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AI decision support

AI that helps your team decide, and shows its reasoning.

Most companies already hold the data for better decisions: claims history, property transactions, machine telemetry, plant sensors. We build practical AI systems that turn it into forecasts, scores and recommendations your people can check and act on, and we make sure they get into daily use.

Discuss a decision you want to improve

Who we work with

  • Leadership teams that want decisions backed by data
  • Risk, pricing and operations teams
  • Companies whose AI pilot stalled
  • Regulated firms preparing for the EU AI Act
Use cases

Where AI earns its keep

Good AI projects start from a specific, recurring decision with a measurable cost. These are the ones we work on most.

IndustryDecisionApproachData it uses
InsuranceWhich claims to fast-track, which to investigate; how to price driving riskClassification, anomaly detection, scoring modelsClaims history, telematics, policy data
PropertyWhat a property is worth; which loans or policies need revaluationComparable-sales valuation models, as in our Estiq platformNotarised transactions, building register, location data
AgricultureWhere to spend inputs; how to schedule harvest and logisticsYield forecasting, optimisationField history, satellite imagery, machinery telemetry
Energy & biogasWhat to feed tomorrow; when to service the engineGas yield forecasting, predictive maintenanceProcess sensors, lab results, CHP data
Land surveyWhich anomalies to investigate firstAnomaly classification and rankingMagnetometer survey data
Any businessFinding the right answer in contracts, policies and proceduresAI assistants with retrieval from your own documentsInternal documents and knowledge bases
Common problems

What clients bring to us

If one of these sounds familiar, the answer below is where we would start.

Starting point

“We have plenty of data, but decisions are still made on gut feel.”

The way forward is to pick one decision rather than to “do AI”. Choose one that is made often, has a measurable cost when it goes wrong, and has history to learn from. Then compare a simple model against current practice on past cases before building anything bigger.

If the model beats the current approach on historical data, you have a business case. If it doesn't, you have learned that cheaply.

What we do

  • Shortlist and value candidate decisions with your team
  • Audit the data and build a baseline model
  • Back-test against past decisions to estimate the gain
Delivery

“Our AI pilot worked in the demo but never reached production.”

This is the most common outcome of AI pilots, and the cause is rarely the model. Usually no one built reliable data feeds, no one placed the output where people actually work, or no one owned the system after the pilot ended.

Production AI needs automated data pipelines, integration into existing tools, monitoring for drift, and a named owner on the business side.

What we do

  • Review the stalled pilot and what blocks production
  • Build data pipelines, integration and monitoring
  • Agree ownership, success metrics and a retraining routine
Trust

“How can we trust what the model recommends?”

People trust a recommendation when they can see why it was made. Every system we build shows its reasoning in terms the user understands: the comparable sales behind a valuation, the factors behind a risk score, the confidence range of a forecast.

Combined with back-testing, a human sign-off on consequential decisions and regular accuracy reports, that makes the system auditable as well as useful.

What we do

  • Design explanations and confidence ranges into the output
  • Set human review points for high-impact decisions
  • Report accuracy and drift to management regularly
AI assistants

“Should we use ChatGPT-style assistants on our own documents?”

For many teams, yes. Underwriting guidelines, contracts, technical manuals and procedures are where staff lose hours searching. An assistant that retrieves the relevant passages and answers with citations can save that time, provided it respects access rights and keeps data where it should stay.

The design choices that matter are which documents it may see, who may see which answers, where data is processed, and how answers are checked against their sources.

What we do

  • Select documents and define access rules
  • Build retrieval-based assistants that cite their sources
  • Test answer quality before rollout and monitor it afterwards
Regulation

“What does the EU AI Act mean for us?”

The AI Act sets obligations by risk level, and its requirements are being phased in. For our clients the key point is that some uses are classed as high-risk, including creditworthiness assessment of individuals and risk assessment and pricing in life and health insurance. Those systems need risk management, data governance, technical documentation and human oversight.

Most operational uses, such as plant forecasting, machinery analytics or document search, carry lighter obligations. Knowing which category each system falls into shapes how it should be built.

What we do

  • Classify your current and planned AI systems by risk level
  • Build documentation and oversight into high-risk systems
  • Work alongside your legal and compliance teams
How we deliver

From one decision to a system in daily use

  1. Decision framing

    Which decision, who makes it, what a wrong call costs, and how success will be measured.

    1–2 weeks

  2. Data audit & baseline

    What data exists, its quality, and how a simple model performs against current practice.

    2–4 weeks

  3. Pilot

    The model in front of real users on live cases, with an agreed target to hit.

    4–8 weeks

  4. Production & monitoring

    Integration, pipelines, monitoring, retraining and handover to your owner.

    Ongoing
FAQ

AI decision support questions

What is AI decision support?

AI decision support means using models to give people better information at the moment they decide: a forecast, a risk score, a valuation or a recommended action, with the reasons behind it. Unlike full automation, a person stays responsible for the decision, which makes the system easier to trust, audit and improve.

How much data do we need to start?

Less than most companies expect. For many decisions a few thousand historical cases with known outcomes, or one to two years of operational data, are enough for a useful first model. A short data audit at the start shows what is possible with what you already have and what is worth collecting.

Do you build custom models or use off-the-shelf AI?

Both, depending on the problem. Forecasting, scoring and valuation usually need models trained on your own data. Document search and assistants are built on existing large language models, connected to your documents with access control. We choose the simplest approach that meets the accuracy target.

Have a decision worth improving?

Describe it in a few sentences: what is decided, how often, and what a wrong call costs. We'll tell you whether AI is likely to help.

Book a free call Reply within two business days