Intelligent Manufacturing
Unplanned downtime, quality losses or a manual handoff may be limiting output. Before choosing AI, establish the constraint, the available data and the value of removing it.
A manufacturing leader must decide whether better maintenance planning, process redesign or a data-supported intervention can relieve a consequential bottleneck.
We work with production and technical owners to compare process improvements, existing system capabilities and feasible AI approaches. The assessment covers available evidence, integration, safety, human intervention and full incremental costs. Any pilot or implementation follows an agreed scope.
Good output at the constraint, unplanned downtime, accepted defect reduction, maintenance burden and net contribution after integration and operating costs.
Evidence and source investigation, workflow and system mapping, alternatives, investment sensitivity and accountable pilot conditions.
Explore the operating choices, assumptions and decision conditions. Worked examples are labelled where they appear.
Typical decision: Can source-backed service knowledge improve remote troubleshooting before engineers are replaced or AI gets access to the machines (OT)?
Decision owners: CEO/COO, CTO, Head of Service, CFO
Typical decision: Build, buy or integrate an engineering agent? Local or cloud? Who holds technical authority?
Decision owners: CTO, Digital Product, Procurement
Typical decision: Which AI/automation investment improves cycle time, quality or after-sales, and who owns the machine data?
Decision owners: Engineering, Service, Finance
Typical decision: Safe programming, simulation and commissioning: what evidence must a supplier provide?
Decision owners: Engineering, Quality, Procurement
Typical decision: Predictive maintenance vs. better monitoring vs. existing controls: what pays off net?
Decision owners: Plant operations, Maintenance, Finance
Segmentation from . These are hypotheses for a conversation, not claims about named companies.
1. After-sales and technical service intelligence (flagship). Does a human-approved diagnostic assistant raise first-time resolution and reduce avoidable trips, without unsafe machine actions and without losing customers' machine-data rights? Measured in: accepted cases, cost per accepted case, repeat visits, review effort. 2. Agentic automation engineering and commissioning. Should you adopt an engineering agent from a controls vendor, integrate an open platform or build a narrow internal assistant? These are different product types, so the first job is to establish functional fit. Measured in: accepted engineering tasks, correction hours, commissioning time, IP and security constraints. 3. Quality, condition and supplier trust. When do visual inspection, predictive maintenance or robotics justify a pilot, and when do existing controls do better? Measured in: accepted detections, false rejects, downtime, maintenance cost.
What it is: Better manuals, ticket classification, training, no new AI
When it fits: When the bottleneck is knowledge structure, not AI
What it is: Answers cite manuals and versions; a technician validates; no machine writes
When it fits: First, low-risk step for service knowledge
What it is: Bounded retrieval plus authorised service-system actions (draft a case, request approval); no direct machine control
When it fits: When O1 works and authority is defined
What it is: A separate use case
When it fits: Only with reliable fleet telemetry and validated failure labels
Options from . Supplier examples (e.g. Siemens, Bosch Rexroth, Beckhoff, Festo, SEW-EURODRIVE, DMG MORI, KUKA) are capability references from their own 2026 publications, not ETHORITY validation or a shortlist .
Fully synthetic example of a fictional German special-machine builder. It makes no claim about any company .
Inputs: 1,200 installed machines · 2 incidents per machine per year · 55% eligible · 40% completed with AI assistance · 90% quality-accepted · €500 avoidable effort per accepted case · €105,000 annual run cost.
The chain: 2,400 incidents → 1,320 eligible → 528 assisted → 475 accepted → €237,600 gross potential.
Annual result: −€21,840
Annual result: +€13,800
Annual result: +€49,440
Break-even is about 44% conversion. Below that, a labour-cost case does not justify the investment, and redesign or stopping is the honest outcome. (Method: ETHORITY Capacity Conversion .)
- Volumes: ticket counts by machine model, fault family and severity. - Service performance: response and resolution times; field-engineer effort. - Costs and inputs: the real avoidable marginal cost; manual and version accuracy; technician re-check time. - Rights and safety: - data ownership and customer licences; - the IT/OT boundary; - cybersecurity and industrial-safety sign-off; - escalation error cost. - Commercials: vendor prices and contract terms.
Unknown is a valid answer; we record it instead of guessing .
We draft, and you own, the criteria and the weighting : 1. Scenario and data rights. 2. Performance on a representative anonymised ticket hold-out. 3. Grounding and manual-version citation. 4. Abstention and calibrated uncertainty. 5. Human approval and escalation. 6. Least-privilege access to ERP/PLM/MES/ticketing. 7. Separated IT/OT write permissions, with no unsanctioned machine control. 8. Security, regions and integrations. 9. Change, version and deprecation policy. 10. Licence and usage pricing basis. 11. Reliability and fallback. 12. Pilot success/failure criteria, data deletion and exit. 13. IP, confidentiality and vendor conflicts.
- Scope: a human-reviewed knowledge and service workflow on approved historical or anonymised documents. - Not allowed: autonomous PLC or machine writes, safety bypasses or parts purchasing. - Comparison: O0, O1 and O2 under the same quality rubric. - Hard stops: unauthorised access, hallucinated safety instructions, a confidentiality incident, unbounded cost .
Can AI improve yield automatically? Not as a claim. We compare against your observed quality and production data and a no-AI baseline .
Do we need new machinery? Not necessarily. We evaluate the existing systems first .
What if safety is involved? Qualified safety engineering, the regulator and the vendor's responsibility remain a separate gate .
Technical and organisational assessment. Legal and safety-engineering decisions need qualified advice. Calculations are scenarios; vendor references establish no endorsement.
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Name the process owner, data authority, reviewer and outcome acceptor. Specify the work that may happen and where a human must intervene.
Retain the baseline, case mix, quality threshold, review effort, rework and integration costs. Include a credible non-AI alternative. Released time needs an agreed destination before its value is claimed.
Agree the conditions for a limited test, the evidence needed to expand and the reasons to redesign or stop. A market profile establishes neither workflow suitability nor a customer outcome.
485 catalogue entries. Counts show membership in each category; entries can belong to several categories.
The map shows a selection of offerings; open a topic area to see all of them. Offerings with the most documented facts appear first, then in alphabetical order. The order shows only how much is documented; check the profile to see whether an offering fits you.
Catalogue snapshot: . Offerings as of 23 Oct 2026. Every fact shows its source and date; before you decide, check whether it applies to your use.
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