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AI Monitoring & Security for Small Business — 24/7 Operations

Train AI to learn your daily patterns and alert you when something unusual happens — after-hours entries, repeated unknown visitors, loitering, or access to restricted areas. Local Austin setup and privacy-first practices for small businesses.

Request a monitoring pilot How it works
Fast pilot
Get a lightweight pilot with existing cameras or edge devices — we tune models to your site and provide alerting with human review.

What we offer

We build a monitoring layer that learns what “normal” looks like for your location and alerts on deviations. Our approach blends on-device privacy, configurable sensitivity, and human-in-the-loop verification to reduce false alarms and respect privacy and legal requirements.

Key capabilities

  • Anomaly detection: AI models establish daily/weekly patterns and flag unusual movement or presence.
  • Event classification: tag events like entry, exit, loitering, tailgating, restricted-area access, or unusual hour presence.
  • Configurable alerts: push notifications, SMS, email, Slack, or integration with alarms and security services.
  • Edge processing & privacy: perform inference on local devices to keep sensitive video on-site; send only events or blurred thumbnails to the cloud if desired.
  • Human verification: route flagged events to staff for quick review and confirm before escalation.
Important: audio recording and some forms of surveillance are regulated. Always follow local laws, post required notices, and obtain consents when necessary. We design solutions that prioritize privacy and legal compliance.

Concrete examples — what we can detect

After-hours entry

Detect people entering the facility outside normal business hours and automatically notify managers or security with timestamped evidence.

Unusual frequency

Flag repeat visits by the same person or vehicle that deviate from daily patterns — helpful to spot suspicious loitering or prospected theft patterns.

Restricted-area access

Alert when someone enters a backroom, storage area, or equipment zone they don't normally access during the day.

Tailgating & door breaches

Detect multiple people passing a secure doorway on a single badge swipe or detect doors left propped open.

Suspicious loitering

Identify people who linger in sensitive areas (loading dock, storefront windows) longer than normal thresholds.

Unusual vehicle activity

Track and alert on repeated vehicle stops, unknown plates in restricted lots, or clusters of vehicles at odd hours.

Example: A small retail shop experienced repeated nightly window checks. After a pilot, the system alerted staff on unusual visits between 2–4 AM and provided blurred thumbnails and timing. Staff confirmed increased patrols and adjusted locks — incidents stopped.

How a pilot works (practical steps)

  1. Site review: we audit camera coverage, connectivity, and privacy requirements.
  2. Define normal: collect baseline data (a few days to a few weeks) so the model learns typical entry/exit patterns and schedules.
  3. Model tuning: we configure sensitivity, detection classes, and restricted zones (geofences within camera views).
  4. Deploy: on-premise or hybrid deployment; alerts stream to your chosen channels with thumbnails and metadata.
  5. Human-in-loop: staff review flagged events; we refine thresholds to reduce false positives.
  6. Operationalize: integrate with alarm systems, incident logs, or emergency contacts as required.

Privacy, legal & ethical notes

  • Local rules: we require customers to confirm compliance with local laws and workplace policy (signage, consent where required).
  • Audio: audio capture is sensitive and often more regulated than video; we recommend avoiding audio recording unless fully compliant and with explicit consent.
  • Minimization: we support on-device inference and sending only metadata or blurred thumbnails off-site to minimize privacy risk.
  • Retention & audit: configurable retention windows, secure storage, and audit logs to support accountability and investigations.
  • Non-discrimination: test models for bias and avoid enforcement actions that discriminate — provide human review and appeal processes.