Service 03

Prediction, recommendation and action — tied to a named decision.

Move past description into prediction, recommendation, interaction and responsible automated action against a defined decision.

Discuss a Decision See Client Work

Advanced Analytics & AISchematic

AI earns its place when it shortens the distance to a decision.

The problem

A model only matters if something happens differently because of it.

Analytical sophistication is not the constraint in most organizations. The constraint is the distance between a model output and a decision someone is accountable for making.

We specify the decision, the threshold for action and the route into the operating rhythm before development begins, then build, deploy and monitor the model that supports it.

We build models against a stated decision, with the threshold for action agreed before development starts and a plan for how the output enters an operating rhythm. Methods are chosen for fitness, not fashion.

Questions we help answer

The questions this service is built to resolve.

What would we do differently if we could predict this reliably?
Which decisions should be recommended, and which can be automated?
How accurate does the model need to be to change the action?
Where should the output appear so it is used in time?
How is the model governed, monitored and retrained?

Capabilities & deliverables

What we build and hand over.

Grouped by the part of the work they belong to. Every engagement is scoped to the decisions the business has prioritized.

Model the decision

Forecasting, prediction, optimization

Forecasting and demand modeling: Time-series and driver-based forecasting with explicit accuracy targets, uncertainty ranges and a defined decision the forecast supports.
Predictive and propensity models: Risk, churn, conversion and prioritization models built for deployment, with monitoring and retraining designed in from the start.

Apply and automate

Interfaces, agents, deployment

Optimization and resource allocation: Constrained optimization for budget, portfolio, capacity and field resource decisions, with scenario comparison the business can interrogate.
LLM applications and conversational analytics: Governed natural-language access to trusted data and documents, with lineage, permissions and citation for every figure returned.
Analytics automation and agents: Automation of recurring analytical work — monitoring, exception detection, briefing generation — with human review where judgement is required.

How we work

A sequence, not a proposal.

Short increments, each one producing something usable, with ownership transferred as we go.

01
Specify
Define the decision, the action thresholds, the evaluation metric and the acceptance criteria before any modelling begins.
02
Prove
Establish a baseline, then test approaches against it on real data with honest validation.
03
Deploy
Productionize with monitoring, drift detection, documentation and clear human accountability for the resulting action.
04
Operate
Track performance against the decision it was built to improve, and retrain or retire it on evidence.

Signals this is the right work

Models perform well in evaluation and never reach production.
AI pilots have no named decision or owner attached.
Forecasts exist but planning still runs on last year plus a percentage.
Allocation decisions are made on negotiation rather than analysis.

Where this service is most often applied.

Solution 03

Portfolio & Resource Optimization

Structured allocation across portfolios, programs, capacity and field resources — with scenarios leaders can interrogate and defend.

Solution 02

Marketing Performance & Measurement

Measurement that survives executive scrutiny, and allocation decisions that follow from it rather than from channel advocacy.

Solution 04

Operational Intelligence & Automation

Exception-driven operational decisions: detect the condition, route it to an owner, and automate the analytical work around it.

Name the decision. We will build the model that improves it.

From forecasting and optimization to governed LLM interfaces, scoped to a decision with an owner and a threshold.