Actuate

Name: Actuate
Brand: Actuate

Actuate turns any security camera into a smart camera to keep people safe.

Est 2018|Valuation $59M|Raised $21.9M|3 Ref Customers

LinkedIn 7K|Web traffic 4K|FTEs 38|HQ New York, New York, US

MarketsPhysical Security Systems

01Competitive Landscape

Narrative analysis

- Actuate vs ZeroEyes: ZeroEyes emphasizes a human verification layer and publishes some pricing examples for small deployments. Actuate presents a broader analytics portfolio (intrusion, loitering, crowding, fire, visible weapons) and focuses on integration into existing VMS workflows. Actuate’s public materials offer less standardized pricing and limited independent performance validation in the sources reviewed. - Actuate vs Omnilert: Omnilert describes an on-premise appliance approach with device price references that may appeal to organizations that require local processing. Actuate’s cloud-based approach reduces on-premise hardware needs but increases the need for clarity on data transfer and security attestations.

Market adoption signals

Public-facing adoption claims are vendor- or partner-stated; presence in Milestone’s partner ecosystem and partner customer stories indicate some channel traction but do not substitute for broad third-party reviews.

	Actuate (actuate.ai)	ZeroEyes	Omnilert Gun Detect (appliance)
Starting Price	Not consistently published as fixed $/mo (per-camera tiers; quote/scale dependent)	Example self-serve plan examples exist with published figures for small deployments (varies by term)	Appliance price points are referenced in vendor materials (deployment-dependent)
Primary Use Case	Monitoring centers and integrators seeking cloud analytics integrated into VMS and a multi-analytics suite	Organizations focused on weapon detection with a human verification service model	Buyers preferring a local appliance option and bundled monitoring services
Key Attributes	Broad set of analytics, VMS integration, optional add-ons (dynamic slicing, dewarping)	Human-verified detections and a defined monitoring model	On-prem device model that may align with strict network policies
Key Limitations	Limited publicly verifiable accuracy/false-positive benchmarks and limited mainstream review footprint	Less emphasis on broader operational analytics beyond weapon detection	Appliance maintenance and lifecycle management can raise TCO

01Competitive Landscape

Narrative analysis

Actuate vs ZeroEyes: ZeroEyes emphasizes a human verification layer and publishes some pricing examples for small deployments. Actuate presents a broader analytics portfolio (intrusion, loitering, crowding, fire, visible weapons) and focuses on integration into existing VMS workflows. Actuate’s public materials offer less standardized pricing and limited independent performance validation in the sources reviewed.
Actuate vs Omnilert: Omnilert describes an on-premise appliance approach with device price references that may appeal to organizations that require local processing. Actuate’s cloud-based approach reduces on-premise hardware needs but increases the need for clarity on data transfer and security attestations.

Market adoption signals

Public-facing adoption claims are vendor- or partner-stated; presence in Milestone’s partner ecosystem and partner customer stories indicate some channel traction but do not substitute for broad third-party reviews.

	Actuate (actuate.ai)	ZeroEyes	Omnilert Gun Detect (appliance)
Starting Price	Not consistently published as fixed $/mo (per-camera tiers; quote/scale dependent)	Example self-serve plan examples exist with published figures for small deployments (varies by term)	Appliance price points are referenced in vendor materials (deployment-dependent)
Primary Use Case	Monitoring centers and integrators seeking cloud analytics integrated into VMS and a multi-analytics suite	Organizations focused on weapon detection with a human verification service model	Buyers preferring a local appliance option and bundled monitoring services
Key Attributes	Broad set of analytics, VMS integration, optional add-ons (dynamic slicing, dewarping)	Human-verified detections and a defined monitoring model	On-prem device model that may align with strict network policies
Key Limitations	Limited publicly verifiable accuracy/false-positive benchmarks and limited mainstream review footprint	Less emphasis on broader operational analytics beyond weapon detection	Appliance maintenance and lifecycle management can raise TCO