One decision engine.
Three operating layers.
The platform ingests roster, demand, SLA and payroll signals from your existing systems, runs them through a per-tenant decision engine, and writes scheduling, dispatch and intervention actions back into the systems your operators already use. No middleware, no parallel reality.
- 01
Forecast
Site-level demand prediction across hours, channels and skills — replacing spreadsheet rosters and gut-feel staffing curves with calibrated forecasts that close the loop with payroll.
- grain
- 15 min · per site · per skill
- horizon
- intra-day · 12 weeks
- closes loop with
- payroll · WFM · ERP
- 02
Scheduling
Optimization engine producing compliant rosters, routing and dispatch plans in seconds under labor law, contract, skill and fatigue constraints. Re-optimization fires on real-time disruption.
- constraints
- law · contract · skill · fatigue
- re-optimization
- real-time, on disruption
- solver
- deterministic · auditable
- 03
Workforce IQ
Attrition early-warning, internal-mobility matching and structured pulse feedback. Targets the single most expensive line in a labor budget: unplanned exits.
- targets
- regretted attrition
- signal cadence
- weekly · per cohort
- surface
- named senior lead
The platform sits behind your systems of record. We integrate with HRIS, WFM, payroll, time & attendance, ERP, dispatch and the point-of-sale or telemetry sources that drive demand — through documented connectors, event streams or direct database reads against client-owned infrastructure.
Outbound writes are gated behind explicit policy. Every automated action is logged in an append-only audit ledger that mirrors the client’s own retention requirements. No shadow systems.
- systems_of_record
- HRIS · WFM · ERP
- event_streams
- Kafka · webhooks
- audit_ledger
- append_only
- latency_target
- < 250 ms
- phase_01
Architecture audit
Structured read of the operating ground: process map, data quality, integration topology. Output is a written assessment, not a pitch deck.
- phase_02
Single-tenant model training
Models are trained on your data, in an isolated tenant. No cross-customer training corpus, no benchmarking pool, no secondary use.
- phase_03
Shadow deployment
The engine runs alongside the existing process for a defined period. Decisions are observed and compared before they are wired through.
- phase_04
Live orchestration
Cut-over to live operation under a named senior lead. Steady-state with quarterly written reviews against the agreed KPIs.
Reading time is the cheapest part of the engagement. The conversation starts in writing.
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