EdgeLiberator

EdgeLiberator

Unlock contextual intelligence from disconnected data

EdgeLiberator brings together structured, semi-structured, and unstructured content from across the enterprise — turning disconnected documents, telemetry, asset records, and operational content into a unified contextual intelligence layer.

It is a contextual data fusion product built to unlock the value of information that traditional systems leave fragmented, isolated, or difficult to use.

Most organisations already hold large volumes of valuable operational and enterprise data, but it is spread across PDFs, SOPs, results files, SharePoint, BIM models, CRM systems, asset records, videos, and other disconnected sources. EdgeLiberator solves this by breaking open proprietary formats, extracting meaning from diverse content types, and fusing them with telemetry to create a richer, more usable data foundation for analytics, reasoning, and AI.

Core Capabilities

EdgeLiberator transforms disconnected content into contextualised, reusable intelligence.

It is designed to solve the practical problems that sit between isolated information sources and meaningful enterprise insight:

• valuable information trapped in proprietary or unstructured formats

• telemetry without the documents, records, or human context needed to interpret it

• fragmented content spread across OT, IT, and ET environments

• limited visibility into how operational, engineering, and enterprise data relate to each other

• time-consuming manual effort to assemble context before analysis can begin

By resolving these issues early, EdgeLiberator provides the contextual layer needed for stronger reasoning, root-cause analysis, AI adoption, and digital transformation.

• Content aggregation across diverse sources
  EdgeLiberator collects contextual content from multiple sources, including SOPs, results files, telemetry, enterprise applications, engineering systems, asset records, SharePoint, PDFs, video, and other structured or unstructured repositories.
• Data mining and fusion
  It extracts meaning from structured, semi-structured, and unstructured content, then fuses it with time-series telemetry to create richer context around assets, processes, and events.
• Contextual intelligence layer
  EdgeLiberator bridges OT, IT, and ET by bringing operational, enterprise, and engineering information together into a single contextual layer that supports exploration, reasoning, and decision-making.
• FAIR data enablement
  By making disconnected information more findable, accessible, interoperable, and reusable, EdgeLiberator helps organisations turn fragmented content into a reusable digital asset base.
• AI and advanced reasoning support
  EdgeLiberator provides the contextual foundation needed for Generative AI, machine learning, and advanced analytics to operate on more complete and meaningful information.

How it works

• Collect → Extract → Fuse → Contextualise → Organise → Deliver

Collect – EdgeLiberator gathers structured, semi-structured, and unstructured content from enterprise systems, engineering repositories, operational platforms, and document-based sources.

Extract – Relevant metadata, entities, and relationships are identified across formats such as PDFs, results files, video, asset records, and enterprise content.

Fuse – This contextual content is combined with telemetry and time-series data to connect operational events with the wider business and engineering context around them.

Contextualise – OT, IT, and ET information is aligned to provide a more complete view of assets, processes, behaviours, and outcomes.

Organise – Information is structured into a form that is easier to search, explore, reason over, and reuse across different use cases.

Deliver – Contextualised, analytics-ready information is made available to downstream applications, AI services, digital workflows, and decision-support environments.

Why it matters

Customers are not really asking whether more data exists. They are asking:

• How do we unlock trusted, real‑time operational data from the intelligent edge so we can act faster, operate smarter, and deliver measurable value?
• How do I validate results data where telemetry context is required?
• How do I unlock historic results data associated with obsolete technology?
• How do I bring together OT and IoT data with contextual information such as BIM models or SOPs so I can reason across the full operating picture?
• How do I knowledge-mine unstructured and semi-structured content such as images, PDFs, PowerPoints, video, and documents?

EdgeLiberator answers these questions by turning disconnected content into a contextual intelligence layer that makes data more understandable, reusable, and valuable.

Business Benefits

Context-rich data for faster reasoning, better decisions, and stronger innovation

EdgeLiberator helps organisations:

• unlock value from disconnected and previously underused data sources

• improve situational awareness across assets, processes, and operations

• reduce manual effort required to assemble context for analysis

• support stronger root-cause analysis and reasoning

• create FAIR data that is easier to search, share, and reuse

• accelerate analytics, AI, and new digital services on a richer contextual foundation

Summary

Don’t just collect data — unlock the context around it

EdgeLiberator turns disconnected documents, records, and operational content into a unified contextual intelligence layer — helping organisations connect telemetry with the information needed to interpret, trust, and act on it.

EdgeLiberator breaks open proprietary formats and fuses structured, semi-structured, and unstructured content with telemetry to create contextual, reusable intelligence for analytics and AI.