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Data & AI Platforms

MTK builds a governed data foundation in the client's cloud, so analytics and AI work from one trusted version of the business.

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Customer records from ERP, CRM and files pass quality rules and become one governed data product in the client's cloud, feeding dashboards, AI assistants and applicationsCustomer records from ERP, CRM and files pass quality rules and become one governed data product in the client's cloud, feeding dashboards, AI assistants and applications

The challenge

Where data holds AI back

  • Data scattered across systems

    Sales, finance and operations data sit in separate systems, files and spreadsheets with no shared structure.

    Addressed byData engineering and integration

  • Numbers that disagree

    The same KPI has several definitions, so dashboards conflict and AI answers cannot be trusted.

    Addressed byAnalytics and decision intelligence

  • No lineage or access control

    Teams cannot trace a figure back to its source, or control who sees sensitive data and why.

    Addressed byData governance and quality

How it works

One trusted version of the business

Data from business systems lands in one governed foundation, and every dashboard, assistant and application reads the same definitions.

Sources

  • ERP
  • CRM
  • Files
  • Operations and IoT

MTK Intelligence in the client's cloud

  • Data foundation
  • Business ontology
  • Control and audit

One definition per KPI

Uses

  • Dashboards
  • AI assistants
  • Applications

What MTK offers

Data and AI platform services

  1. Data platform modernisation

    Fragile extracts and spreadsheet reporting replaced by one governed lakehouse in the client's cloud.

    OutputA platform ready for analytics and AI workloads, with access and cost controls built in.

  2. Data engineering and integration

    Reliable pipelines from ERP, CRM, operational and IoT sources, with monitoring and data contracts.

    OutputPipelines that catch a change in one system before it breaks reports or models elsewhere.

  3. Data governance and quality

    Catalogue, lineage, quality rules and access policies that make data fit for AI.

    OutputData products, each with a named owner, a written definition and a quality measure tracked over time.

  4. Analytics and decision intelligence

    Semantic models and dashboards built on shared KPI definitions.

    OutputOne set of numbers used by business teams, management reports and AI assistants.

How MTK delivers

How a data foundation is built

MTK Intelligence supplies the foundation components, deployed in the client's cloud rather than built from scratch.

Stages

  1. Assess

    Data sources, quality and usage profiled on live systems.

  2. Plan

    Target architecture, data products and the governance model.

  3. Pilot

    One data product feeding one dashboard or AI use case.

  4. Scale

    Further business domains onboarded on the same foundation and controls.

  5. Operate

    Pipelines, quality and cost monitored under agreed service levels.

  • Data foundation

    Connectors, storage and pipelines for common business systems, configured for each client instead of written from zero.

    Pilot · Scale

  • Business ontology

    A shared model of customers, products, orders and assets that gives every KPI a single definition.

    Pilot · Scale

  • Control and audit

    Access control, lineage and audit trails applied to data and AI in the same way.

    Scale · Operate

Related use cases

Use cases built on the foundation

View all use cases

  • MTK Intelligence

    Business data in one model

    ERP, CRM and file data unified, with one definition per KPI shared by dashboards and AI.

    Business data in one model

  • Intelligent Enterprise Applications · MTK ERP

    Business operations on one platform

    Sales, targets and bids for every unit on one platform, with company data linked in place and agents that propose.

    Business operations on one platform

  • MTK Energy

    Energy monitoring

    Meter data from sites, lines and equipment brought into one view and measured as energy per unit produced.

    Energy monitoring

FAQ

Questions about data platforms

Which cloud does MTK build on?
The client's chosen cloud subscription. Data stays in the client's environment, and the design avoids services that would tie data to a single vendor without a clear reason.
Do we have to replace our existing data warehouse?
No. The foundation can extend what already exists, using the current warehouse as a source or a target. Replacement is planned only where cost, performance or support limits justify it.
How is data quality managed?
Quality rules run inside the pipelines, and each data product carries an owner and a measure. Issues are flagged before they reach dashboards or models, with a record of how each was resolved.
Who owns the platform and the data?
The client owns its data, its data products and the models trained on them. MTK Intelligence components used in the foundation are licensed, on terms agreed at the start.

Assess your data readiness for AI

A two-week assessment profiles real data and shows which AI use cases it can support today, and what to fix first.

Request an AI readiness assessmentTalk to an AI consultant

Next in the lifecycleApplied AI & Agents