Visual detection
Detect configured faces, people, plates, regions, screens, documents, objects, or scene areas.
Accessibility, Language & Privacy
Real-Time AI Privacy Shield
Automatically detect configured sensitive visual and audio information and apply approved blur, masking, replacement, or muting policies before media reaches viewers, recordings, archives, or downstream systems.
Privacy controls are often applied only after the original media has already been transmitted or stored. OPTIME can integrate AI directly into WebRTC, broadcast, surveillance, recording, transcoding, and streaming pipelines so organizations can control what leaves the trusted boundary.
WHY THIS MATTERS
Privacy must be enforced inside the media path-not after sensitive content has already moved.
Video and audio systems capture more information than every viewer or downstream system needs.
When privacy transformation occurs only during post-processing, the original media may already have left the organization.
- unrelated bystanders;
- minors;
- protected participants;
- license plates;
- private properties;
- computer screens;
- documents;
- selected regions;
- confidential speech.
Detect configured sensitive visual and audio content and apply privacy policy before live or recorded media reaches viewers, storage, or downstream systems.
SYSTEM PRESSURES
Why simple implementations break down.
Privacy is applied too late
Original media may be transmitted, recorded, or copied before redaction.
Detection and policy are confused
A model output should not independently determine who must be hidden or what must be muted.
Tracking fails between frames
Masks may disappear during movement, scale changes, occlusion, or camera motion.
Audio and video policies are separate
Sensitive speech and sensitive visuals may need one coordinated media policy.
Every viewer receives the same stream
Different roles may require original, partially protected, or fully anonymized media.
Central processing exposes source media
Sending raw content to another region or cloud may defeat the privacy objective.
COMPLETE SOLUTION
Engineer the workflow as one controlled production system.
The complete system can connect video decode, audio extraction, face detection, person detection, object detection, license-plate detection, OCR, screen and region detection, tracking, segmentation, speech recognition, sensitive-phrase detection, privacy policy, masking, blur, pixelation, replacement, muting, tone or noise substitution, encoding, streaming, recording, audit-supporting metadata.
CAPABILITY GROUPS
What the complete Solution can contain.
Tracking and persistence
Maintain the privacy transformation through motion, occlusion, camera movement, and frame transitions.
Visual transformation
Apply blur, pixelation, fill, replacement, cropping, region masking, background treatment, or configurable overlays.
Identity-aware policy
Where authorized, distinguish allowlisted participants, protected identities, known roles, or unknown bystanders without claiming universal recognition.
Audio privacy
Mute configured words, phrases, speakers, or time ranges and support tone, noise, or replacement behavior.
Real-time media integration
Integrate with WebRTC, SIP media, RTP, RTSP, broadcast, surveillance, recording, transcoding, and streaming paths.
Multi-output policy
Produce different protected outputs for different viewers, recordings, exports, or downstream systems.
Edge and private processing
Transform media close to capture so the unprotected source does not need to leave the trusted environment.
ARCHITECTURE FLOW
AI Detection → Privacy Policy → Transformation
Models identify what may be present. Explicit policy determines what should happen to it, for which viewer or destination, before an approved transformation produces the output.
Camera
Camera, microphone, call, stream, recording, or media source
Capture
Capture, decode, synchronization, and source identity
Face
Face, person, object, plate, screen, document, and speech detection
Tracking
Tracking, segmentation, timestamps, confidence, and evidence
Privacy policy
Privacy policy, role, destination, and authorization
Blur
Blur, mask, replace, crop, mute, or substitute
Encode
Encode, package, and produce approved output variants
Call
Call, stream, record, archive, export, or application delivery
Policy logs
Policy logs, model versions, quality, performance, and review
Private GPU
Private GPU, edge, media-server, or batch infrastructure
ARCHITECTURE DECISIONS
Make important trade-offs explicit before they become operating constraints.
Pre-distribution or post-recording protection
Approach A
Pre-distribution
Approach B
post-recording protection
Selection considerations
Protect before distribution when viewers or downstream systems should never receive source media.
Reversible or irreversible privacy
Approach A
Reversible
Approach B
irreversible privacy
Selection considerations
Retain original content only when policy and authorized review require it.
Edge or centralized processing
Approach A
Edge
Approach B
centralized processing
Selection considerations
Use edge processing when source-media exposure, bandwidth, or latency matters.
All-person or policy-aware masking
Approach A
All-person
Approach B
policy-aware masking
Selection considerations
Define role, consent, identity, scene, and destination rules separately from detection.
Model confidence or safe fallback
Approach A
Model confidence
Approach B
safe fallback
Selection considerations
Determine whether uncertain detections should trigger broader masking, human review, or another policy.
REPRESENTATIVE SCENARIOS
How the Solution applies in representative situations.
Representative examples: Each scenario separates model detection from the policy that determines which transformation and output are authorized.
Bystander protection
- Scenario
- Keep approved subjects visible while anonymizing unrelated people.
Minor protection
- Scenario
- Apply configured masking rules to children in education, public, media, or healthcare settings.
License-plate redaction
- Scenario
- Protect vehicle identifiers in live or recorded footage.
Privacy-protected video calls
- Scenario
- Apply participant, background, screen, or region policies before distribution or recording.
Surveillance export
- Scenario
- Create a protected version for sharing while preserving restricted source evidence for authorized investigators.
Sensitive-audio redaction
- Scenario
- Mute configured phrases or segments from a live or recorded path.
ENGAGEMENT APPROACH
Move from operating context to a validated production system.
Phase 1
Privacy and media-boundary assessment
Define sources, sensitive content, viewers, outputs, latency, quality, policy, retention, and deployment.
Phase 2
Representative privacy pilot
Prove detection, tracking, transformation, media quality, and failure behavior on representative footage.
Phase 3
Pipeline integration
Integrate models, policy, GPU processing, media servers, applications, outputs, and monitoring.
Phase 4
Quality and operational validation
Test false negatives, false positives, occlusion, quality, performance, policy changes, and review.
POSSIBLE DELIVERABLES
Evidence and implementation that can transfer into operation.
Privacy-processing architecture
Detection and tracking pipeline
Privacy-policy service
WebRTC or media-server filter
GStreamer or FFmpeg integration
Broadcast or surveillance integration
Audio-redaction pipeline
GPU-optimized runtime
Review and policy application
Monitoring and quality evaluation
Edge appliance software
SDK or API
Final deliverables depend on the selected architecture, validation evidence, and engagement scope.
Start with the workflow, evidence, integration, deployment, performance, and operating constraints that determine whether this system can work in production.
START A TECHNICAL CONVERSATION
Discuss the system, constraints, and engineering outcome.
Share the product, workload, architecture, infrastructure, performance, security, or delivery constraint that must be resolved.
Austin, Texas
Distributed engineering teams across North America, Europe, the Caucasus, and Latin America.
[email protected]