AI Video Generation

Consistent presenter video at any volume, train an AI avatar on a real person once using 5-10 minutes of high-quality source footage, then generate every future video from a script alone with no camera, no scheduling, and no reshooting. HeyGen Instant Avatar or Synthesia as the generation layer: both produce photorealistic presenter video where the avatar's lip movements, facial expressions, and head movements synchronise naturally with the provided script audio. Pipeline architecture: a script input interface (direct text entry or API call from your CMS/LMS), optional voice-over generation using ElevenLabs or the avatar platform's native TTS for the specific presenter's cloned voice, video generation API call with the script and voice audio, quality check on the returned video, and delivery to the configured destination. Training content use case: when a procedure changes, the process owner updates the script in a document, the script flows through the pipeline to a new video, and the LMS module is updated, no production crew involved. Multi-language capability: a single avatar can be dubbed into 20+ languages using AI translation and lip-sync adjustment, making localisation a per-language API call rather than a per-language recording session. Cost economics: HeyGen charges approximately $0.25-$0.50 per generated minute on production plans; a 3-minute training module updated monthly costs under $2 per update in generation fees compared to hundreds in production costs for a reshooting. SCORM-compatible output for LMS upload; MP4 delivery for direct embedding.

Short-form video creative for paid ads, social, and campaigns produced at the volume that performance marketing requires, dozens of creative variations for A/B testing without the production cost that makes traditional video A/B testing impractical at scale. Model selection matched to content type: Runway Gen-3 Alpha for cinematic scene generation with consistent visual style; Kling for high-motion product action sequences; Pika for short social formats (6-10 second clips) with text overlays; image-to-video via Runway or Kling for animating static product photography into social-ready short clips. Prompt engineering and style guide encoding: your brand's visual language (colour palette, mood, compositional style, product placement rules) translated into generation prompts that produce consistently on-brand output without per-generation prompt crafting by a creative team. Creative variation pipeline: a single brief generates N variations with different visual approaches, text hooks, and lengths (6s, 15s, 30s) for the same campaign, feeding the creative testing framework your performance team runs. Video editing automation using FFmpeg or AWS Elemental MediaConvert: reformatting existing creative into platform-specific aspect ratios (9:16 for Reels and TikTok, 1:1 for feed, 16:9 for YouTube pre-roll), adding platform-compliant text overlays and logo lockups, and trimming to required durations, converting one master creative into every required placement variant automatically. Digital Asset Management integration for storing generated creative with metadata tags and campaign attribution; publishing API connections for direct upload to Meta, TikTok, and YouTube.

Unique video generated per recipient by injecting CRM-sourced variables into an avatar script before each generation, producing a video where the presenter says the recipient's name, references their company, and speaks to their specific use case or offer rather than sending a generic message. Personalisation variable pipeline: Salesforce, HubSpot, or custom CRM queried for the recipient's name, company name, industry, product interest, and any relevant usage metrics; variables formatted into a personalised script template; the completed script sent to HeyGen or Synthesia's batch generation API which queues the generation jobs and returns completed videos within minutes. Quality gate before delivery: each generated video reviewed automatically for audio-video sync score above threshold and lip movement accuracy; videos below threshold regenerated before entering the delivery queue; a random sample of passing videos flagged for human review at configurable sampling rates (e.g., 5% review for outreach sequences). Delivery infrastructure: each generated video uploaded to S3 or equivalent object storage; a unique personalised landing page generated per recipient with the video embedded and a CTA specific to their pipeline stage; the landing page URL included in the email or LinkedIn message; video view events and CTA clicks tracked back to the recipient record in the CRM. Typical outreach performance compared to text-only: 35-60% higher reply rates reported in published studies for video-personalised cold outreach, with open rates unaffected (the differentiation is at the reply stage).

Automated demo video generation that keeps product documentation and sales content current with your release schedule rather than falling 2-3 versions behind because production can't keep pace with engineering. Demo generation pipeline: screen recording captured automatically via Puppeteer or a lightweight browser automation script that navigates the product's key flows; narration script pulled from the feature spec or release notes; AI voice-over generated with ElevenLabs using your product team's cloned voice or a consistent brand voice; video assembled by synchronising the screen recording with the narration audio and adding zoom-in callouts at key interaction moments using Remotion or FFmpeg. Release pipeline integration via GitHub Actions or your CI system: when a release tag is published, the demo generation workflow triggers automatically, produces updated demo videos for each changed feature, and opens a PR in the documentation repository with the new video files attached, ready for review before the release goes live. Localisation for multiple markets: narration script translated by DeepL or GPT-4o, voice-over regenerated in the target language, lip sync adjusted if an avatar presenter is used, the same screen recording reused across all language variants with only the audio layer changed. Product tour video for in-app onboarding: short 30-60 second guided videos showing new users the product's core value moments, embedded in the onboarding flow and triggered contextually based on what the user has and has not completed.

AI video generation embedded as a feature inside your product, whether a social content creation tool, a presentation platform, a marketing tool, or an e-learning platform where users generate video content as part of their core workflow. Architecture for user-facing generation: a React or React Native UI captures the user's input (script, prompt, uploaded image, or screen recording); a backend API validates the request against the user's plan limits and submits the generation job to the appropriate model API (HeyGen, Runway, Kling, or Pika depending on content type); a job queue tracks generation status and pushes a webhook notification when the video is ready; the completed video is stored in S3 with a pre-signed URL returned to the client for display and download. Rate limiting and quota management per user: generation job counts tracked against plan tiers; users approaching limits notified proactively; overage requests blocked with an upgrade prompt rather than silently failing. Content moderation before delivery: input prompt screening with OpenAI's moderation API for policy violations; output video scanning for inappropriate content using Google Cloud Video Intelligence before the video is made accessible; flagged outputs quarantined for human review rather than delivered to the user. Storage and cost management: generated videos stored with configurable retention periods per plan tier (free tier: 7 days; paid tier: 90 days; enterprise: indefinite); video transcoding via AWS Elemental for consistent playback quality across devices; CDN delivery for low-latency streaming globally.

Production-grade quality infrastructure for AI video pipelines, because AI video generation is non-deterministic and unreviewed outputs reaching customers or LMS learners create brand and compliance risk that outweighs the production efficiency gains. Automated quality checks applied to every generated video before it enters the delivery queue: lip sync accuracy score computed by comparing mouth-movement landmarks against audio frequency peaks using MediaPipe Face Mesh and FFT audio analysis, with a configurable minimum score (typically 0.75) below which the video is automatically regenerated; audio-video alignment drift detected by comparing expected audio onset times against detected speech boundaries; visual artifact detection scanning for common AI generation failures (ghosting, temporal inconsistency between frames, watermarks from model outputs); frame-level content integrity check confirming the generated video matches the expected duration and resolution. Human review queue for outputs that pass automated checks but fall into a review-required category: first generation for a new avatar configuration, videos referencing specific claims that need accuracy review, or any video in a regulated category requiring sign-off before delivery. Review UI: flagged videos displayed with the specific automated score that triggered review, the generation parameters, and one-click approve or regenerate controls, reviewers resolve the queue without navigating the generation system. Audit log for every video: generation timestamp, model and parameters used, automated quality scores, review outcome and reviewer ID, delivery timestamp, the audit trail for content governance in regulated industries.