VERMILION · PREDICTIVE ANALYTICS

See the surge.
Keep the promise.

The engine behind Vermilion's failure predictions, pointed at claims, cash, freight and service queues. Every forecast respects your operating limits and ships with its reasoning.

Talk to the teamHow the engine works
Constrained
Forecasts obey your operating limits
Auditable
Every forecast, every driver
Five
Service sectors covered
DEMAND · 15 MIN INTERVALS
TARGET · SERVICE LEVEL
CONSTRAINT-CHECKED · LRMBREACH WINDOW · FORECAST
INSURANCE

Claims, catastrophe and loss development. Forecasts an actuary can audit.

WHAT WE FORECAST

Volumes and severities that move reserves, staffing and reinsurance decisions.

  • Claim frequency
    By line, region and peril, weeks ahead
  • Weather-driven property losses
    Freeze, hail, wind and water events
  • Loss development
    How open claims mature toward ultimate
  • Adjuster workload
    First notices of loss against desk capacity
  • Lapse and renewal
    Retention risk by segment

RULES THE MODEL RESPECTS

Insurance losses follow exposure and physics before they follow statistics.

  • Exposure-driven frequency
    Policies in force, insured values, geography
  • Peril physics
    Temperature, precipitation, wind fields, freeze-thaw cycles
  • Development patterns
    Paid and incurred triangles, reporting lags
  • Accumulation limits
    Portfolio concentration, reinsurance attachment points
SAME ENGINE

Different systems. The same discipline.

Vermilion was built to catch the signal before a failure. A missed service level, a claims spike or a jammed dock gives off the same early signal.

01 / 03

Ingest

Pull history and live feeds from the systems you already run: data warehouse, CRM, policy admin, TMS, contact-center platform. No rip and replace.

02 / 03

Constrain

Encode the rules your operation cannot break: capacity, conservation of flow, exposure, contract terms. A forecast that violates them never ships.

03 / 03

Recommend

Get a time-bounded call: which queue, which lane, which week, and why. The drivers come attached.

IN A PLANTIN A SERVICE OPERATION
Failure windowBreach window
Conservation of energyConservation of flow
Fatigue accumulationBacklog accumulation
Vibration spectrumDemand seasonality
Sensor historianWarehouse, CRM, policy admin
Reasoning traceThe same reasoning trace
ILLUSTRATED SCENARIO

Watch a breach get called early.

A billing queue, three days before cycle close. Every step is a checkable inference, not a score. Pause on any step and inspect the evidence.

FORECAST TRACEillustrated
1
SIGNAL
Billing-queue arrivals running above the weekday pattern for three consecutive intervals.
2
CONTEXT
Billing cycle closes Thursday. The last two closes produced the same ramp shape.
3
CONSTRAINT
Little's Law: at forecast arrivals and current handle time, the queue grows unless staffed agents rise.
4
FORECAST
Service level likely below target Thursday, 10:00 to 13:00. Calibrated band attached.
5
ACTION
Move cross-skilled agents from the general queue for that window. Re-forecast after Wednesday's close.
SOURCE: 1 QUEUE · 3 SIGNALSCALIBRATED BAND ATTACHED
DATA & GOVERNANCE

Your data stays yours.

Analytics touches customer and financial records, not just sensor streams. We scope data handling with your privacy and risk teams before anything moves.

Runs where your data lives.
Your cloud, on premises, or air-gapped. The same deployment options as predictive maintenance.
Personal data on purpose.
Minimum necessary fields, pseudonymized where possible, handled in line with PIPEDA and Quebec's Law 25.
Built for model review.
Every forecast is versioned with its inputs, drivers and constraint checks, so risk and audit teams can re-run it.
QUESTIONS

What operations leaders ask first.

Same reasoning engine, same reasoning trace, same calibrated confidence. What changes is the library of constraints the model is held to and the data connectors it reads from. A plant feeds it sensor telemetry and failure physics. A contact center feeds it arrivals, handle times and schedules, with queueing laws in place of fatigue mechanics.
Sometimes literally: property claims follow weather, and cold-chain losses follow heat transfer. Elsewhere the laws are operational, and just as strict. Queues obey Little's Law, ledgers balance, and a hotel cannot sell a room it does not have. The model is constrained by whichever set applies, so it will not forecast something your operation cannot physically do.
History of the thing you want to forecast, plus the drivers you believe move it, covering at least one full seasonal cycle. Typical sources are your data warehouse, CRM, policy administration system, TMS or contact-center platform. We start with exports and connect live feeds once the forecast has earned it.
We scope it with your privacy, security and risk teams before any data moves. Where possible the model works on aggregated or pseudonymized features, and it can run entirely inside your environment. Every forecast is versioned with its inputs and reasoning, which gives model-risk and audit reviews something concrete to test.
Most built-in forecasting fits a curve to history and hands you a line. Vermilion checks every forecast against the rules your operation runs on, attaches a calibrated range, names the drivers, and says when conditions have moved outside what it has seen before. You get a forecast your team can argue with.
READY WHEN YOU ARE

Bring one forecast you can't miss.

Thirty minutes with the team. Bring a queue, a book of business or a lane, and we'll walk through how the engine would reason about it.

Request a private demo How it works