Zylo Labs

Measured results

• −35%

  equipment downtime

• +40%

  maintenance efficiency

• −30%

  operational cost

Source: Epsilon AI Success Stories & Project Highlights, Ver. 3 — 15 October 2025, page 5

Technical approach

Data collection

Machinery manuals digitised into JSON for metadata indexing.

Retrieval-augmented generation

BM25 retrieval over the manual corpus, generating repair instructions on demand.

Natural language understanding

BERT and RoBERTa models interpret what the technician typed and route to the right procedure.

Measurement framework

Deployment evidence is drawn from the published engagement. The delivery and measurement plans are case-specific recommendations grounded in the documented solution and results.

Evidence status

The published case study documents an operational predictive-maintenance system combining equipment telemetry, failure prediction and grounded repair guidance.

Indicative delivery plan

Connect historical and live equipment data, validate failure thresholds, pilot alerts with the maintenance team, then expand to the approved asset population.

Recommended baseline

Recommended: the 90-day pre-deployment average for unplanned downtime hours, maintenance labour per work order and maintenance-related operating cost, normalised by equipment operating hours.

Recommended measurement method

Compare matched equipment over equal operating-hour windows before and after deployment. Exclude planned shutdowns, reconcile events with CMMS and production logs, and report monthly with the same asset population.

Recommended items are not presented as the original study method. They should be replaced with the confirmed project methodology when the underlying records are approved for publication.