Blogs
February 10, 2026
Thomas Alflen
Oddity.ai
Most AI systems built on video analytics originate from security, retail, or smart city environments. Their underlying assumptions are familiar: continuous monitoring, dense dashboards, frequent alerts, and a strong focus on maximizing model accuracy.
Care environments operate under a fundamentally different reality.
In human services and care organizations, the goal is not surveillance. It is situational awareness that arrives fast enough to preserve context, is private enough to protect dignity, and is restrained enough to support professional judgment rather than override it. This difference fundamentally changes how real-time detection in care must be designed.
In platforms like Oddity.ai, this translates into product decisions that deliberately avoid constant visibility and instead focus on selective awareness. The system is designed to remain operationally quiet until a relevant pattern emerges. This prevents the common IT problem of deploying yet another system that demands ongoing attention or supervision.
For Directors and IT leaders, the relevant question is no longer whether AI can technically detect certain behaviors. The more important question is whether the system behaves appropriately once detection occurs, under real operational conditions.
In most care facilities, incidents involving aggressive behavior or physical abuse rarely begin abruptly. They tend to develop in stages such as verbal escalation, changes in proximity, or shifts in physical interaction. These transitions often unfold over seconds.
Without automated support, these moments may take minutes or hours to be noticed. In some cases, they are not detected at all until after harm has already occurred. Traditional responses often involve adding more screens, more feeds, or more manual monitoring. In practice, this increases cognitive load without reliably improving outcomes.
The core challenge is achieving awareness without creating a surveillance environment.
Oddity.ai operationalizes this by explicitly rejecting continuous monitoring as a design goal. Detection runs in the background and only surfaces information when predefined behavioral thresholds are crossed. For IT teams, this avoids the creation of a parallel monitoring operation that would otherwise require staffing, escalation policies, and governance expansion.
From an IT perspective, this approach prevents several common issues:
Privacy-first video analytics, in this context, is not about feature differentiation. It is about avoiding operational and governance overhead.
Care-specific detection systems start from a different premise. Video itself is not the product. Awareness is. This leads to deliberate design choices that differ from general-purpose analytics platforms.
Behavior-based rather than identity-based detection
Instead of facial recognition or individual tracking, detection models analyze behavioral patterns such as movement dynamics, proximity changes, posture, and interaction sequences. Oddity.ai implements this through behavior-pattern-based detection without facial recognition. For IT leaders, this avoids the introduction of identity data that would otherwise trigger additional privacy impact assessments, consent models, or data subject access processes.
No continuous monitoring
The system does not present live dashboards or require staff to actively observe AI output. In practice, this means Oddity.ai does not create an expectation of supervision. The platform does not introduce an “AI screen” that someone needs to own. This avoids the common IT pitfall of deploying technology that quietly becomes a human monitoring obligation.
Explicit human-in-the-loop boundaries
Detection does not trigger automated actions or enforcement. It provides awareness signals only. Oddity.ai enforces this boundary at the product level. Alerts inform, but never prescribe action. This design prevents liability ambiguity and ensures that professional responsibility remains clearly human, not algorithmic. This boundary is essential for responsible human-in-the-loop AI systems in care.
These choices are not technical compromises. They are safeguards that translate care values into system behavior.
Why sub-second speed matters without creating urgency
Detection speeds are often misunderstood. In care environments, speed is not about generating alarms. It is about preserving situational context. Oddity.ai’s detection ensures that alerts are generated while an interaction is still unfolding. From an IT standpoint, this avoids the problem of alerts that arrive too late to be useful, which often leads to staff ignoring them altogether.
Why realistic coverage is safer than perfect claims
Care environments are inherently complex. Occlusion, varied room layouts, and unpredictable movement patterns make claims of perfect detection unrealistic. Oddity.ai is designed around transparent (true positive) coverage above 80 percent, with clearly defined boundaries. This prevents the IT risk of false certainty, where systems are assumed to catch everything and gaps are only discovered after incidents.
Why alerting design determines operational success
Alerts in care must be selective and meaningful. Oddity.ai implements selective, real-time alerts without escalation language or forced workflows. For IT leaders, this avoids alert fatigue, reduces downstream ticketing or incident noise, and prevents the need for complex alert routing rules.
Model accuracy is relatively easy to benchmark in isolation. Alert behavior is not. Alert behavior is not a byproduct of the model. It is a deliberate product decision.
In Oddity.ai, alert design is treated as:
If alerts require constant attention, systems fail operationally. If alerts imply urgency every time, they fail culturally. By designing alert behavior as a first-class product concern, Oddity.ai avoids creating a system that technically works but operationally collapses under its own noise.
For many IT leaders, architectural compatibility is decisive. Oddity.ai is designed to integrate via RTSP with existing camera infrastructure, without requiring new hardware or replacement of existing VMS platforms. Deployment occurs in a fully private cloud environment.
This design prevents several common IT issues:
From a compliance perspective, this supports HIPAA compliant video monitoring through end-to-end encryption, absence of third-party data access, and audit-ready logs.
Before adopting any real-time detection platform for care, IT leaders should critically evaluate several assumptions.
These considerations are often more important than benchmark scores or marketing claims.
A real-time detection pilot should not be judged on promises, but on observable behavior.
In a limited Oddity.ai pilot, IT leaders can concretely evaluate:
These signals provide far more insight than any specification sheet. In care environments, trust is built through systems that behave predictably, respect boundaries, and reduce operational risk rather than shifting it.
It prioritizes awareness over surveillance and avoids continuous monitoring.
No. Behavior-based detection preserves dignity without sacrificing situational awareness.
Private cloud deployment, encryption, no third-party access, and audit-ready logs.
Sub-second detection preserves context without creating urgency.
Yes. RTSP-based integration avoids infrastructure change.
If you’re exploring real-time detection for care, we can help you assess product design and alert behavior—not just model specs.
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