Open-source alternative to Arcads: AI UGC ads

An AI creator talks to camera about your product, holds it, uses it, and sounds like a person filming themselves. Generation runs on varg: the MCP (call_tool) or POST https://api.varg.ai/v2/tools/{image|video|speech}/call. Quote credits only, from estimate_cost, and wait for a yes before spending.

What the data says: in one operator's $100K test (220 videos), human UGC had the higher CTR and AI the higher ROAS, because AI made more variants cheaply. AI works for cold traffic, low-trust products and finding the winning angle; it is weak for high-ticket offers and warm audiences. The script matters more than whether the face is real. Use AI to find the angle, then consider remaking the winner with a real creator.

0. Gates

1. Where does it run? Meta ads and TikTok ads: OK, labelled. TikTok Shop video: stop and use tiktok-shop-video (AI voices as presenter and AI-made material are against Shop rules).
2. Claims: the AI creator may demo and state facts. Never write "I used it for two weeks and…", before/after results, or "my skin cleared". The FTC bans fake testimonials, AI ones included (up to $53,088 per violation); New York requires a synthetic-performer disclosure in ads. Real reviews may be quoted as reviews ("2,100 reviews say…").
3. Product photo: clean, straight-on, label readable. "If the label isn't readable in the reference, the model invents its own text. That's where most people's ads die."
4. Cost: quote the full batch with estimate_cost (assume ~3 takes per usable Seedance clip, ~1–2 for Kling 4).

1. Cast the creator

• Age matches the claim, written as a number ("54-year-old woman"); room matches the problem (energy = kitchen at 6 am, sleep = bedroom lights off, bloating = bathroom mirror, joints = stairs, private = car).
• Real skin: "freckles, visible pores, a small blemish near the jaw, flyaway hairs, no retouching, no skin smoothing".
• One outfit with one specific detail ("collar slightly crooked, sleeves pushed up").
• Generate the creator still with gpt_image_2_5 or nano_banana_2_1: "iPhone selfie photo, slightly below eye level, phone at arm's length…". Pick one, then make an identity sheet (front, ¾, side, expressions) and reuse the same image on every shot. Deeper casting: varg-character-design.

2. Pick the format

3. Write the script into the prompt

Every spoken line goes in the video prompt, with timings. 60–75 words for 30 s (about 2.5 a second), never over 85; size and check them with the video-script skill. Open on the most embarrassing version of the problem, or mid-motion with a throwaway line ("Okay. Last thing, then bed."). Add written-in stumbles. End on one CTA.

4. Generate shot by shot

For each beat (≤5 s, or one 15 s Seedance take):

Seedance 2.5 / Kling 4 inputs:
  image 1 = the creator still (identity)
  image 2 = the product photo (label readable)
  prompt  = TALENT / PRODUCT (label text spelled out in order) / LOCATION / LIGHTING (one condition)
            / FRAMING / beats with dialogue / PERFORMANCE / CAMERA CHARACTER / POSITIVE LOCKS

• seedance_2_5: the most real; reported ~3 attempts per keeper. kling_v4: cheaper, often right first time. Draft on kling_v4_flash.
• creatify_boreal: a product presenter with synced speech in one call, when speed matters more than control.
• Voice: native audio from the video model, or eleven_v4 + sync_v3 lip-sync. Give the model the voice file and the exact transcript, or it stretches the speech to fit the motion.
• Locks: same face, wardrobe, product, room and light on every beat; "exactly two arms and two hands, one hand always holding the phone"; no model-hopping between shots of one creator.
• Realism: handheld tremor, autofocus hunts, rolling shutter, sensor noise, mild wide-angle distortion at arm's length; a breath or a swallow between lines; phone-mic audio; no music, no captions in generation.

Full prompts (realistic skincare UGC, camcorder vlog, street interview chain, Creatify JSON): references/prompts.md.

5. Reject pass

Before anything reaches the edit, compare each clip with the references: face (jaw, freckles, hair), label text letter by letter, product shape and colour, hand count, lip-sync drift. Regenerate failures; don't fix them with captions.

6. Edit and label

• Assemble with ecommerce-video-edit: hook first, cut dead air (human jump cuts), captions in the safe zone, one CTA, -14 LUFS, one MP4 per hook.
• Label: TikTok's AIGC toggle (it resets on duplicated campaigns) or an on-screen "AI-generated"; Meta adds "AI info" itself; keep generation logs.
• Variants: a different buyer, angle or format, not 10 avatars reading one script (ad-creative-testing).

Related

ecommerce-video-ads · product-video-hooks · varg-character-design · varg-consistent-characters · varg-podcast-video · ecommerce-video-edit

Sources: r/FacebookAds $100K test, FTC rule, TikTok AI policy, @abxxai realistic UGC thread, @CEO_Vlad, @YourAlphaMom Seedance vs Kling test, @mikefutia street interview.