Graphs Built for Operational Use
We design knowledge graphs as operational information architectures. They support discovery and traceability where relationships matter, while providing a shared basis for integration and reasoning. Applications can use the same context without treating the graph as a replacement for every source system. A record’s meaning may depend on the configuration to which a component belongs. An obligation means little without its jurisdiction, just as an assertion needs the dated evidence that supports it. Each intended use determines what belongs in the graph and what should remain in an authoritative source system.
Our work begins with conceptual modelling and stable identity. We establish source authority before representing change over time, then attach provenance to every material assertion. Access control governs use, while published contracts define how applications read from and contribute to the graph. RDF suits work that depends on standards alignment and inference. A property graph may better serve traversal performance or developer ergonomics. Hybrid designs combine these needs when justified. Platform selection follows those requirements rather than preceding them. We also design ingestion and reconciliation, then place quality controls under clear stewardship. The graph remains intelligible as its sources and concepts change.
Business Domain and Enterprise
Domain-specific concepts connected across the enterprise.
We develop domain graphs in the language and level of detail used by specialist teams. An enterprise layer then connects them through stable identities and the few concepts that genuinely need to be shared. Governance determines how those connections change. Specialist teams can solve operational problems within their own domains while exchanging meaning across the organisation, without being forced into an abstract model that serves no team well.
We develop each domain model with subject specialists around real decisions and queries. Following the relevant processes shows which entities and relationships require common treatment across the enterprise. The resulting architecture may use a shared semantic spine. Where autonomy matters more, domains can remain federated. Selectively materialised views support recurring cross-domain needs. Explicit rules establish authority and access, while versioning governs change. This supports incremental delivery. An individual domain can solve a useful problem early without creating an incompatible foundation for later work.
Graph based logic induction and enrichment
Evidence-based discovery of new relationships, with uncertainty explicit.
Keeping Facts and Hypotheses Distinct
We use graph structure to discover relationships and patterns unavailable in isolated records. Repeated paths may connect organisations that appear separate, while an entity whose connections depart from its peers can reveal a meaningful anomaly. Independent evidence may also support a plausible missing link. We keep three epistemic states distinct because the distinction itself matters: facts asserted by an authoritative source, conclusions entailed by explicit rules and hypotheses generated statistically. Enrichment can therefore add analytical reach without disguising uncertainty.
The method follows the relationship being investigated. Constraint reasoning tests what explicit rules permit. Path and motif analysis exposes recurring structures, while community detection reveals broader groupings. Graph embeddings or probabilistic relational models can quantify less explicit similarity. Link prediction proposes relationships for further scrutiny. Symbolic and statistical techniques may be combined when the evidence warrants it. Every candidate conclusion is checked for temporal and domain plausibility. We test for leakage from future information and sensitivity to an incomplete network, then assess performance on representative decisions. Each derived assertion carries the rule or model that generated it. Supporting evidence sits alongside confidence and review state, allowing revision whenever the data or reasoning changes.
Multi-Graph Orchestration
Coordination across specialised graphs without forcing every domain into one model or platform.
We orchestrate distributed graph assets so they can be discovered and composed while remaining under the responsibility of their domain owners. Research and product teams retain the modelling freedom appropriate to their work. Supply-chain or regulatory graphs can keep different security boundaries, update rates and database characteristics. Cross-domain value emerges without consolidation into a single model or platform.
Contracts Across Graph Boundaries
We establish the shared contracts on which cross-graph work depends. Graph and schema registries make assets discoverable, while identifier policies and semantic mappings make composition possible. Authority statements show which source can support a claim. Query interfaces provide access, and provenance travels through the composed result. The operating requirement determines the orchestration pattern. Federated query can serve live discovery. Routed APIs suit controlled service boundaries. Event-driven synchronisation supports continuing exchange, while purpose-built materialised views meet recurring needs at scale. Latency and control requirements determine the balance. Conflict handling is treated as a first-order concern, as are version compatibility and access enforcement. Partial failure must remain visible because a technically valid cross-graph query may still produce an operationally misleading answer.
Hybrid Neurosymbolic + LLM systems
Flexible language understanding grounded in knowledge that can be checked and rules that can be enforced.
We design hybrid systems that combine flexible language understanding with explicit knowledge. Rules govern what follows from that knowledge, while calculation and verification handle tasks that need deterministic assurance. Language models interpret varied requests and unstructured material. Graphs and ontologies establish what is known, including stable identity and context. Rule engines and solvers determine what is permitted or compatible, while conventional services perform authorised operations and numerically exact work. This division of responsibility preserves a natural interface while grounding consequential decisions in components that can be inspected.
Architecture depends on the consequence of error as much as on model capability. We define retrieval and tool boundaries before designing semantic parsing. Evidence handling preserves citations, while rule evaluation tests whether a proposed result is valid. Confidence policies determine when the system should abstain. Human approvals retain authority where required. Recovery routes address incomplete or conflicting inputs. Assessment is decomposed in the same way. Extraction accuracy is measured separately from factual support, and constraint satisfaction is distinguished from action validity. Robustness is tested independently of prose quality. Teams can locate a fault and strengthen the responsible component. In delivery, this brings dependable automation into rule-bound or safety-relevant work while keeping decisive facts and checks inspectable.
Our experience
Problems our experts have solved
Knowledge Graph System
Our experts solved this problem for an equipment supplier whose product structures and certification records were distributed across systems owned by different functions. Service histories sat apart from customer configurations, while technical documents were managed elsewhere again. We designed and delivered a graph that linked the relevant entities without displacing any source as the authority for its own records. Every relationship retained its validity dates and provenance. Service engineers could retrieve guidance for the exact installed configuration, while compliance teams could follow a certification change through to the affected products and customers.
Business Domain and Enterprise
We helped an insurer resolve the scope of a proposed graph when definitions and ownership remained contested. The initial proposal attempted to cover customers and policies alongside claims. It also included providers and assets, as well as the investigations that connected them. Our experts began instead with the evidence required for claims investigation. We connected it to a deliberately small enterprise core covering party identity, then added policy and asset identity where the investigation required them. The delivered graph supported investigators immediately. Its published interfaces allowed underwriting and customer-service teams to add their own domain structures, while fraud specialists could do the same without reopening every established decision.
Graph based logic induction and enrichment
Our experts addressed a procurement function’s need to understand the relationships behind its supplier records. Directors and registered addresses connected some organisations, while contracts and subcontractors exposed other dependencies. We used deterministic company-control rules to establish known links. Network analysis then identified further associations supported by recurring addresses and personnel, as well as by trading patterns. Strongly evidenced relationships entered the analytical graph with provenance. Ambiguous ones went to investigators, enabling closer scrutiny without presenting statistical proximity as proof of misconduct.
Multi-Graph Orchestration
We solved a cross-domain information problem for a multinational manufacturer that maintained separate graphs for products and research. Supplier information and regulatory obligations lived in other graphs, each on a platform suited to its domain. Our experts introduced common identifiers and a mapping service. A query layer recorded which graph supplied each assertion, avoiding an unnecessary migration into a single store. Analysts could trace a proposed material change from research into the affected products, then follow supplier implications through to the relevant obligations. Domain teams retained control of their models and release cycles, together with their permissions.
Hybrid Neurosymbolic + LLM systems
Our experts corrected a material weakness in a technical-support assistant. It explained manuals clearly but occasionally paired components that could not operate together in the customer’s installed configuration. We connected its interpretation of each request to a product graph containing versioned compatibility rules and permitted substitutions. Proposed actions had to pass those checks before an answer was composed. The assistant retained a natural conversational interface, while technicians could inspect the product evidence and the rule governing each recommendation.