polished lesson.
Nothing here acts on its own. Accepting a recommendation records your decision, and nothing is executed. The engine stays on the advice side of the line, and the choice stays with you.
Institutional memory software vs a wiki vs retrospectives
A wiki and a good retro both capture things the engine can't. The engine's edge is that it needs no extra writing and always shows its source.
| Need | Team wiki | Retros and postmortems | Nautis Learning Engine |
|---|---|---|---|
| Captured without extra writing | No | No, someone runs the session | Yes, from finished work in five modules |
| Linked to the original record | If someone adds the link | Sometimes | Always, with a source link |
| Expected vs actual | Rarely | Often discussed | Recorded on every event |
| Finds similar past situations | Search, if you know the words | Memory | Similar past actions with % match |
| Covers context nobody logged | Yes, any topic | Yes | No, only what was recorded in Nautis |
| Recommendations | No | Action items | Source-linked, accept or reject |
Plans and limits
- Browsing events needs read permission for the Learning Engine. Accepting or rejecting recommendations and recording outcomes need update permission.
- Lesson distillation uses a small amount of AI credits from your monthly allowance: 1,000 on Launch, 2,000 on Voyage and 2,600 on Fleet. See pricing.
What Learning Engine doesn't do
- It's quiet at first. With no completed experiments, decisions or deals, there's nothing to match, and the empty state says so.
- It only knows what was recorded in Nautis. A lesson from a hallway conversation or a tool outside the workspace won't show up.
- You can't write standalone lessons. That keeps every claim sourced, but free-form knowledge still belongs in a wiki or in Notes.
- Patterns from a short history can mislead. Check the sources and match scores, and reject recommendations that rest on one or two events.
- It doesn't pool data across companies, so there are no outside benchmarks here.
Learning Engine questions
What is institutional memory software?+
It's software that keeps what an organization learns, so knowledge survives people leaving and time passing. Most tools rely on someone writing things down. The Learning Engine builds memory from finished work with recorded results, and links every lesson to its source.
How long before the Learning Engine is useful?+
It depends on how much you finish with a recorded result, not on calendar time. A team that completes a handful of GTM experiments and reviews its decisions will see matches sooner than one that only plans. Start by recording results on work you've already finished.
Which action types does it track?+
Eight: Campaign, Pricing change, Investor outreach, Product launch, Hiring, Runway action, Experiment and Decision. Outcomes are tagged Win, Loss, Mixed or Inconclusive, and you can filter by either.
What does Superseded mean on a recommendation?+
A newer proposal replaced it. The other statuses are Proposed, which needs your decision, Accepted and Rejected. Realized outcomes show as Worked or Didn't work badges.
Does the Learning Engine replace a retrospective?+
No. A retro is where a team argues about why something happened, and that conversation still matters. The engine is where the result and the lesson stay findable afterwards, linked to the record.
Why is everything empty?+
The engine only has something to say 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. Accepting just records your decision. Nothing is executed automatically.
Can I write my own lessons here?+
No. Every lesson traces back to something that actually happened in another module, which is the point of the engine.
Is the engine learning from other companies' data?+
No. Matching and recommendations are computed only from your organization's own records.
Does it use AI credits?+
A small amount, automatically, to distill lessons in the background. If the Learning Engine's AI toggle is off or credits run out, events still arrive with their raw details, without AI-polished lessons.
Can I trust a recommendation?+
Check its Sources box. Every recommendation links to the past events it's based on, with match scores. If the sources don't convince you, reject it.
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