Solutions & Use Cases

Not as a live detector. Those are reached with prompt-driven analysis of recent frames, which runs after the fact on the language model running locally in your deployment. Intrusion and line-crossing rules fire live when someone enters.

Data held in the platform can be exported through the REST API to your own systems, so footage, cases and records are not locked into the deployment.

It is the practice of making an organization's own documents, recordings and records answerable in natural language, with each answer grounded in a specific source rather than generated from a model's general training. Analysts and vendors also describe this as generative AI knowledge management.

A general assistant answers from what it was trained on. This answers from what you gave it, using retrieval-augmented generation, and shows the material each answer came from. Where it has no grounding, confidence thresholds let it decline or escalate rather than produce a plausible answer.

Both. Transcription and OCR put recordings, scanned material and documents into one index, so an answer can come from a meeting recording or a training video as readily as from a policy PDF.

No. Results are permission-trimmed against the access each person already holds, through SSO and role-based access control, so the assistant does not become a route around existing controls.

The citation identifies the material the answer drew on. Judging whether the statement follows from that source remains the reader's job, which is why the source is shown rather than hidden.

Yes. Deployment covers on-premises, private cloud and fully air-gapped environments, with self-hosted models for cases where no data may leave your infrastructure.

Confidence thresholds govern the boundary. Below it, the question is handed to a person through a human-in-the-loop step rather than answered speculatively.

Software that reads camera streams continuously and raises an event when a condition you define occurs -- a person in a restricted zone, missing PPE, a plate on a watchlist, smoke, a vehicle going the wrong way. It replaces someone watching screens, and it runs on the cameras already installed rather than requiring new ones.

No. Detection runs over RTSP and ONVIF against the existing estate and works alongside whatever VMS or NVR is in place. Camera health and auto-reconnect mean you find out when a camera drops rather than discovering it during an investigation.

People, vehicles, PPE, faces, licence plates, weapons, fire and smoke, activity, zone entry and wrong-direction movement, with per-camera configuration and cross-camera re-identification. Where a condition matters and is not covered, a detection model can be trained on your own footage.

The detection is. Scene-level description runs on cached frames on a cadence, so it tells you what has been happening rather than reading the current frame. The alert does not wait for it.

The event triggers a workflow. It can notify, escalate, route to a person for review, or retain the media as a record with chain of custody. Confidence thresholds decide what is acted on automatically and what a person sees.

No. A workflow triggers on an event, not on a state that was already true before it was watching. And the response inherits the accuracy of the detection that raised it.

To a degree, and this is where a describing model beats a class list. Rather than matching against enumerated categories, it describes what a scene showed, which reaches unusual or ambiguous situations -- including on production and packaging lines. It is not a substitute for purpose-built inspection equipment where repeatable measurement against a specification is the requirement.

Yes. The recorded estate is searchable in natural language, and answers return the clip they came from rather than a timestamp to go and find.

Yes. Self-hosted inference supports on-premises, private cloud and fully air-gapped deployment.

Software that keeps recorded calls compliant automatically: bulk audio redaction removes payment card and health data, AI transcription makes every call reviewable, and QA analytics score the whole volume, not a sample.

33+ spoken PII categories across 82 languages: card numbers, CVV, SSNs, bank accounts, SWIFT and IBAN, patient identifiers, and plan numbers, including bilingual calls that switch language mid-sentence.