Auto Pilot Events OS

Every events company's real intelligence lives in a few heads and old spreadsheets.

An RFP lands. Someone digs through last year's quotations to remember what a stand, an LED wall or a custom counter actually costs. Pricing drifts between estimators. When a senior person leaves, years of pricing instinct leave with them, and nothing the company learned on one deal makes the next deal any smarter.

Auto Pilot Events OS is the fix, built after twelve years of watching that exact problem from the floor: a system that turns every document the company already produces into institutional memory it can act on. So pricing, proposals and planning stop depending on who happens to be in the room.

Eleven things it does without anyone touching a spreadsheet.

Ranked by how much each one actually changes a working day, most impactful first.

1. Reads any document, learns every price

   Drop in an RFP, a supplier quote, a 3D design file or a pricing sheet. It extracts every priced line item and learns from it automatically. No manual data entry, ever. Every other feature below runs on top of this one.
2. Prices new work from the company's own history, not a guess

   A machine-learning pricing model, cutting the typical pricing miss by 66% versus the raw baseline. It only ships a number when the evidence backs it: otherwise it flags the item for a human instead of guessing.
3. Turns a brief into a client proposal in seconds, not hours

   Internal cost sheets, client-facing PDFs with margins hidden, RFP compliance checks and full proposal packages, generated in the company's own house style. Work that used to be manual formatting is now a request.
4. Runs the company's finances in real time

   Cash flow projected 13 weeks out, receivables and payables tracked, pipeline revenue forecast by deal stage, a full profit and loss: computed straight from real event data, with no spreadsheet upkeep.
5. Talk to it: type or speak, it does the work

   A natural-language assistant plans and runs the right chain of tools itself and narrates each step as it goes, with voice input and output built in. No training required to operate a 95-tool system.
6. Seven specialist agents, each boxed into its own job

   An intake clerk, a pricing analyst, a sourcing scout, a commercial drafter, a render reader, a QA verifier and a data scientist, coordinated by a director. The drafting agent physically cannot send a client file, only queue it. The risk every CEO worries about with AI, an agent acting outside its lane, is designed out at the architecture level.
7. Builds staffing plans and books resources itself

   Generates crew plans straight from a design's item list, books people and equipment with conflict detection, and turns logged hours into real labour cost: the double-booking that turns into a live-event crisis gets caught before it happens.
8. Flags the failures that actually sink events, before they happen

   Every deal within 7 to 21 days of its event date is scanned for missing suppliers, no crew assigned, unsent quote requests or unpaid deposits, and the whole business rolls up into a single 0 to 100 health score with a transparent reason behind it.
9. Builds the supplier and client database on its own

   Every quote and RFP ever dropped in becomes a scored supplier and client record automatically. No one sits down to maintain a CRM. It maintains itself.
10. Runs from free to full-scale on the same system

    One setting moves the whole thing between four cost modes, from a free laptop setup to a scaled cloud deployment at roughly $100 to $300 a month: same features, same database, nothing rebuilt to grow.
11. Keeps a full audit trail of who did what, including the AI

    Every action, human or agent, is written to an append-only log: not a bolt-on feature, the architecture itself. Daily backups and tested restores sit on top of it, along with margin floors that block a wrong AI-suggested price before it ships.

Measured on real data, not projected.

The one number I'll put a stake in the ground on, because it came out of a validation run against the company's own historical pricing corpus, not a pitch deck.

What I won't claim: no client-facing hours-saved or revenue-ROI figures yet. The system has been in production since 12 July 2026; those numbers get published once real usage produces them, measured the same way as the number above.

For anyone who wants to kick the tires.

The full engineering account: the five design principles, the governance ramp, the four cost modes, and the section on what I deliberately won't claim yet: is in the build story.

The rest of the stack.

Eighteen things published on Kaggle.

Expert on two tracks out of three, earned before the events work turned into systems work. Everything below is public, dated, and open to anyone who wants to check the code.