AI agents
Answering executive questions from a re-engineered ontology
AI analyst
The product-centric ontology re-engineered into a semantic layer an AI analyst can read, then tuned for the leadership team.
The engagement: existing, as built, in production
Existing
The challenge
The group’s chief technology, data and AI officer wanted to demonstrate AIP Analyst for leadership decision-making. The enterprise ontology was product-centric and not built for a language model to consume: it lacked the contextual relationships, financial data bindings and external market signals needed for cross-functional synthesis, planning and forecasting.
As built
What we built
- Ontology optimisation: the product-centric ontology re-engineered into a unified semantic layer integrating business-unit data, financial models and external signals
- AIP Analyst deployed and tuned for complex financial calculations and automated recommendations on strategic planning, underwriting and forecasting
- Specialised training for the chief underwriting officers across every business unit
In production
The impact
- Real-time, data-driven synthesis and strategic calculations for the leadership team, cutting the time to planning and forecasting insight
- Product-centric silos broken down by an AI-consumable semantic layer linking business-unit operations, group-level financial metrics and external signals
- Senior underwriting executives moved from legacy analysis to AI-assisted decision-making
Leadership asks
and the model answers, with the sources
Existing
The challenge
The group’s chief technology, data and AI officer wanted to demonstrate AIP Analyst for leadership decision-making. The enterprise ontology was product-centric and not built for a language model to consume: it lacked the contextual relationships, financial data bindings and external market signals needed for cross-functional synthesis, planning and forecasting.
As built
What we built
- Ontology optimisation: the product-centric ontology re-engineered into a unified semantic layer integrating business-unit data, financial models and external signals
- AIP Analyst deployed and tuned for complex financial calculations and automated recommendations on strategic planning, underwriting and forecasting
- Specialised training for the chief underwriting officers across every business unit
In production
The impact
- Real-time, data-driven synthesis and strategic calculations for the leadership team, cutting the time to planning and forecasting insight
- Product-centric silos broken down by an AI-consumable semantic layer linking business-unit operations, group-level financial metrics and external signals
- Senior underwriting executives moved from legacy analysis to AI-assisted decision-making
Leadership asks
and the model answers, with the sources
The same problem elsewhere
The same analyst answers leadership questions in any business whose model is governed: production, network, portfolio or service questions, with the lineage behind each answer.
- Insurance & reinsurance
- Leadership questions on planning, underwriting and forecasting answered from a governed model.
- Manufacturing & engineering
- Board questions on output, margin and supply risk answered across plants from one model.
- Energy & utilities
- Executive questions on generation, demand and asset risk, with the sources behind each figure.
- Logistics & transport
- Network performance and cost questions answered across regions without a new dashboard each time.
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