Decision intelligence

Decision intelligence, with a person still accountable.

We start from the operational data you already keep. We build only the forecast, ranking or allocation that is expensive when it is wrong, and a person still confirms the call.

  • DataWhat you already record
  • The decisionForecast, rank or allocate
  • A personSees why, and can override

Decisions

Forecast, rank, allocate

One repeated choice, wired to the records you already trust, with the reason left on the screen.

See the three parts

Where a decision is still a spreadsheet

These are the situations we are asked to fix. The first slice is the choice that costs the most when it is wrong.

Habit

Gut and spreadsheets

The same choice is remade from a sheet. The trade-off is never written down.

Forecast

A number nobody trusts

The forecast exists. Nobody can see what it used, so the team ignores it.

Ranking

A list with no reason

Work, stock or leads are ordered, and no one can say why this one is first.

Allocation

Assignment by habit

People, stock or jobs are assigned the way they were last week, not against the constraint that matters.

Reports

A dashboard, not a choice

Charts explain what happened. They do not say what should happen next.

Control

No way to override

A model writes the answer and a person cannot see it, stop it, or record a different call.

How a decision actually gets built

Three parts. A new model is the last option, not the first.

1 · Data

Use records you already keep

Orders, stock, jobs or capacity from the system you run. We do not ask for a new warehouse of data before the first decision.

2 · The decision

One forecast, rank or allocation

Rules where the rule is enough. A mathematical method where the constraint is clear. A model only where the pattern is not a rule. The reason stays visible.

3 · A person

Confirm, or override, on the record

The recommendation shows what it used. A person accepts it or records another call. We do not promise the model is right, and we do not hide the alternative.

What you can check

Each step leaves something you can open.

The decision
Which choice we will improve, and which ones we will not touch yet.
The inputs
The records the recommendation reads, named before it is built.
The method
Rule, mathematical method, or model, and why that one.
The evidence
What the screen shows so a person can see why this answer appeared.
The override
How a person stops or replaces the recommendation, and where that is stored.
The comparison
How we will tell whether the new choice is better than the old habit. No lift figure is promised up front.

Questions before you decide

The contract still sets the exact terms.

What is decision intelligence?

Decision intelligence combines data, rules, analytics and models to help organisations design, automate and improve business decisions.

Is this the same as business intelligence?

No. BI primarily explains what happened. Decision systems help determine what should happen next.

Do these systems need AI?

Not always. Many important optimisation and scheduling problems are better solved with deterministic mathematical methods.

Can decisions remain explainable?

Yes. Rules, constraints, alternatives and model outputs can be retained as decision evidence.

Can these systems work in real time?

Yes, depending on the complexity of the optimisation and available infrastructure.

Tell us the decision that is expensive when it is wrong.

We reply with whether your current records are enough, what the first slice is, and where a person must confirm.

Discuss a decision