Fifteen workflows where AI actually pays for itself

The process

Automated extraction of still frames from live or recorded video streams, selected according to technical quality criteria (sharpness, exposure, composition) and criteria related to on-screen talent (facial expression, emotional state, eye contact).

The traditional problem

An editor must scrub through hours of footage frame by frame to identify moments where the subject is correctly framed, in sharp focus, well exposed, and displaying the desired expression — a task that is both subjectively demanding and technically tedious.

What the AI does

• Technical quality filters — sharpness, exposure, composition and compression-artefact detection automatically exclude unsuitable frames
• Facial detection & expression classification — AI classifies detected expressions (joy, concentration, determination) and prioritises frames where talent makes eye contact with the camera
• Pose & body language assessment — full body pose estimation models evaluate body language for peak action moments

The process

AI autonomously assembles a fully edited highlight reel, selecting the best moments, ordering them narratively, applying transitions, adding music, and rendering a publication-ready package for social, OTT or broadcast use.

The traditional problem

Producing a highlight reel requires an editor to review all footage, select the best moments, assemble a sequence, add music, mix audio and render — a process taking hours even for a skilled editor, by which time the event's social media momentum may have passed.

What the AI does

• Event ranking — scores all detected highlight moments by significance (goal vs. near-miss, crowd reaction intensity, replay speed)
• Narrative assembly — sequences selected clips in a coherent editorial order: chronological, reverse-chronological or drama-first, based on configurable templates
• Automated music & audio mixing — selects and synchronises a licensed track from the MAM library, ducking it under key audio moments
• Format rendering — renders simultaneously in all required formats (16:9 broadcast, 9:16 social, 1:1 square) with lower thirds and branding applied automatically

The process

AI analyses the visual and audio content of every ingested asset and automatically assigns genre, sub-genre, mood, tone and content-rating tags — fully aligned with broadcast classification standards.

The traditional problem

Manual content classification relies on human cataloguers watching content and assigning tags from a controlled vocabulary: slow, subjective, inconsistent across cataloguers, and impossible to scale across large archives or live ingest pipelines.

What the AI does

• Visual scene classification — computer vision models identify scene types (outdoor sports, studio interview, outdoor news scene, religious ceremony) and assign genre and sub-genre tags
• Audio & speech classification — NLP models assess spoken content to confirm or refine genre classification and detect domain-specific language
• Content rating — AI evaluates violence levels, adult content and age suitability, automatically assigning broadcast content ratings aligned with national regulatory frameworks

The process

Deep indexing of hours of live content — talk shows, breaking news, religious sermons — to make it immediately searchable within the archive.

The traditional problem

To catalogue a three-hour asset, an operator must watch and listen to it in order to manually enter tags, chapters and keywords. As a result, vast amounts of material remain uncatalogued and effectively “lost” on servers.

What the AI does

• Speech-to-text — generates a timestamped transcript linked to the timecode, applied by Synaxis directly on the proxy generated in real time by Ingrid
• NLP algorithms — identify the macro-themes covered; in a House of Worship context the system recognises specific biblical references and indexes them automatically

The process

In remote production environments, AI autonomously monitors ISO feeds and pre-selects the most relevant clips in real time, presenting the remote editor with a pre-curated timeline rather than raw unedited feeds.

The traditional problem

In REMI workflows the remote editor must monitor and evaluate all ISO feeds simultaneously in real time — a cognitively demanding task that limits the number of feeds any single editor can manage effectively, and increases the risk of missing key moments.

What the AI does

• Real-time feed monitoring — AI continuously evaluates all ISO feeds using the event detection, expression analysis and quality scoring engines
• Pre-curated timeline — detected highlight moments are automatically assembled into a suggested timeline, presented alongside the full ISO feeds
• Bandwidth optimisation — only the pre-selected segments are flagged for priority transmission over constrained links, reducing contribution circuit costs

The process

For every asset published to an OTT or VOD platform, AI automatically generates optimised thumbnails, writes SEO-optimised titles and descriptions, and populates all required platform metadata fields — eliminating a significant manual bottleneck in the publishing workflow.

The traditional problem

Publishing to OTT and VOD platforms requires manually selecting thumbnails, writing platform-optimised titles and descriptions, and populating multiple metadata fields for each platform's specific requirements — repetitive, time-consuming, and scaling poorly across large catalogues.

What the AI does

• Thumbnail selection & generation — AI selects the most visually compelling, platform-optimised thumbnail, applying aspect ratio and resolution requirements automatically
• Title & description generation — a large language model generates platform-optimised titles and descriptions from existing MAM metadata and the transcript, tailored to SEO best practice and per-platform character limits
• Platform metadata mapping — all required fields for each target platform are populated automatically from the Synaxis record

The process

Based on a syndication partner's defined preferences — sport, language, duration, format — AI automatically assembles and delivers a customised content package from the MAM, without manual intervention from the rights holder's distribution team.

The traditional problem

Preparing customised packages requires a team member to search the MAM, select assets, verify rights clearance for the specific partner and territory, assemble the package and deliver it — a manual workflow that limits the number of active syndication relationships a team can manage.

What the AI does

• Partner profile matching — each partner has a defined preference profile (content types, languages, durations, formats, territories) stored in the MAM
• Automated selection & rights verification — AI continuously matches newly ingested assets against partner profiles and verifies rights clearance for the specific partner, territory and platform
• Package assembly & delivery — cleared assets are transcoded to the partner's required format and delivered over the configured mechanism (SFTP, cloud storage, API)

The process

Conversion of native 16:9 video streams (horizontal broadcast) into the vertical 9:16 or square 1:1 format required by mobile platforms.

The traditional problem

Cropping a live video into vertical format requires manual pan-and-scan work to prevent the subject — the athlete running, the preacher on stage — from moving outside the tight frame.

What the AI does

• Object & action tracking — a module integrated into the Synaxis export workflow analyses scene motion on the proxy file
• Dynamic region of interest — the AI calculates the region of interest and dynamically shifts the 9:16 frame to keep the subject consistently centred

The process

Automated post-emission segmentation of a recorded broadcast output stream into its individual programme, segment and interstitial components through audio and video fingerprinting — without relying on EPG data or manual logging.

The traditional problem

When a broadcaster records its own output stream, the resulting file is a continuous, undifferentiated stream of hours of content. Manual segmentation requires an operator to watch the entire recording and mark in and out points — a workload that scales linearly for multi-channel broadcasters.

What the AI does

• Audio & video fingerprinting — each frame is compared against a reference fingerprint database with frame-accurate precision
• Boundary detection — AI detects segment boundaries based on fingerprint matching, scene-change detection and audio signature analysis
• EPG & as-run reconciliation — output is cross-referenced with the scheduled EPG, flagging discrepancies between what was scheduled and what actually aired
• Music & rights reporting — audio fingerprinting identifies music tracks, automatically generating cue sheets for reporting to collecting societies

The process

AI embeds invisible forensic watermarks into every output stream at ingest, enabling downstream detection of unauthorised redistribution. A complementary monitoring module scans public platforms to detect and report pirated content derived from the broadcaster's assets.

The traditional problem

Unauthorised redistribution causes direct revenue loss and rights value erosion. Identifying the source of a leak in a multi-platform, multi-partner distribution chain is extremely difficult without forensic traceability built into the content itself.

What the AI does

• Forensic watermarking at output — a unique, invisible watermark is embedded into every output stream, encoding recipient identity, timestamp and distribution channel, surviving transcoding, compression and re-editing
• Active piracy monitoring — an agent continuously scans public video platforms and peer-to-peer networks for content matching the fingerprint database
• Source identification — when pirated content is detected, the embedded watermark identifies the exact distribution point of origin, enabling targeted legal action

The process

AI verifies that advertising content broadcast matches the contracted creative, duration and placement — detecting substitutions, truncations or missed spots and generating compliance reports automatically for advertisers and traffic departments.

The traditional problem

Verifying advertising compliance across multiple channels and dayparts requires reviewing hours of as-run recordings to confirm each contracted spot aired at the correct time, in the correct position, with the correct creative and duration — largely manual, prone to error, and unable to scale.

What the AI does

• Ad creative identification — each broadcast spot is fingerprint-matched against the contracted creative registered in the MAM
• Duration & position verification — AI verifies spot duration, break position and daypart compliance against the traffic schedule
• Automated discrepancy reporting — any substitution, truncation, missed spot or incorrect placement generates a discrepancy report with timecode evidence, flagged to both the traffic department and the advertiser

The process

Monitoring of incoming streams at the MCR or OB van to ensure signal integrity and regulatory compliance — presence of advertisements, correct logos, technical conformance.

The traditional problem

The operator must visually monitor dozens of screens simultaneously. Microscopic errors — an isolated black frame, an audio sync offset of a few milliseconds, a missing logo — can easily go unnoticed.

What the AI does

• Technical anomaly detection — constant, non-invasive monitoring directly on the Ingrid software pipeline detects black frames, freeze and audio drop
• Commercial verification — brand and logo detection verifies the actual airing of contractually agreed sponsor placements

The process

Real-time monitoring and automated censoring of live video and audio streams to detect and suppress inappropriate content — profanity, offensive gestures, explicit imagery — before broadcast output or digital distribution.

The traditional problem

Live broadcasts carry an inherent risk of uncontrolled content reaching the audience. Traditional profanity delay systems rely on a human operator manually triggering a bleep or video cut within a narrow window, typically five to seven seconds — making errors and missed triggers virtually inevitable during high-intensity live events.

What the AI does

• Audio analysis — real-time speech-to-text flags profanity, automatically triggering an audio bleep or mute before the signal reaches the output
• Computer vision — object and gesture recognition models scan each frame for offensive gestures or explicit imagery, automatically applying blur, pixelation or cut to black
• Contextual awareness — NLP models assess the broader context to reduce false positives
• Logging & evidence — every censoring event is logged in the MAM with its exact timecode and the action taken, creating a full compliance audit trail

The process

Real-time or near-real-time automated translation and voice synthesis of live or recorded audio streams, enabling multilingual broadcast output from a single source feed without human interpreters or dubbing studios.

The traditional problem

Producing dubbed versions traditionally requires human transcription, professional translation, voice talent booking, studio recording, audio synchronisation and final mix — a process that can take days or weeks per language version.

What the AI does

• Speech-to-text — the original audio track is transcribed in real time with a timestamped transcript locked to the video timecode
• Neural machine translation — the transcript is translated into one or more target languages, preserving context, tone and domain-specific terminology
• Text-to-speech and voice cloning — a neural voice synthesis engine generates the dubbed audio track, optionally replicating the original speaker's voice characteristics
• Lip sync & audio alignment — AI aligns the synthesised audio to the speaker's lip movements and original speech rhythm
• Multi-track output — dubbed audio tracks are embedded as discrete audio channels within the output stream

The process

Real-time automated generation of subtitles and closed captions for deaf and hard-of-hearing viewers, and of audio description tracks for blind and visually impaired viewers, directly within the Ingrid pipeline.

The traditional problem

Subtitling live content requires trained human stenographers operating in real time, with significant latency and a non-negligible error rate. Audio description requires skilled writers, voice talent, studio recording and precise mixing — multiplying post-production timelines and budgets.

What the AI does

• Subtitling and closed captioning — a speech-to-text engine transcribes programme audio in real time, NLP models attribute captions to multiple speakers, and captions are output in all standard formats including EBU-TT and SRT
• Audio description — computer vision models analyse the video frame continuously, a large language model generates natural-language description scripts timed to fit within natural pauses, and a text-to-speech engine renders them as a natural-sounding track compliant with EBU R128