Predictive mobility
A train's route is known before it departs, so every handover is pre-armed along the path. Near-RT RIC xApps steer beams and thresholds per train — not per statistical average.
Intelligence
One brain.
Three skies.
Railways are deterministic: known paths, known schedules, known priorities. FRMCS.ai turns that determinism into an advantage no consumer network can match — a network that knows the future and prepares for it.
The corridor twin
A living model of every kilometre.
At the centre of FRMCS.ai sits a digital twin: every train position, every cell's load, every drone's state of charge, every satellite pass, every wayside sensor reading — fused into one continuously updated model of the railway.
Everything the network does is a query against this twin: where will coverage be needed in 12 minutes? Which sky should carry this CCTV stream? Which axle bearing will fail next month?
- Inputs: timetable, live signalling, RAN telemetry, TCMS, IoT wayside, weather.
- Model: per-corridor RF map, mobility graph, asset-health state.
- Outputs: coverage plans, drone missions, bearer policies, maintenance orders.
- Loop time: milliseconds at the RIC, minutes at orchestration, days at planning.
Closed loops
Six loops, one operating model.
Drone-fleet orchestration
Every dock held mission-ready — charging, self-tests, weather holds, NOTAM compliance — with launch decisions in seconds when a mast goes dark or a new sector requests coverage.
Energy orchestration
Tower cells shape power to traffic, drones stay docked until called, and C-DAS advises drivers on energy-optimal profiles — the network and the trains save power together.
Closed-loop assurance
Anomaly detection across core, RAN and transport. Faults are diagnosed against the twin, healed by orchestration, and only then reported — with the fix attached.
Predictive maintenance
TCMS and wayside IoT feed models that see hot boxes, sticking points and degrading antennas weeks out — work orders raised before failures strand trains.
Intent-based operations
Operators declare the service plan — "12 trains per hour, GoA2, CCTV on demand" — and the intelligence layer compiles it into coverage, capacity and fleet plans.
Where it runs
Intelligence at every altitude.
| Layer | Timescale | Decisions | Standard hooks |
|---|---|---|---|
| Near-RT RIC xApps | 10 ms – 1 s | Handover steering, beam selection, scheduler policy per train | O-RAN E2 / E2SM |
| Non-RT RIC rApps | 1 s – hours | Coverage policy, drone launch schedules, energy plans | O-RAN A1 / O1 |
| Orchestration | minutes – days | NF scaling, corridor rollout, fleet logistics, closed-loop healing | ETSI ZSM · TM Forum ODA |
| On-train edge | real time | Bearer steering, local buffering, video analytics at source | TOBA · MEC |
- Explainable: every autonomous action logged against the twin state that justified it.
- Bounded: AI never touches vital signalling logic — it optimises the network under it.
- Governed: aligned to NIST AI RMF and ISO/IEC 42001 management practice.
- Human-commanded: operators set intent and can override any loop, any time.
Safety first
Autonomy with a safety case.
Railway AI must be auditable. FRMCS.ai's intelligence optimises coverage, capacity and cost — while vital train-protection logic remains deterministic, certified and untouched. The AI makes the network better; it never makes the safety decision.
See your corridor think.
Bring us a line diagram and a timetable — we'll show you the digital twin, the drone plan and the energy numbers for your corridor.