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Operating method

Start with the workflow. Add AI only where it helps.

Magnetta helps manufacturers choose a practical path, assign responsibilities, select the smallest adequate technology layer, test the work, and keep consequential decisions with people.

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Choose the appropriate path

Start with learning, a decision sprint, or ongoing management.

A manufacturer may need only independent learning. A company with an unresolved operating question may need a focused decision engagement. A team already using approved tools and workflows may need recurring management. Each option can stand alone; choose only what helps now.

Choose implementation only after the problem, owner, information boundary, operating measure, and decision authority are clear enough to evaluate the result.

  1. Learn

    Build practical judgment.

    Start with free individual learning or a bounded company learning engagement.

    Explore Learn
  2. Decide

    Choose where AI fits.

    Begin with a free Fit Call, then use a fixed-scope Direction Sprint when a decision requires evidence.

    Explore Decide
  3. Manage

    Operate and improve AI responsibly.

    Use recurring fractional AI leadership alongside management, process owners, quality, and IT/MSP.

    Explore Manage

Operating sequence

Make the decision before adding more technology.

The method begins with the current workflow and the decision the organization is actually trying to improve. It preserves a stop decision at every stage: education may be sufficient, process correction may come before AI, an existing product may be adequate, or the risk and evidence may not support proceeding.

  1. Frame the work

    Name the process, users, sources, friction, consequence, owner, and current operating measure.

  2. Set the boundary

    Define permitted information, prohibited decisions, human review, escalation, and evidence that must remain visible.

  3. Choose the smallest layer

    Prefer process correction, an approved existing tool, configuration, or supported connection before custom code.

  4. Evaluate realistic cases

    Use representative inputs, explicit acceptance criteria, exception cases, and a named person who can reject the output.

  5. Operate and improve

    Track issues, changes, costs, adoption, workflow performance, and the conditions that require correction, pause, or retirement.

Responsibility model

Managed AI complements the people who already own the business and its systems.

The managed-IT analogy is useful only when the boundary is explicit. Managed IT keeps assigned infrastructure and support operating. Managed AI helps the organization choose where AI belongs, evaluate its work, support approved use, maintain AI-specific operating records, and improve bounded workflows over time.

Who owns what

Four parties keep distinct authority.

An executed agreement defines these responsibilities more precisely. Magnetta does not take control of customer systems, policies, approvals, or consequential decisions by implication.

Magnetta

Owns contracted education, opportunity analysis, workflow design, AI-specific evaluation, operating records, adoption support, and agreed monitoring.

Manufacturer

Owns objectives, policies, records, employee conduct, vendor approval, risk acceptance, funding, and business or production decisions.

IT or MSP

Owns assigned identity, devices, networks, backups, tenant administration, cybersecurity controls, and general technical support.

Specialist authorities

Quality, engineering, legal, HR, security, and other authorized functions retain the decisions delegated to them.

Included

AI operating work Magnetta may accept

  • Education, workflow mapping, tool review, evaluation, adoption support, and documented recommendations
  • Agreed AI issue, risk, change, decision, cost, and workflow records
  • Escalation of material issues observed inside the contracted work

Not included

Authority Magnetta does not accept by implication

  • Final engineering, quality, production, shipment, employment, legal, regulatory, or security decisions
  • Infrastructure and systems administered by the manufacturer or its IT/MSP
  • Guaranteed accuracy, unrestricted third-party operation, or outcomes outside the agreed scope

Technology selection

Use one familiar front door without creating one uncontrolled database.

A useful employee interface can coordinate several approved tools or workflows while each underlying source retains its own permission path, owner, logging, and system-of-record status. Authentication makes a connection possible; it does not authorize every source or action.

Use the layer map below to examine where an interface, knowledge source, bounded workflow, governed connection, system of record, customer-owned capability, and operating responsibility fit. The correct design may stop at any layer; more technology is not automatically a better answer.

Three rules govern the stack

Starting point
Existing behavior and approved tools first
System boundary
ERP, QMS, PLM, CRM, and controlled sources retain assigned authority
Custom capability
Added only after a narrower adequate option has been ruled out
  1. Layer 1

    Familiar employee interface

    Begin in an approved interface employees can use appropriately.

  2. Layer 2

    Approved knowledge and collaboration

    Keep source ownership, permissions, revision, and authority visible.

  3. Layer 3

    Agents and bounded workflows

    Add tools and workflow state only for a defined, evaluated task.

  4. Layer 4

    Governed connections

    Use the least complex approved connector or API that meets the requirement.

  5. Layer 5

    Existing systems of record

    Preserve ERP, QMS, PLM, CRM, and controlled repositories as assigned authorities.

  6. Layer 6

    Custom workflow you own

    Add narrow software only when existing products cannot meet a validated need.

  7. Layer 7

    AI management around every layer

    Assign owners, permissions, evaluation, support, cost, change, and retirement decisions.

Explore the complete Manufacturing AI Technology Stack Browse manufacturing AI Resources

See the approach in practice

Explore demonstrated capability in a working application.

Qualification Compiler lets you inspect how Magnetta built and bounded a working AI application. The example uses fictional data, so use it to evaluate Magnetta’s approach and technical capability—not as a promise of results for your company.

Browse all Case Studies Read the manufacturing AI Blog

A practical next step

Choose the next step that fits your team.

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