AI-Powered Video & Audio Analytics for Broadcast Pipelines
Automate scene, object, and quality-related stream signals to reduce manual routine in content operations. With AI-driven analytics, you can enrich live and file-based workflows across mixed-vendor ecosystems.
Promwad builds software for AI-powered video/audio content processing and analytics, including LLM adaptation for content search and prioritization. We train and port custom models to cloud or embedded hardware, and deliver dashboards/multiview tools.
Our Partners and Companies Employing Promwad Solutions
Built for AI-Powered Content Processing
Broadcast ecosystems are increasingly distributed and multi-vendor. Vendors need seamless remote control of streaming/processing tools, integrated AI content analytics, and scalable services that can grow to millions of users.
If this sounds familiar, you’re not alone:
✓ Manual tagging and segment search slow down content workflows.
✓ Content triage takes too long: deciding what matters isn’t automated.
✓ Remote streaming operations are hard to manage across tools and vendors.
✓ AI analytics is hard to productize inside dashboards and multiviewers.
What changes with Promwad analytics:
✓ Integrate AI-powered streaming content analytics into your product (APIs + UI).
✓ Enable media content categorisation, ad filtering/personalisation, and harmful content detection.
✓ Improve discovery with LLM-powered search and prioritisation of relevant segments.
✓ Deliver dashboards and multiview displays powered by analytics signals.
What We Deliver
Promwad delivers software for AI-powered broadcast content processing and analytics—ready to integrate into vendor products, dashboards, and multiviewers.
AI video & audio analytics (content signals)
- Scene segmentation & scene change detection for structure, highlights, and navigation
- Object / person / brand / logo detection with tracking over timecodes
- Configurable event detection for studio, sports, news, live events, and Pro AV
- Audio analytics: signal-level monitoring combined with AI-driven speech and event detection
Content processing outputs (ready to integrate)
- Metadata generation: timecodes, tags, confidence scores for indexing and downstream workflows
- APIs, webhooks, and export formats to embed analytics into your platform
- Exportable reports for compliance evidence and faster customer support resolution
LLM adaptation for search & prioritisation
- LLM-based media search and summarisation across metadata, timecodes, and detected events
- Prioritisation of relevant segments (e.g., incidents, highlights, risky scenes) for faster retrieval and review
Productization: UI + deployment
- Dashboards and multiview displays powered by AI content analytics (widgets, overlays, drill-down to segments)
- Custom AI model training for your classes/events and edge cases
- Porting to cloud, edge servers, or embedded hardware (depending on latency/BOM constraints)
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
AI content analytics is valuable only when it integrates with your streaming/processing tools, remote control, and monitoring stack. We design around the standards you 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, and 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 We Build AI Video Analytics Into Your Product
1. 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.
2. Before: content workflows rely on manual tagging, fragmented tooling, and slow discovery.
After: AI generates structured content signals (scenes/objects/events/audio cues), feeds dashboards/multiviews, and powers search/prioritisation via LLMs.
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
Our Case Studies
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.
AI Shoppable Video for Smart TV & STB
AI video analytics for in-stream product discovery — enabling viewers to search and buy clothing directly from video on Smart TVs and set-top boxes
Challenge
Build and deploy a shoppable video feature on Smart TVs and STBs—one of the early solutions in Europe—so viewers can identify clothing items seen in a video stream and instantly find matching products in online stores.
Solution
Implemented photo/video recognition using the neural network technology of the European startup Oyper, and integrated a “clothes search scanner” flow that returns a product list from online retailers directly on the TV screen.
Result
Developed an end-to-end shoppable video application for TV/STB environments, enabling telecom operators and content providers to differentiate their offering, increase engagement, and generate additional revenue via retailer referral programs.
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 — Build Smarter Content Workflows
Bring your streaming/processing stack, target use cases (categorisation, filtering, segment search), and deployment constraints. We’ll propose an architecture and PoC plan—covering analytics, dashboards/multiview, and LLM-powered search.