FORECAST · AI INVENTORY INTELLIGENCE

Monday morning.
Every shelf accounted for.

Forecast watches every walk-in cooler, dry-storage shelf, and POS ticket in real time — then delivers purchase orders before your manager opens a spreadsheet.

CLIENTS INCLUDE
· Multi-unit operators· Hotel F&B directors· Distributor sales teams
FUEGO TACO GROUP · 40 UNITS · LIVE
// 7-DAY DEMAND FORECAST — COVERS
Mon
Tue
Wed
Thu
Fri
Sat
Sun
AI Forecast
Prior Actual
MAPE: 2.3%
// SKU-LEVEL ORDER QTY · MONDAY DELIVERY
Ground Beef 80/20
████ lbs
Avocado Case 48ct
████ cases
Corn Tortilla 6"
████ units
Cotija Cheese
████ lbs
Jalapeño Fresh
████ lbs
Run a Forecast on Your Data →
// MODULE SPEC · REV 4.2
// MODULE-01 · DEMAND ENGINE
97.7%
Avg Accuracy

Gradient-boosted forecasting trained on 847M restaurant transactions

ALGORITHMXGBoost + LSTM ensemble
TRAINING DATA847M transactions, 6-yr window
REFRESH CADENCEIntraday, every 4 hours
SIGNAL INPUTSPOS, weather, events, seasonality
SKU GRANULARITYItem-level, not category
// MODULE-02 · INTEGRATION LAYER
140+
Native Connectors

Connect your stack in 48 hours — POS, distributor EDI, IoT sensors

POS SYSTEMS
ToastSquareAlohaMicrosRevelLightspeed
DISTRIBUTORS / EDI
SyscoUS FoodsPFGGordon FoodReinhart
IOT / SENSORS
Walk-in temp sensors, smart scales, RFID shelf tags — MQTT + REST
// MODULE-03 · ALERT LOGIC
< 4 min
Alert Latency

Stockout probability triggers and waste threshold automation

STOCKOUT TRIGGER≥ 15% probability flag
WASTE THRESHOLDConfigurable per SKU, ±5%
REORDER AUTOMATIONAuto-PO to distributor portal
ESCALATION PATHSMS → Email → Slack → ERP
OVERRIDE WINDOW2-hour manager review period
// MODULE-04 · CASE STUDY · BLAZEBURGER GROUP · 60 LOCATIONS · 18-MONTH DEPLOYMENT● VERIFIED RESULTS
31%
Waste Reduction
From $218K annual spoilage to $150K — verified against prior 12-month P&L. Primary drivers: beef trim and produce over-ordering eliminated.
74%
Stockout Incidents Down
86'd menu items dropped from 4.2 per location per week to 1.1. Saturday night service reliability score jumped from 71% to 96%.
12 hrs
Purchasing Labor Saved / Week
Across 60 locations, GMs reclaimed an average of 12 hours per week previously spent on manual order sheets and distributor calls.
DEPLOYMENT TIMELINE
Month 0
Pilot: 3 locations
Month 1
Model accuracy confirmed: 96.2%
Month 2
Full fleet: 60 locations live
Month 6
Waste -18%, stockouts -41%
Month 12
Waste -27%, stockouts -66%
Month 18
Waste -31%, stockouts -74%
Full methodology, raw P&L data, and GM interview transcripts available.
// MODULE-05 · SECURITY & COMPLIANCE
SOC 2
Type II Certified

Enterprise-grade data governance with zero-trust architecture

CERTIFICATIONSOC 2 Type II, ISO 27001
ENCRYPTIONAES-256 at rest, TLS 1.3 transit
DATA RESIDENCYUS, EU, APAC — tenant isolated
ACCESS CONTROLRBAC + SSO (SAML 2.0, OIDC)
AUDIT LOGS365-day immutable trail
// MODULE-06 · DEPLOYMENT
14 days
Pilot Go-Live

Pilot live in 14 days. Full rollout to 100 locations in under 6 weeks

Day 1–2POS & distributor API keys provisioned
Day 3–5Historical data ingestion + model warm-up
Day 6–10Pilot location forecasts running live
Day 11–14Manager dashboard training + sign-off
Week 3–6Full fleet rollout, location by location
// MODULE-07 · PRICING
$0
Per-Seat Fee

Per-location tiers. No seat licenses. No surprise overages.

GROWTH$89/loc/mo
15–49 locations
SCALE$71/loc/mo
50–99 locations
ENTERPRISECustom
100+ locations

Includes all modules, unlimited API calls, 24/7 support.

// LEAD INTAKE · STEP 1 OF 3

Tell us about your operation.

We'll scope a forecast model to your exact fleet before the call.

Your organization · — locations · POS TBD