Brand-Consistent AI Video Ad Generator with Automatic Checks and Disclosure Labels
Small brands want short video ads for Instagram, TikTok and YouTube but cannot afford a studio, and AI video tools produce clips that drift off-brand and are not clearly labelled as AI-made. Build a generator that turns a product brief and brand kit into short ads in several formats, then automatically checks every frame for logo, colours, text spelling and banned claims, and adds a visible AI label plus Content Credentials before export.
Advanced
Needs a team, real planning, and a supervisor who knows the area.2 semesters · 2 to 3 students
7 to ship
3 optional extrasSuggested stack
What you should ship
- A brand kit model: logo files, colour palette, fonts, tone of voice, product photos and banned claims
- A storyboard step that turns a brief into scenes, script and on-screen text in English and Urdu, approved by the user before any video is generated
- Generation of clips using an image-to-video or text-to-video model, plus assembly with FFmpeg: product shots, captions, music, end card
- Automatic checks on rendered video: logo presence and placement, colour palette distance, OCR of on-screen text for spelling and banned claims, safe areas for each platform
- A visible AI-generated label and C2PA Content Credentials embedded in the exported file
- Exports in 9:16, 1:1 and 16:9 with a check report per video
- A study with two or three small businesses or student-run brands comparing ads with and without the checks
If you have time left
- Voice-over in Urdu with a licensed synthetic voice and clear disclosure
- A/B variant generation with tracked results
- A human review queue for agencies
The problem: cheap video, off-brand and unlabelled
A clothing brand, a home bakery or a local skincare label needs a steady stream of short videos. AI video tools can now produce clips in minutes, but the results drift: the logo warps, colours change between scenes, on-screen text is misspelled, and a model might add a claim like "100% organic" that the brand cannot make. Platforms and laws increasingly expect AI-generated content to be labelled, and many small brands do not know how.
What already exists and the gap
- AI video generators such as Runway, Google Veo, Kling, Pika and Luma generate clips from text or images.
- Template-based ad tools such as InVideo AI and Canva help assemble ads quickly.
- Platform labelling rules: YouTube asks creators to disclose realistic altered or synthetic content, and Meta and TikTok label AI-generated content, partly using C2PA metadata.
- The EU AI Act's transparency duties require deepfakes to be disclosed and AI-generated content to be marked.
The gap is a pipeline that treats brand rules and disclosure as automated checks, not afterthoughts, and blocks export until they pass.
Inspiration
Large brands use brand guidelines and human review for every asset. This project asks how much of that review can be automated with computer vision and OCR, so a small brand gets the same discipline.
What you build
- A brand kit and a brief-to-storyboard step.
- Generation and assembly.
- Automatic checks on every rendered frame.
- Labels, credentials and exports.
The technically interesting part
- Checking video, not just prompts. Generation is unpredictable, so verify the output: logo detection, colour distance in a perceptual colour space, OCR on frames, and claim matching.
- Consistency across scenes. Using product photos as image-to-video inputs keeps products recognisable. Measure how often clips stay on-brand.
- Provenance. Embed C2PA credentials correctly and verify they survive export to each format.
How you will evaluate it
- Share of generated clips that pass checks first time, and the most common failures.
- Agreement between automatic checks and a human brand reviewer.
- Time and effort to produce a set of ads, compared with the brands' current process.
Path to a product
Agencies and small brands would pay for on-brand, compliant ad generation. A subscription tool, or a service for agencies that manage many small brands, are both realistic, and the checks layer could be sold separately to teams already using AI video tools.
Scope warning
Use only brand assets you have permission to use, never real people's faces or voices without consent, and never imitate a real brand you do not work with. Keep generation costs in check by approving storyboards before rendering. The visible AI label is not optional.
Learn what you need on Sefism
- AI creative roadmap: image and video generation, workflows and disclosure.
- AI creative cheat sheet: models, prompts and labelling practice in one page.
- AI governance roadmap: transparency obligations for AI-generated content.
- OpenCV Python tutorials: frame analysis for the checks.
- Stable Diffusion: a local image model for storyboards and product shots.
- InVideo AI: an example of a template-based ad tool to compare against.
- Can I make a career with AI image and video generation?: what clients actually pay for in this field.
Get help with this idea
The risk is spending the budget on generation and having nothing reliable to show. Tauseef can help you scope the pipeline, choose models and design checks that prove their value. If you are new here, create a free account first.
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