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The best AI services start with ready data

To use AI well, you first need to look at your data. We review where you are and start with the stage you need.

Three reasons AI adoption stalls

AI adoption often stalls while collecting, understanding and using data.

  • Data collection

    Every system formats data differently

    Formats and units differ by system, so preparing data for AI takes time.

  • Data understanding

    The same data means different things

    Terms and rules differ by system, so AI cannot link information correctly.

  • Operational continuity

    When the connection drops, operations stop

    When service decisions are centralised, a network outage slows the response.

How we start the AI & AX transition

We check the state of your data and build the foundations you need, in order.

  1. Operations optimisation

    Find the right settings for operating conditions

    We compare outcomes by condition using operational data and help find the right settings.

    AutoOpt Nexus
  2. Data understanding

    Link decision rules to data

    We define what data means, how it relates and the business rules, so AI can use context and evidence.

    Semantic layerOntology design
  3. Operational continuity

    Keep key decisions running on devices

    We apply edge AI functions suited to each sensor and device, and resync data when the network connection returns.

    Edge AI AgentCouchbase Lite

From data to AI, built in-house

  1. Collection & edge layer
    • IoT platform
    • Edge gateway
    • Time-series data
  2. Data & semantic layer
    • Data hub
    • Ontology
    • Vector search
  3. AI & agent layer
    • LLM agents
    • RAG
    • Anomaly detection
  4. Application & service layer
    • Monitoring dashboards
    • Predictive maintenance
    • Decision support

Work in four stages

Collect
nTomIoTEdge Gateway

AS-IS

  • Each machine has its own screen, or records are kept by hand
  • Protocols and data units differ by manufacturer
  • Getting data means asking someone every time

TO-BE

  • Build a collection layer on the oneM2M standard
  • Install adapters and gateways for mixed protocols
  • Design time-series storage and monitor data quality

Deliverables

  • Standard collection pipeline
  • Device management console
  • Time-series dataset
Connect
nTomHubnTomDI

AS-IS

  • Data exists but is scattered across systems and teams
  • People pull and merge data for every report
  • The same metric shows different values in each system

TO-BE

  • Build a data hub and normalise to a standard model
  • Automate collection, conversion and routing with low-code pipelines
  • Link external and public data and tidy up APIs

Deliverables

  • Integrated data hub
  • Standard data model
  • Integration API catalogue
Understand
nTomRAGnTomViewSemantic layer

AS-IS

  • Data definitions exist only in documents
  • Manuals and work logs are hard to search
  • AI is connected, but without context its answers cannot be trusted

TO-BE

  • Set up metadata management and register systems
  • Define relationships with a domain ontology and knowledge graph
  • Search work documents and show the source of each answer
  • Add conversational queries to dashboards

Deliverables

  • Metadata repository
  • Domain ontology
  • Document RAG index
Automate
AutoOpt NexusEdge AI AgentCouchbase Lite

AS-IS

  • AI raises alerts, but people still take action
  • Optimal operation still needs an expert on hand
  • When the network drops, decisions stop too

TO-BE

  • Train and serve anomaly detection and demand forecasting models
  • Map agent function calls to existing control APIs
  • Put lightweight agents and local vectors at the edge
  • Double-lock guardrails based on hardware limits

Deliverables

  • Automated optimisation loop
  • Edge inference package
  • Guardrail verification logic

N2M’s AI & AX direction

Technologies we are researching to take the next step.

  • 01 · Operations optimisation · Autonomous Twin

    A digital twin that finds its own optimum

    We provide technology that simulates changing conditions and adjusts settings and plans to fit.

  • 02 · Data understanding · Semantic Layer

    Turning experience into machine-readable knowledge

    We provide a knowledge system that models decision rules as data relationships, so they still work when conditions change.

  • 03 · Operational continuity · Edge Intelligence

    Decisions that continue when the connection drops

    We provide a way to run AI functions on devices and sync data when the connection returns.

Where should your AI adoption start?

We review your current data and systems and find the next step together.

Request an AI & AX assessmentSee our solutions