Novity

Name: Novity
Brand: Novity

AI Native

Reduce Unplanned Downtime With Your Data

Est 2022

LinkedIn 1K|Website|FTEs 7|HQ San Carlos, California, US

MarketsPlant Maintenance

01Competitive Landscape

Narrative analysis

Where Novity may provide value: - Hybrid physics + ML approaches can be beneficial when failure labels are scarce and when extrapolation to RUL is required. - Starting with existing OT/historian data may lower initial sensor deployment costs for some sites. Where competitors may be preferable: - Vendors that supply standardized hardware packages plus analytics (e.g., sensor-centric providers) may be quicker for programs that need vendor-managed sensing. - Large incumbents (Siemens, AspenTech, GE Digital) may offer broader enterprise integrations, established references, and more published compliance documentation. Pricing note: Many enterprise PdM/APM vendors are quote-led; public pricing is uncommon for enterprise-grade prognostics offerings.

	Novity (TruPrognostics)	Siemens Senseye Predictive Maintenance	Augury (Machine Health / Process Health)
Starting Price	Not publicly listed (quote/demo-led). A separate consumer-facing site appears unrelated.	Quote-led; enterprise pricing model.	Quote-led; commercial engagements typically start with vendor consultation.
Suited to	Asset-intensive sites seeking RUL-focused prognostics and a hybrid physics/ML approach; organizations with historian/SCADA data.	Organizations seeking a Siemens-supported PdM offering with broad services and integration options.	Sites looking for a packaged approach combining sensors, analytics, and vendor services.
Key Advantages	RUL-focused positioning and hybrid modeling approach intended to perform in limited-fault-data scenarios (vendor claim).	Large vendor ecosystem, services, and enterprise procurement familiarity.	Packaged sensor+software+services model aimed at rapid program rollout.
Key Weaknesses	Limited independent public references and limited public security/compliance documentation; performance claims require validation.	Can be complex to deploy at scale; pricing and configuration can be opaque.	Typically requires sensor investments; may be less focused on starting from existing historian-only data.

01Competitive Landscape

Narrative analysis

Where Novity may provide value:

Hybrid physics + ML approaches can be beneficial when failure labels are scarce and when extrapolation to RUL is required.
Starting with existing OT/historian data may lower initial sensor deployment costs for some sites.

Where competitors may be preferable:

Vendors that supply standardized hardware packages plus analytics (e.g., sensor-centric providers) may be quicker for programs that need vendor-managed sensing.
Large incumbents (Siemens, AspenTech, GE Digital) may offer broader enterprise integrations, established references, and more published compliance documentation.

Pricing note: Many enterprise PdM/APM vendors are quote-led; public pricing is uncommon for enterprise-grade prognostics offerings.

	Novity (TruPrognostics)	Siemens Senseye Predictive Maintenance	Augury (Machine Health / Process Health)
Starting Price	Not publicly listed (quote/demo-led). A separate consumer-facing site appears unrelated.	Quote-led; enterprise pricing model.	Quote-led; commercial engagements typically start with vendor consultation.
Suited to	Asset-intensive sites seeking RUL-focused prognostics and a hybrid physics/ML approach; organizations with historian/SCADA data.	Organizations seeking a Siemens-supported PdM offering with broad services and integration options.	Sites looking for a packaged approach combining sensors, analytics, and vendor services.
Key Advantages	RUL-focused positioning and hybrid modeling approach intended to perform in limited-fault-data scenarios (vendor claim).	Large vendor ecosystem, services, and enterprise procurement familiarity.	Packaged sensor+software+services model aimed at rapid program rollout.
Key Weaknesses	Limited independent public references and limited public security/compliance documentation; performance claims require validation.	Can be complex to deploy at scale; pricing and configuration can be opaque.	Typically requires sensor investments; may be less focused on starting from existing historian-only data.