Intelligence

AI that tells you when it doesn't know

Models that rank SLA risk, pick the technician and the route, and forecast demand. They say when they don't know, and a person approves every change.

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From risk to warningvs. hours after the breach
< 90s
Interval coverage targetDurations come with real ranges
90%
Changes committed by a model alonePeople and rules decide
0

Capabilities

Every capability answers one question

Pick an area and a question to see how the model answers, where it shows up and the line it won't cross.

Getting the right person there in time

The models dispatchers work beside every day: who is at risk, who should go and how to route the day.

Seeing the week before it happens

For operations and executive leadership: where the work will come from and whether the capacity is there.

Settling the arguments with facts

When a client disputes an arrival time, the answer should come from evidence, not from memory.

Help for the people doing the work

Language models where they're useful: diagnosing, searching what the fleet already knows and writing it up.

SLA risk triage

Which open tickets are going to miss their deadline?

For assigned tickets, it projects when the technician will actually finish, from where they are, what else is on their list and current traffic. Schedule pressure and a calibrated breach probability are reported separately. Unassigned tickets are listed apart, by deadline, since nobody is on the way.

Where it appears
Console · Dispatch → Risk triage
Guardrail
Without a calibrated model it answers "unknown risk", with no probability.
waydot console: SLA risk triagewaydot console: SLA risk triage

In numbers

What the numbers look like

Two checks we run on every model: whether its probabilities match what actually happens, and how early its warnings arrive.

Predicted vs. observed breach rate

When the model says 70%, about 70% of those tickets miss their deadline.

Example calibration report on resolved tickets

Show the numbers
PredictedObserved
0–10%5%4%
10–20%15%13%
20–30%25%22%
30–40%35%33%
40–50%45%43%
50–60%55%57%
60–70%65%63%
70–80%75%74%
80–90%85%86%
90–100%95%93%

When the first risk warning arrives

Two out of three warnings arrive an hour or more before the deadline.

Example data

Show the numbers
Share of warnings
< 15 min4%
15–30 min9%
30–60 min23%
60–90 min31%
> 90 min33%

Principles

Six rules every model follows

They are why a dispatcher can act on a recommendation without double-checking it first.

01

It suggests; people decide

Every answer that could change something is a proposal tied to the record versions it was computed from. If anything moved, it asks again.

02

It says where each answer came from

Each answer says whether it came from a model, from a stated rule over real records, or couldn't be produced.

03

It says when it doesn't know

An uncalibrated model returns "unknown risk", not a plausible number. Missing traffic returns "traffic unavailable", not a straight line.

04

Fair comparisons between clients

Incidents are divided by the equipment in service, so a large client isn't mistaken for a troubled one.

05

People first

No individual attrition score is ever produced, small groups are suppressed, and uses that affect people are approved before they run.

06

Every decision is logged

Inputs, model versions and outcome, so accuracy is measured later against what actually happened.

Governance

How a model earns its place

Models and dispatch weights are promoted only when they meet published targets, and a policy change can be replayed on past days first.

  1. Log every decision

    Each recommendation is stored with its inputs, versions and the probability of every alternative, because what the options were at that moment can't be reconstructed later.

    inputs · versions · outcome

    waydot console: Log every decisionwaydot console: Log every decision
  2. Measure against what happened

    Calibration is checked on tickets that have since resolved, with a Brier score and a ten-bucket reliability curve. Declining to guess is never scored as guessing badly.

    Brier score · reliability curve

    waydot console: Measure against what happenedwaydot console: Measure against what happened
  3. Replay before switching

    A digital twin replays past days under the current and the proposed policy side by side, so the effect of a change is seen before it touches real work.

    current vs. proposed, same days

    waydot console: Replay before switchingwaydot console: Replay before switching
  4. A person promotes

    Only when every target is met, and the approval is written to the audit log. The ML service never promotes itself.

    human approval, recorded

    waydot console: A person promoteswaydot console: A person promotes
Brier score
≤ 0.12
Accuracy of breach probabilities
Calibration error
≤ 0.05
How far predicted rates drift from observed ones
Interval coverage
≥ 90%
Share of real durations inside the predicted range
Population stability
≤ 0.10
How much the data has shifted since training
Minimum cohort
30
Smallest group a model may be evaluated on
Collateral risk
≤ 5%
Most a plan may raise another ticket's breach risk

See it on your own operation

A 30-minute briefing with your dispatch, payroll and asset flows on screen.

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Keep exploring

The visibility gap

What the office can't see

What running on WhatsApp and spreadsheets really costs, and what changes with one record.

Contact sales

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  • 01A review of your current field operation
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  • 04Expected impact and clear next steps

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