Regulus
Reliability Data Systems
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Reliability Data Remediation · Heavy Industry

Years of downtime data. None of it can defend a maintenance strategy.

Regulus turns noisy CMMS and downtime records into clean, decision-grade reliability data — aligned to your existing asset and reliability structures, or to ISO 14224 where you choose the standard. Cleaned, coded and human-verified inside your own boundary. Your data, your hardware if you choose, your model weights.

WO 4831202 · SAP PMIW29 EXPORT · ANONYMISED
As found

“pmp trippd again brg running hot changed seal last month”

Damage code: 9999 — OTHER · Cause: blank · Object part: blank · Downtime: 5.4 h, unattributed
Classified · Verified · Aligned
As delivered
Equipment class
Centrifugal pump
Subunit
Pump unit
Maintainable item
Bearing
Failure mode
OHE — Overheating
Failure cause
Inadequate lubrication — maintenance-related
Detection
Temperature alarm — trip
Downtime
5.4 h · repair 3.1 h / logistics 2.3 h
Repeat-failure chain linked: 3 prior events, same pump, 11 months
The problem, stated as queries

Questions your history should answer — and currently can't

01MTBF, critical pump fleet, 5 yr Insufficient data
71% of failures coded “OTHER” — no failure-mode split possible
02Top failure mode, HV motor fleet Insufficient data
Cause field blank on 8 of 10 work orders — evidence lives in free text
03Downtime attributable to lubrication failures Insufficient data
Downtime captured, never attributed to failure mode or maintainable item
Weibull — critical pump fleet (impeller)β 2.3 · η 4,120 h
9990201051 63.2 1001,00010,000 UNRELIABILITY F(t) % OPERATING TIME — HOURS (LOG)
What those queries should return — a shape and a life you can take to a capital committee. Today's data can't support the fit.

If the codes are noise, everything built on them inherits the noise — every FMECA, every RCM review, every condition-monitoring business case. The evidence exists. It's trapped in free text.

How it works

Audit. Remediate. Sustain.

A fixed-fee audit first, so you know exactly what your data can and cannot support before committing to remediation. The sequence is deliberate — each stage pays for the decision to take the next.

01 — Audit

Data quality audit

Your history scored against your own asset and reliability structures — or ISO 14224, where the standard is the target: code integrity, field completeness, downtime attribution. Quantifies the decision cost of what's missing and maps the remediation. The report is yours — whoever you hire to act on it.

1–2 weeks · Fixed fee
02 — Remediate

History rebuilt to standard

AI-assisted classification of free text into equipment class, subunit and maintainable item, with failure mode, mechanism, cause and detection method. Every machine suggestion carries a confidence score; low-confidence records are human-verified. Delivered as clean tables your EAM and BI consume directly — coded to your catalogue, or to the standard where you choose it.

2–8 WEEKS · SCOPED PER SITE
03 — Sustain

It stays clean

Monthly ingestion, exception queues for new records, code-catalogue governance and drift reporting — so the standard holds after the project ends, not just at handover.

Monthly · Per site
Capture at source

Clean data starts at the breakdown

Remediation fixes the history you have. Capture fixes the history you're about to write. A Regulus capture agent — a phone line or chat the artisan reaches during the breakdown — takes the story in their own words, asks the follow-ups a reliability engineer would, and writes the record fully coded into your EAM. Ninety seconds at the machine instead of a Monday batch entry.

  • In their language — English and the working languages your crews actually use. The model is fine-tuned on your catalogue and your plant's shorthand; it speaks plant, not textbook.
  • Coded on the spot — functional location resolved, failure-mode candidates with confidence scores, the downtime clock split live between repair, logistics and waiting.
  • No identities stored — speech and text processed on your hardware; the record captures the equipment story, not who reported it. No operator or technician information is kept. Low-confidence records queue to a human before anything posts.
Breakdown line · 02:47Voice or chat · In-boundary
AgentWhich machine are you standing at?
ArtisanS2 transfer pump — tripped on temp, bearing's running hot.
AgentDrive-end or non-drive-end?
ArtisanDrive end. We changed the seal on this one last month.
AgentLogged. Is it running again, or standing?
ArtisanStanding — waiting on the millwright.
Record written · Centrifugal pump · Drive-end bearing · OHE — Overheating · Clock: waiting-on-labour · Repeat flag: 2nd event, 11 months · → EAM notification 10004182
Security & data handling

Processed inside the boundary

Regulus works only on equipment data — downtime, work orders and notifications. Operator and technician fields are excluded at ingestion, so there's no personal information in our pipeline to begin with. What remains is still production data — original values preserved, every transformation logged, and nothing that leaves your boundary.

No personal information

We ingest only equipment, downtime, work-order and notification fields. Operator, technician and HR-linked delay reasons are excluded before processing — they never enter our pipeline.

On-premise processing

A GPU appliance in your server room running our stack. Records never leave your network. An in-region private-cloud option exists for sites that prefer it.

No cross-border transfer

No third-country APIs in the pipeline. Data-residency rules never come into question, because nothing crosses a border to begin with.

Your weights, your property

Models fine-tuned on your data belong to you. Contractually. They don't train anyone else's product.

For a listed company, downtime data is production data — price-sensitive by definition. It should never transit a public API, so in our pipeline it doesn't.

Private fine-tuning

Models trained on your vocabulary

Every plant writes its own shorthand. Generic models guess at it; a model fine-tuned on your code catalogue, asset structure and years of long text doesn't have to.

  • Remediation classifiers — fine-tuned on your verified records, so accuracy climbs and review cost falls with every batch.
  • Domain models — trained on your standards, COPs and procedures for retrieval and drafting that speaks your plant's language.
  • Deployed on your hardware — or in-region GPU infrastructure under a data-processor agreement. Weights are contractually yours.
Practitioner-led

Built by engineers who sign off maintenance strategies and write the capital motivations that bad data blocks — fifteen-plus years in heavy-industry asset management and reliability engineering, now paired with private AI infrastructure. Not a consultancy that read the standard. People who live with its absence.

Start with the audit

Find out what your history can prove — before you need it to.

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Founding-client pricing for the first three sites, in exchange for a written case study.

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