Novity

• If procurement requires extensive third-party review validation from G2/TrustRadius/Gartner Peer Insights before vendor consideration; the vendor has limited public review coverage.
• If you need a full enterprise APM suite with deep CMMS/EAM workflow support and extensive prebuilt asset libraries; incumbents may be a safer option.
• If you require a standardized turnkey hardware+sensor deployment from the vendor; sensor-centric providers may provide a clearer hardware offering.
• If your procurement process requires immediate, public evidence of SOC 2 Type II / ISO 27001; such documentation is not publicly posted in the reviewed materials.

• G2: No clear product page for Novity TruPrognostics was found in the searches performed; “Novity” searches can return unrelated companies.
• TrustRadius: No product profile for Novity TruPrognostics was identified in the searches performed.
• Gartner Peer Insights: No publicly accessible product page for Novity TruPrognostics was found during this run.

Implication: There is limited third-party review data from mainstream B2B review platforms to validate vendor claims via aggregated ratings.

Vendor-specific discussion threads were not readily surfaced on Reddit or other public PdM forums in this research. Publicly available community evidence is therefore limited to the vendor’s LinkedIn page and general industry discussion threads about PdM practices.

What practitioners commonly complain about in PdM (category-level observations from public forums)

These category-level pain points are common in PdM evaluations and should be tested with Novity during procurement:

• Limited labeled failure data and site-specific configurations complicate model development; some PdM products effectively provide advanced anomaly detection rather than calibrated prognostics.
• Translating anomaly alerts into actionable maintenance work orders and timing decisions can be difficult.
• Integration effort: accessing OT systems, cleaning tags, and establishing trust in model outputs often extend time-to-value.

Quotes cited in original searches reflect general practitioner skepticism about PdM ROI and setup effort; these quotes are category-level and not specific to Novity.

Vendor-published customer outcomes (case study)

The vendor’s compressor case study reports a very high accuracy rate for valve fault prognoses at a natural gas plant. This is a vendor-published outcome; methodology details (accuracy definition, sample size, false positive rate, and baseline comparison) were not disclosed in accessible materials and should be requested for independent validation.

• Source: Vendor case study (vendor-published).
• Claimed results: Claims very high accuracy for valve fault prognoses; described by the vendor as integrated into the site’s maintenance operations.

Critical evaluation:

• Independence: This is a vendor-published case study; no independent validation was identified.
• Missing methodological details: The case study does not provide a clear definition of what constitutes a “valve fault,” the sample size, false-positive rates, lead-time distribution, or baseline comparison. These details should be requested during procurement.

Novity markets TruPrognostics as a predictive maintenance (PdM) / prognostics product focused on Remaining Useful Life (RUL) estimation for industrial equipment (e.g., pumps, motors, compressors). The vendor describes a hybrid approach combining machine learning and physics-based models and states the system can produce RUL estimates when historical failure data are limited. The vendor indicates the solution can operate using existing SCADA/DCS/historian/IoT data in many deployments, with the option to recommend supplemental sensors where needed.

• Target users: Reliability engineering, maintenance leadership, plant managers, and operations teams in asset-intensive industrial environments.
• Target industries (per vendor materials and funding announcements): process industries such as oil & gas, chemicals, and manufacturing; the vendor materials also reference other industrial verticals.
• Positioning: The vendor emphasizes RUL-focused prognostics (predicting when maintenance will be needed) rather than solely anomaly detection.