AI-Powered Video & Audio Analytics for Broadcast Pipelines
Automate scene, object, and quality analysis to reduce manual routine and operator workload. With AI-driven detection and scoring, you achieve consistent results across mixed-vendor broadcast ecosystems—from contribution to playout and monitoring.
Promwad designs and integrates AI video / audio analytics as product-ready components for broadcast and Pro AV vendors. We combine embedded systems, FPGA, and ML expertise to meet real-world latency and interoperability requirements.
Our Partners and Companies Employing Promwad Solutions
Built for Broadcast Vendors Who Need QC at Scale
Modern broadcast pipelines are increasingly complex and mixed-vendor by design. Relying on manual QC and operator compliance checks makes it difficult to maintain consistent quality and compliance at scale.
If this sounds familiar, you’re not alone:
✓ Manual QC don’t scale: operators watch, mark, verify—again and again.
✓ Results vary by team, region, and shift, creating disputes and rework.
✓ Quality is hard to maintain across mixed-vendor devices, formats, and transport layers.
What changes with Promwad analytics:
✓ Reduce operator routine via automated detection and scoring.
✓ Improve consistency with measurable metrics: confidence, thresholds, audit trails.
✓ Accelerate roadmap by plugging in a team that understands broadcast constraints: latency, interoperability, and predictable releases.
What We Deliver
Promwad help you productize analytics as a capability inside your device/software stack:
Content Understanding
Scene segmentation & scene change detection
for structure, highlights, and navigation
Object / person / brand / logo detection
with tracking over timecodes
Configurable event detection
aligned to your domain: studio, sports, news, live events, and Pro AV
Quality Analytics for Video & Audio
Perceptual quality scoring
plus artifact detection: blockiness, blur, noise, freeze, stutter
Audio analytics
loudness/peaks, silence, presence, and anomaly signals to speed QC and troubleshooting
Stream health signals
dropouts and A/V sync flags (where available in your pipeline)
Workflow Outputs Vendors Can Ship
Metadata generation
timecodes, tags, confidence scores—ready for indexing and search
Alerts, dashboards, APIs & webhooks
to integrate into your product UI and monitoring layer
Exportable reports
for compliance evidence and faster customer support resolution
Want to see what “analytics as a feature” looks like in your product?
Vadim Shilov, Head of Broadcasting & Telecom at Promwad
Why Promwad
We plug in fast—at any stage. PoC, integration, rescue or scaling: we can join where your team needs momentum without adding chaos.
Engineering credibility you can take to your roadmap review:
Compatible by Design: Broadcast Protocols & Tech
Broadcast AI-driven analytics is only valuable when it fits your transport, timing, and deployment constraints. We design around the standards you already ship.
- CPU / GPU / FPGA offload depending on latency and BOM targets.
- Low-latency pipeline practices (e.g., optimized data paths and device-level constraints) for real-time environments.
- Model selection and customization per target classes/events.
- Dataset strategy (labeling, balancing, edge cases).
- Optimization for streaming inference (latency, batching, quantization where applicable).
- ST 2110 + NMOS for studio IP cores and routing
- PTP 1588, QoS, IGMP for sync and multicast health
- AES67 / Dante, NDI for audio IP and cost-efficient AV-over-IP
- SRT / RIST for contribution over public internet
- ATSC 3.0 where relevant for hybrid OTA + broadband workflows
Short on ML + broadcast engineers? Plug in a team that can ship analytics that survives real pipelines
Application Areas
How It Works: Manual Routine → Automated Analytics
Before AI analytics: operators watch, manually annotate, and run repetitive checks.
After: AI pre-labels content and quality issues—operators focus on exceptions, approvals, and edge cases.
Productization paths (choose what fits your roadmap):
Rollout approach that stays predictable:
Share your pipeline and target detections. We’ll propose architecture and PoC scope
Case Study: AI-Powered Content Analysis & Behavioral Filtering
Real-time behavior detection for video filtering, censorship, and targeted advertising
Challenge
Required accurate, low-latency detection of specific behaviors (smoking, mobile phone use, mask wearing) in video streams. Existing solutions lacked performance, accuracy, and flexibility for production use.
Solution
Built a custom computer vision pipeline based on YOLOv5/YOLOv8, trained on 12K+ labeled images. Optimized inference to reduce processing latency by 10×, with support for rapid adaptation to new detection classes.
Result
Enabled reliable real-time content filtering, censorship workflows, and automated content categorization. The solution is production-ready and suitable for integration into vendor video platforms.
How We Ensure Quality
Delivery process built for broadcast realities: latency budgets, sync, and interoperability must be verified early.
QA specifics for live and mixed-vendor environments:
Trusted by Global Leaders
As a plug-in engineering partner, Promwad serves SONY, Vestel, and other top 10 brands in the Broadcasting and Media industry across 25+ countries:
Our clients value engineering depth, predictable delivery and cross-industry expertise — especially in complex, real-time environments.
AI Video Analytics for Broadcast Vendors — Automate QC at Scale
Replace manual routine with AI video analytics that detects scenes, objects, and quality issues across your broadcast pipeline—so your team ships faster with consistent, measurable results.
FAQ
Can analytics run on-prem / offline for privacy-sensitive customers?
What latency can you achieve for live pipelines?
How do you integrate analytics into ST 2110 / NDI / SRT workflows?
How do you handle model updates, drift, and new requirements?
Can you start with a PoC and then productize it inside our device/software stack?
Do you provide dataset/labeling strategy and evaluation methodology?