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Applied AI & Agents

MTK puts AI agents, generative AI and predictive models to work in daily operations, with people approving the decisions that matter.

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AI reads a document and extracts cited fields; an AI agent checks stock and credit and proposes to release an order; a person approves and the step is logged; the pilot KPI rises above the baselineAI reads a document and extracts cited fields; an AI agent checks stock and credit and proposes to release an order; a person approves and the step is logged; the pilot KPI rises above the baseline

The challenge

Why applied AI rarely reaches production

  • Demos, not systems

    Generative AI prototypes impress in a demo but are not connected to the systems where the work happens.

    Addressed byGenerative AI and agentic workflows

  • No measure of quality

    Without evaluation sets and baselines, nobody can say whether a model is good enough to put in front of users.

    Addressed byAI engineering and MLOps / LLMOps

  • Automation without oversight

    Agents that act without review steps or audit trails create risks that legal and operations teams will not accept.

    Addressed byAI proposes, people approve

How it works

AI proposes, people approve

Generative AI, predictive models and agents share one control point: a person approves, and every step is logged.

AI that proposes

  • Generative AI
  • Predictive AI
  • AI agents

Control point

  1. Proposes
  2. Person approves
  3. System updatedEvery step logged

Measured

  • Baseline
  • Pilot KPI

What MTK offers

Applied AI services

  1. Generative AI and agentic workflows

    Agents that carry out multi-step work across existing systems, such as preparing an order or reviewing a contract.

    OutputAgents that propose each action for a person with authority to approve, with every step logged.

  2. Predictive AI

    Demand, inventory, cash-flow, maintenance and risk models built on the client's own data.

    OutputModels measured against a baseline taken before go-live and monitored for drift once in use.

  3. Document and knowledge intelligence

    Invoices, contracts and orders read into business systems, and assistants that answer staff questions from company documents and policies.

    OutputStructured records from documents, and answers that cite their source.

  4. AI engineering and MLOps / LLMOps

    Evaluation, deployment, monitoring and retraining for models and agents in production.

    OutputVersioning, access control and rollback for every release.

How MTK delivers

From use case to model in production

Models and components that MTK maintains shorten the path from a first pilot to a system in production.

Stages

  1. Assess

    Use cases ranked by value, data fit and risk.

  2. Plan

    Model or agent design, controls and success measures.

  3. Pilot

    Six weeks on real data, with one KPI measured before and after.

  4. Scale

    Integration into workflows, user training and production controls.

  5. Operate

    Monitoring for quality, drift and usage, with scheduled retraining.

  • MTK Predictive

    Forecasting and predictive maintenance models ready to adapt to client data.

    Pilot · Scale

  • MTK Energy

    Energy visibility, forecasting and reporting across sites and assets.

    Pilot · Scale

  • MTK Intelligence

    A governed runtime for AI agents, with approval steps and audit trails built in.

    Scale · Operate

Related use cases

Use cases in operations

View all use cases

  • MTK Predictive

    Predictive maintenance

    Early warning of equipment failure from sensor data and maintenance history, measured by unplanned downtime.

    Predictive maintenance

  • Applied AI & Agents · MTK ERP

    Transport operations control tower

    Multi-stop trips planned on a dispatch board that checks vehicles and drivers, with proof captured in a driver app.

    Transport operations control tower

  • Generative AI

    Enterprise knowledge assistant

    Answers drawn from company documents and policies, each with a link to the source it came from.

    Enterprise knowledge assistant

  • MTK Intelligence

    Data centre design review

    Drawings, schedules and specifications checked for single points of failure across power and cooling, with evidence for each finding, measured by review time per revision.

    Data centre design review

FAQ

Questions about applied AI

What is an agentic workflow in practice?
A sequence of steps an AI agent carries out across systems, such as checking stock and credit before an order is released. People approve the actions that carry business or financial risk.
How does MTK measure whether a model works?
Each use case has a baseline taken before the pilot and one agreed KPI measured after it. Models must also pass an evaluation set before release, and are tested again after every change.
How long does an AI pilot take?
Six weeks for one use case on the client's data. The pilot ends with working software, a measured result against the baseline and a decision on whether to scale.
Which AI models does MTK use?
The model follows the task, the cost and the client's data rules. MTK works with both commercial and open models, and designs each solution so client data stays in the client's environment.

A six-week AI pilot on your data

Choose one process, agree one KPI and see a measured result before deciding to scale.

Discuss an AI pilotTalk to an AI consultant

Next in the lifecycleIntelligent Enterprise Applications