What we solve
Data platform services
A data-centric platform that treats your data as an asset in its own right — ingesting it from the systems you already run, representing it in a governed model, and publishing it back in the format each decision needs. The building blocks below describe how that works.
What makes data successful
Four principles frame how we treat data, so it stays valuable to the business rather than trapped inside the system that produced it.
Data platform
A data-centric architecture in five layers — from application-independent storage up to machine-readable distribution.
Store data in an application-independent RDF graph, deployable on your chosen cloud service — Azure, AWS, Google Cloud or IBM.
Represent context and knowledge using a data model that facilitates natural language and allows additional data sources to be appended easily as you become aware of them.
Configure transformation rules, workflow rules and enriching logic to increase the reusability and efficiency of data processing.
Evaluate inferences and insights using connected AI workbenches and model-driven asserted knowledge. Generate new data through enrichment.
Ingest data in its original format and publish it in a machine-readable format for onward processing when required.
Five-step data asset management
How a data asset moves from source input to target output.
Accept
Accept data from multiple sources in its original format.
Interpret
Interpret it into a universal data format.
Apply rules
Apply a business-rules model for the required context.
Execute
Execute the rules workflow to publish consolidated data in the required format.
Export
Export or connect the published data to an application or document/file template.
Integration technology
The connectivity layer that lets the platform reach a wide range of data sources under strong security and automation.
Model driven
All aspects of solution knowledge and application operations are driven via the semantic model, reducing reliance on coded logic and maximising reuse of the managed connectivity configurations.
Connectivity
Data-centric connectivity that maximises the opportunity to connect to a wide range of data sources — for third-party and business-operations data — using several standard and non-standard protocols to widen the inclusion of relevant data sources.
Cloud based
Platform infrastructure is cloud-based with elastic supply (use only the processing required) and the strong security protocols expected of a SaaS solution. Data services are all PaaS and meet GDPR compliance.
Automation
Automated integration workflow, monitoring and load balancing for connection services.
AI and learning
AI capability beyond deep learning that uses machine-readable semantic inference models for a wider learning context, and greater transparency and explainability of AI outcomes for data connectivity.
Data Governance
Governance is built in, not bolted on: every data asset carries end-to-end lineage, and the governed model applies consistent standards, access controls and compliance across all connected sources — keeping data auditable, trustworthy and safe to reuse.
Sustainability and the SDGs
Treating data as a reusable, transparent asset is not only good engineering — it also supports broader sustainability goals aligned with the UN Sustainable Development Goals.
Reuse data, reduce waste
Configuring data once and reusing it across the value chain avoids duplicated effort and redundant copies, supporting responsible use of computing resources (SDG 12 — responsible consumption).
Efficient, elastic infrastructure
Cloud-based elastic supply uses only the processing actually required, avoiding the energy cost of idle, over-provisioned systems (SDG 7 and SDG 9 — affordable energy and resilient infrastructure).
Transparent, explainable AI
Semantic inference models make AI outcomes more transparent and explainable, so the decisions taken on the data can be understood and accounted for (SDG 16 — strong, accountable institutions).
Traceable data lineage
The governed model keeps the lineage of every data asset visible — where it came from and how it was transformed — so reuse stays trustworthy and auditable rather than opaque.
The governed decision loop
The platform in its place: your systems stay authoritative, human-in-the-loop review can be prioritised, and every proposed decision can be approved, adjusted, or overridden by a person before it is written back with an audit trail.
Authoritative systems
Your incumbent ERP, MES, and quality systems stay the source of truth — the platform reads from them, it does not replace them.
Shared operational model & validation
Fragmented operational truth is assembled into one validated model, so options are reasoned about against a coherent picture.
Scenario options
The platform proposes ranked, explainable scenario options with their trade-offs — never a single hidden answer.
Human decision
A person can approve, adjust, or override a proposed option; human-in-the-loop review is prioritised based on the production need, so decisions are made by people, not automated on their behalf.
Auditable write-back
The chosen decision is written back to the authoritative systems with a full audit trail of what was decided and why.
The audit trail feeds the authoritative systems again, closing the loop — decisions stay governed, evidenced, and reversible.
It is the semantic decision-support layer that sits around the systems you already run — it never replaces them.
What it is
- A semantic decision-support platform that helps people compare scenario options.
- A shared operational model that validates proposed decisions against your authoritative systems.
- A governed decision loop where people can approve, adjust, or override proposed decisions, with human-in-the-loop review prioritised as the production need requires.
- Additive to your incumbent business systems, which stay authoritative throughout.
What it is not
- An ERP, MES, or machine controller.
- A replacement for your incumbent business systems.
- A complete digital-twin or edge platform.
- A hands-off automation that removes people from the loop.
Talk to us about your data
Tell us where fragmented data is costing you a decision, and we will scope a low-risk diagnostic around it.
Book a diagnostic conversation