AI Decision Intelligence Solutions for Faster, Confident Enterprise Decisions

An AI Decision Intelligence solution connects enterprise data, artificial intelligence, predictive analytics, business rules and workflow automation so that an insight ends in an executed action. It forecasts what is likely to happen, ranks the available responses against your objectives and constraints, explains why it selected one, and then either recommends it to a person or executes it automatically inside your existing systems.

Business intelligence explains what happened. Decision Intelligence determines what to do next. A business intelligence project ends with a dashboard that somebody has to interpret. A Decision Intelligence project ends with a ranked action, a named owner and an approval path, and it writes the result back into your ERP or CRM.

Predictive analytics estimates what is likely to happen. Decision Intelligence takes that prediction and combines it with your business objectives, cost constraints, policy rules and operational context to determine the best action, then executes it. Prediction is an input to Decision Intelligence rather than a substitute for it.

Yes. We build custom Decision Intelligence systems rather than reselling a licensed platform. The models, decision logic, business rules and integrations are engineered around your decision process and your existing technology stack.

Yes. We integrate with ERP, CRM, finance, supply chain, data warehouse, business intelligence, IoT and custom platforms, including SAP, Oracle, Microsoft Dynamics, Salesforce, Snowflake, Databricks, AWS, Azure and Google Cloud, using APIs, event streams and middleware. There is no requirement to replace your existing systems.

No. We map every decision to one of three levels of control. Decision support keeps the person in charge, decision augmentation asks a person to approve a scored recommendation, and decision automation executes only repetitive, low risk decisions inside limits you define. Anything below your confidence threshold escalates to a person.

Every recommendation carries a confidence score, the top contributing factors, the business rules that were applied and the data it was based on. These reason codes are designed so that risk, audit and compliance teams can review a decision without reading the model.

That is the question the discovery workshop answers. We assess your decision process, evaluate data readiness, and shortlist the use case with the strongest ratio of business value to delivery risk before any large implementation begins.

Yes. Cloud, hybrid, private cloud and on premise deployments are supported, together with data residency controls for regulated jurisdictions.

Discovery and data readiness takes from 90 minutes to two weeks. A proof of value on one decision typically runs 6 to 8 weeks. A full enterprise build usually runs 4 to 6 months, depending on how many systems the decision has to read from and write back into, and on how ready your data is.

By the business outcome the decision was meant to move, such as decision cycle time, forecasting accuracy, manual analysis effort, operating cost, inventory utilisation or risk exposure. We agree the measure during discovery and track it against a baseline, rather than reporting model accuracy alone.

Source code, models, pipelines and documentation are transferred to you where contractually agreed. You can run the system with your own team, keep us on a managed basis, or move it elsewhere.