KPI snapshot
Occ, ADR, RevPAR, Pickup, OTB vs RY, STLY, and forecast confidence — reconciled across your property data.
The product
One AI-Powered Brain.
Three decision surfaces.
From the 6 AM inbox to the 4 PM deep-dive, Pivot Sigma meets revenue managers where they already work — and gives every team member the same source of truth.
06:00 · Inbox
Open one email. Know exactly what to do today.
A 2-minute morning briefing that replaces pickup spreadsheets, rate-plan screenshots, and the overnight “what changed?” thread.
Revenue18.14B+16.5%
Occ65.0%+8.4%
ADR4.73M+7.4%
RevPAR3.07M+16.5%
| Window | Status | OTB | Gap | Action |
|---|---|---|---|---|
| 0–7d | Watchlist | 60.5% | 119 | Direct flash · AP-7 |
| 8–14d | Need volume | 52.1% | 206 | Open AP weekdays |
| 15–30d | Need volume | 45.9% | 693 | Group + MICE outreach |
Executive readout
Current · Risk · Opportunity · Strategy. Written like a senior RM, not a generic chatbot.
Priority windows
Every date range gets a status, room-night gap, yield stance, recommended action, owner, and follow-up.
| Question | Current reading | Decision |
|---|---|---|
| Where is pace soft? | 08–14d weekday shoulder · gap 206 rn | Open AP weekdays |
| Where is compression? | Fri/Sat approaching 80% forecast | Close DISC · MLOS 2 |
| Which mix is dragging? | Discount segment +3 pts · ADR dilution | Cap discount exposure |
| What could break forecast? | Group materialization 76% | Wash tentatives 24% |
The cockpit
A live cockpit for RMs who think in deltas.
Pivot Sigma is built around the seven questions that drive every yield meeting — pace, mix, room type, compression, comp-set stance, booking window, and forecast confidence.
Ask anything
The RM you would hire if you could clone your best one.
A conversational assistant trained on hotel revenue management. It reads years of pace, pickup, pricing, and segmentation history — then answers with a recommendation you can act on.
Why is 8–14d pace soft?
Forecast gap of 206 room-nights, mostly weekday shoulder. Open AP weekdays, protect weekend ADR, and keep premium rooms fenced.
Should I close discount on May 22?
Yes. OTB is tightening and pickup is ahead. Close DISC, raise BAR +5%, and apply MLOS 2 on the peak night.
Million-token context
Long booking histories can be read in one inference, so seasonality, wash, lead-time shifts, and curve breaks stay visible.
RM-native vocabulary
BAR, LRA, AP, MLOS, CTA, GOPPAR, RGI, displacement, wash, and hurdle rates are first-class concepts.
Citable answers
Every recommendation is traceable to a metric, date range, segment, or room type — not a vague model opinion.
From signal to action
Every insight comes with an owner and a date.
| Priority | Action | Owner | Follow-up |
|---|---|---|---|
| P1 | Drive occupancy with AP + selected channel support | Commercial + Sales | 2026-05-15 |
| P2 | Protect ADR and premium room types | Front Office + Distribution | 2026-05-14 |
| P1 | Cap discounting on compression nights | Distribution | 2026-05-22 |
Under the hood
Real data in. Decisions out.
Pivot Sigma is built on four principles: real hotel data, a model tuned for revenue management, million-token historical context, and decision-ready recommendations.
01
Connect to your real hotel data.
We connect to PMS, RMS, channel manager, benchmarking, rate-shopping, direct booking, and market-intelligence feeds. No representative samples. No spreadsheets.
02
Apply an RM-tuned model.
The model understands BAR, LRA, AP, MLOS, CTA, wash, displacement, compression, segment dilution, and booking-window behaviour.
03
Mine years of history in context.
Million-token context lets the model compare today’s pace against long booking histories, seasonality, channel mix, and prior yield actions.
04
Produce decisions, not noise.
Every insight becomes a recommendation with priority, action, owner, follow-up date, and confidence.
Architecture & data security
Your data is yours. Nobody else’s.
Pivot Sigma will never sell, share, or expose your property data to any third party, competitor, or other customer. Your data exists inside your tenant, used only to reason about your property.
Property data
PMS · RMS · channel manager · benchmarking · rate shopping · market intelligence · direct booking.
RM brain
Secure data warehouse, revenue-management tools, million-token historical context, and auditable recommendation logic.
Decision surfaces
Daily RM Brief, RM Studio dashboard, and RM AI Assistant — all saying the same thing.
No cross-customer training. Ever.
Your property data is never used to train a shared or multi-tenant model. It is never pooled with competitors. It is never sold to benchmarking vendors, rate-shoppers, or analytics resellers.
Your PMS is not ours to touch.
Pivot Sigma reads your data. It does not push rates, inventory, or group holds without explicit, scoped, human confirmation — and it produces an audit log every time it does.
See it on your data.
We will connect a sandbox to real property data and show the first three revenue actions Pivot Sigma would recommend.