Platform · Connect

One model of your operation, not thirty disconnected systems.

The connected systems resolve into a single graph of entities and relationships, so agents and people reason over one coherent picture instead of stitching dashboards together.

$12.9M/year
Poor data quality costs organisations at least $12.9 million a year on average.
Gartner, Data Quality: Why It Matters and How to Achieve It (2020)
95% struggle
90% cite silos
95% of IT leaders struggle to integrate data across systems; 90% say data silos create business problems.
MuleSoft (Salesforce), with Vanson Bourne & Deloitte Digital, 2025 Connectivity Benchmark Report (2025)
51% negative consequence
30% inaccuracy
51% of organisations have already had at least one negative consequence from AI, and inaccuracy is the most common.
McKinsey (via CX Today), The state of AI in 2025: Agents, innovation, and transformation (2025)

What it is

Every connected system speaks its own language. The knowledge graph resolves them into one canonical model, linking a customer to a deal to a work order to the machine that makes the part. It is the shared context that lets an agent understand that a vibration on a pump actually threatens a specific customer's shipment.

Sound familiar?

The work nobody signed up for.

If you have said one of these this month, the rest of this page is for you.

Ask which customers a failing machine affects, and three teams start three spreadsheets.
Operations planning manager
Our AI assistant answered confidently, from the wrong system.
Head of data
The warehouse tells us what happened last quarter, not what is connected right now.
BI lead
Every analyst has their own definition of an active customer.
Finance analyst
Our root-cause analysis stops at the edge of each system.
Continuous improvement lead

Use cases

What you would use it for, first.

Each one starts from a problem you can name, and ends with something a person can see has changed. Live means it runs in the product today; pilot means we build it with you.

  1. 01

    From a machine signal to the order it threatens

    Live today
    Today

    A pump is degrading and nobody can say which customer's shipment depends on it.

    With the platform

    The graph links the asset to the line it runs on and the line to the open customer deals it serves, so the diagnostic tier states the business impact next to the root cause.

    What changes

    Maintenance priority follows customer impact, not whoever shouts loudest.

  2. 02

    Links from keys, not guesses

    Live today
    Today

    Joining records across systems by fuzzy matching produces confident nonsense.

    With the platform

    Records keep their source lineage, are de-duplicated per source, and are linked through the keys they already state, such as an account id on an invoice.

    What changes

    Every relationship in the graph can be traced back to the field that created it.

  3. 03

    AI answers grounded in computed facts

    Live today
    Today

    A language model asked about your operation will guess when it does not know.

    With the platform

    The copilot works from facts the platform has computed over the graph and from the real tool registry, plans before it builds, and dry-runs before it claims something works.

    What changes

    Answers rest on your data and say where they came from.

  4. 04

    One customer, across systems with no shared key

    Built in a pilot
    Today

    The CRM and the ERP hold the same customer under different ids and spellings.

    With the platform

    Cross-system entity resolution, matching records that share no key, is built with you in a pilot and reviewed before it changes any link.

    What changes

    One customer record where there were three.

Business value

Where the return comes from.

The levers, and the range other organizations have published for each. Ranges are typical of the category, not a promise and not our customers' results.

Cost of poor data

$12.9M a year

A belief-based estimate from surveyed organizations. One linked model removes the re-keying that creates bad data.

Gartner, Data Quality: Why It Matters and How to Achieve It (2020)

AI that answers wrongly

51% hit a negative consequence

Inaccuracy is the most common harm organizations report from AI. Grounding answers in computed facts is the countermeasure.

McKinsey (via CX Today), The state of AI in 2025: Agents, innovation, and transformation (2025)

Silos that hurt the business

90% say so

Vendor-sponsored survey of IT leaders.

MuleSoft (Salesforce), with Vanson Bourne & Deloitte Digital, 2025 Connectivity Benchmark Report (2025)

Time spent reconciling definitions

Analysts work from one model instead of their own extracts. Run the estimate below on your team.

Run your numbers

Hours your people spend moving work between systems.

Every input is yours to change, and the arithmetic is shown in full. Nothing is hidden in a multiplier.

Estimate
≈ €73K
of time handed back a year

12 × 6 × €55 × 40% × 46 working weeks = €72,864

An estimate on the numbers above, not a measurement and not a customer result. In an Operations X-Ray we replace the assumptions with figures measured on your own systems.

Before and after

The same week, run differently.

Impact questions start three spreadsheets.

Impact is one traversal from the asset to the customer.

Records are joined by fuzzy matching.

Records are linked by the keys they already carry.

The assistant guesses.

The assistant answers from computed facts.

Root cause stops at each system's edge.

Root cause follows the graph across systems.

Capabilities

What you get.

Canonical entities

Customers, vendors, employees, deals, invoices, tickets, processes, assets and more, mapped from every source into one type system.

Real relationships

Not just records, but the links between them, so impact and root cause can traverse from a machine signal to a business consequence.

Twin-grounded

The graph is grounded in the real operation, so diagnostics reason over what is actually connected rather than an assumed flowchart.

Shared by every agent

The whole fleet reads the same model, so findings, proposals and actions all speak in the same terms.

How it works

Three moves.

01
Ingest and normalize

Connected systems land as canonical entities with their source lineage preserved.

02
Resolve and link

Records from each source are de-duplicated and linked through the keys they already share, across system boundaries.

03
Reason over it

Agents and analysts query one model to find impact, root cause and opportunity.

Why ours is different

The part competitors leave out.

  • The graph spans IT and OT, so a plant-floor event has a traceable business impact.
  • It is the substrate for governed action, not just a visualization.
  • Lineage is preserved, so every fact traces back to its source system.

Questions

Frequently asked.

Is this a data warehouse?

No. It is a live, relationship-first model of the operation used to reason and act, not a reporting store. It complements, rather than replaces, your analytics stack.

How is the graph kept current?

It updates when the connectors sync, and drift detection flags schema changes so the mapping can be patched.

Operations X-Ray

Ask one impact question of your data.

Bring a question that today needs three spreadsheets. We show it answered as one traversal of your connected systems.