A Contract Above Source Systems
We develop semantic layers that give users and applications stable concepts across platform boundaries and technical change. The layer defines entities independently of the source fields through which they arrive. It establishes how measures are calculated and how relationships or rules should be interpreted. Application profiles complement this contract by adapting broad standards to a particular exchange or product. They specify the properties required there, along with the relevant vocabularies and constraints.
We develop the layer from priority questions and user journeys, with analytical decisions showing which distinctions matter. Every concept and calculation is traced back to an available source. Definitions remain explicit, as do temporal assumptions and aggregation rules. Profiles distinguish required properties from optional ones. They state cardinality and value constraints, define validation and provide controlled extension points. Ownership follows the dependencies that matter in use, while versioning allows the model and its mappings to evolve without silently changing downstream interpretation.
Semantic layer maturity assessments & ontology grounding
A clear view of semantic-layer maturity and the value of ontology grounding.
We assess whether a semantic layer provides consistent interpretation as definitions change and controlled reuse as new domains are added. Analytical and AI systems must also be able to trace concepts to the underlying evidence. The assessment distinguishes a convenient catalogue of metrics from a durable semantic capability. It then identifies where ontology grounding would add practical value.
Assessing the Layer in Use
Representative business questions are traced through their definitions and calculations. We follow identifiers into source mappings, then test the access paths through which users retrieve the result. This reveals how the layer actually behaves. Ownership and documentation show whether it can be maintained. Versioning and validation show whether change is controlled. We consider the depth of the modelling alongside available tooling and alignment with relevant domain ontologies. The resulting improvement path is proportionate. Governance is strengthened where ambiguity is organisational, while structurally weak concepts are remodelled. Simpler arrangements remain in place when they already meet the need.
Decision Trace Auditing & Provenance
A traceable account of a decision from changing evidence to final approval.
Reconstructing the Basis for a Decision
We reconstruct the evidence that informed a decision and the transformations applied to it. Assumptions remain explicit, with the relevant policy or model version attached. Human interventions and approvals complete the account. For fragmented historical records, this shows why an outcome was reasonable at the time. For future workflows, we design provenance capture so accountability does not depend on institutional memory.
Logs and documents are brought into a common account of decision events. Data lineage connects them to analytical outputs, while model records and interviews explain the states that were later superseded. Gaps and conflicting records are reported as such rather than resolved by inference. Ongoing capture follows risk and operational value. Provenance metadata may be sufficient for a routine workflow. Event records or evidence graphs can represent a more consequential pathway. Decision journals preserve material authorisations when human reasoning is central. This concentrates effort on changes that matter instead of imposing indiscriminate documentation. Used this way, provenance shortens assurance and incident review because investigators can reproduce the decision path without reconstructing it from disconnected emails and system logs.
Ontologically grounded Agentic AI
A governed world model for reliable AI-agent action.
We ground AI agents in explicit models of the entities that define their operating environment. Relationships show how those entities interact, while constraints establish what the agent may infer or do. This gives an agent a reliable basis for distinguishing similar asset types and organisational roles. Policy conditions remain explicit, as do lifecycle states, when the agent retrieves information or calls tools. Conceptual errors can therefore be intercepted before they become invalid recommendations or actions.
Grounding is designed around the agent’s goals and action space rather than added as a detached knowledge source. Authority boundaries define what it may do. Evidential obligations define what it must know before acting. Ontologies and taxonomies can shape retrieval, while policies and system schemas constrain planning. Tool selection and pre-action validation follow the same model, with provenance and confidence retained throughout. Semantic checks work with role controls. Human approval remains available, and explicit exception handling governs cases that do not fit the ordinary path. Evaluation focuses on conceptual and action validity as well as the quality of generated language. Reliable automation can extend beyond simple retrieval without moving uncertain or consequential actions outside explicit review boundaries.
Our experience
Problems our experts have solved
Semantic Layers & Application Profiles
We resolved inconsistent performance measures for a public-sector programme that used the same terms in policy documents and dashboards but calculated them differently in its operational systems. Our experts defined a semantic layer around canonical measures. Calculation logic and temporal rules made each measure inspectable, while source mappings connected it to the underlying evidence. We then created departmental application profiles for legitimate local extensions. Central reporting became comparable without requiring any department to discard the detail needed for its own operations.
Semantic layer maturity assessments & ontology grounding
Our experts assessed a retailer’s central semantic analytics platform after teams disputed the definition of active customer. The same problem affected net revenue and product availability. Although calculations were technically centralised, their concepts remained underdefined. Time boundaries and exception rules were also unclear. We grounded the priority measures in a shared domain model and introduced decision records for definition changes. Consistency and trust improved without requiring the analytics stack to be rebuilt.
Decision Trace Auditing & Provenance
We reconstructed the review history for a financial-services firm that could not explain why a portfolio of applications had received different outcomes. Its records failed to connect input data with the relevant policy version. Analyst adjustments and final approvals sat elsewhere. Our experts built a decision-provenance model and located the transitions at which evidence disappeared. Capture was redesigned around those points. Reviewers gained a traceable explanation of every outcome, while authorised human judgement remained visible rather than being misrepresented as an automated rule.
Ontologically grounded Agentic AI
Our experts corrected an early maintenance agent for a facilities organisation after it confused asset types across buildings. It also misread contractual responsibilities and emergency classifications. We grounded the agent in an asset-and-service ontology and linked every available action to role and policy constraints. Semantic validation became mandatory before a work order could be created. Routine requests could then be triaged consistently. Ambiguous or high-risk cases went to the appropriate human operator with the supporting evidence intact.