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Meta InnovationShowcase 2026
Turning production pressure into creative opportunity.
How the session is structured.
H1 moved AI from theory into live production.
What those pilots taught us.
The impact we have seen.
What’s been implemented as a result.
Innovation only mattersif it solves a real problem.
The opportunity is not simply to “use more AI”. It is to identify where new tools, workflows and production models can make Meta’s global content operation materially better.
The H2 challenge
Five H2 objectives
Not tools. Not initiatives. Objectives, because each one solves a business problem.
Build once.
Scale intelligently.
Production logic gets rebuilt from scratch every campaign. Build it once, and every market after that is a rules change.
The build carries over, so a new market starts from work already done rather than from scratch. Rollout moves closer to a configuration than a new production.
Intelligent campaign automation - plan, create, validate and activate. Publishes straight to Meta, YouTube, DV360, TikTok, Amazon and DOOH. Used by Unilever, Ubisoft, Paramount, KLM and Philips.Walkthrough to come
ComfyUIA fully reusable GenAI workflow that swaps faces, clothing and background while holding the composition of the image.In build
3 placements × 3 markets
These are the real Citrine masters and their India and Mexico localisations, reframed for each social placement. Nine finished assets from one build.
Keep the human.
Reduce the shoot footprint.
Shoot real talent once, in a controlled studio. Then build every scene, market and format from that single capture.
Real-talent credibility in every market, without paying for a location shoot in every market.
Shoot once. Build the rest around it.
The capture is real and the creative direction is human. What changes per market is everything around the performance, not the performance itself.
The background stays a variable rather than a decision made on shoot day. The shoot happens once, and every market after it is an assembly we build and run.
Generate movement,
not poses.
We stop prompting for a pose and direct a performance instead - then take the frame we want out of the motion.
Social creative that reads photographed rather than generated - from creative you have already approved.
Movement, not a pose
One performance.
Many markets.
BeebleAIKeeps the original performance and adapts the character for the market - same performance, different person. Runs entirely on local hardware; source files are never uploaded to the cloud.Tested
Sync LabsThe voice side: dubbing and lip-sync that carries one performance into every language.Demo video to come
Automatic dubbing, already embedded in our process. The baseline we are building past.In our processOne casting and one performance serve every market. Localisation stops being a reshoot.
Hero human-led · creator and social AI-supported · high-volume automation-first.
BeebleAI
Freedman internal test · not client work
The performance stays. The person can change.
One captured performance can carry into a market that needs a different face, without recasting, rebooking or reshooting. The timing, the delivery and the intent are all preserved - only the talent changes.
A market that needs a different face or a different language no longer needs a different shoot. Casting and voice stop being production costs and become settings.
Scale quality control
alongside production.
Claude pluginOur own internal test automating transcreation, brand guardianship and quality control in one pass. Built in-house; not yet run on a live campaign.Internal test
Clearance, before legal ever sees it. Adclear reads the brand guidelines and the market rules, then checks every asset against them - advertising restrictions differ market by market, alcohol being the obvious one. It pre-approves at volume and flags only what needs a human decision.In pilot with Meta
Eye-tracking heatmaps in seconds, plus one score covering attention, cognitive demand, memorability and focus - and the specific fix. Built on eye-tracking and brain-scanning data from 300,000 participants over 20 years. Already used by Mars, Chanel, L’Oréal, TikTok and Meta.To licenceVolume can grow without the QA team growing with it - and without brand risk growing either.
Product accuracy - pass
Headline 43 / 40 characters
Logo clear space - pass
Transcreation - term not approved
Format 4:5 - compliant
Machines take the repetitive pass. People take the judgement.
Every asset is checked against the brand and language rules before a human opens it. The Brand Guardian then reviews only what was flagged, rather than everything that was made.
on this asset
Neurons AI predicts where the eye lands before anything launches, scores the creative, and names what is costing you attention - weak hierarchy, poor logo contrast, reduced readability. It is A/B testing that happens before the spend, rather than after it.
The compliance pass is our own internal Claude plugin test, not yet run on a live campaign. The attention map shown here is an illustration of what Neurons AI returns, applied to our own master so you can read it against work you know. We would like to pilot both properly with you.
From innovation day to innovation pipeline.
Which real programme problems create the most friction?
Which are worth solving through technology or workflow?
Run small, clearly bounded tests.
Cost. Time. Quality. Approval. Scale. Creative outcome.
If it works, integrate it into live programme delivery.
Pass the learning back to the wider Meta team.
What we commit to.
Only ideas we believe solve a meaningful production or creative challenge.
Problem, hypothesis and success measures defined before we begin.
If something does not work, we will tell you. The learning still has value.
Best technology for the problem, not everything anchored to one platform.
Creative, brand and cultural expertise remain central.
Successful pilots become improved workflows, not isolated demos.
Programme managers should not have to spot every opportunity themselves.
Pilot 1: Rose still production
Creating Figma Weave stills from motion assets
Taking existing motion assets from project Rose and exporting stills, then generating variants with local talent. Fewer assets need to be shot for just as many creative options that are locally relevant.
Frames pulled from the Rose film
What we are building
Creative direction on stills extraction, then local variants generated using Figma Weave AI.
What this unlocks
- Fewer asset types need shooting
- Faster brief to delivery
- Truly local creative
- Consistent brand standards across markets
- Scalable
What we need from Meta
- Number of still creatives required
- Possible or confirmed markets
- A named owner on the Meta side to review and approve at each stage
- Alignment on the quality bar: the same standard as the master shoot
Pilot 2: Kessel core assets built for every market
Figma Weave + Auto Layout
Combining Figma Weave’s AI generation with Auto Layout templating to produce market-appropriate core assets, faster and at a fraction of traditional production cost.
Frames pulled from the Kessel film
What we are building
Localisation moves further upstream, creating locally relevant generative assets. Auto Layout templates then produce on-brand, localised variants for each market automatically, and they compound in value.
What this unlocks
- Faster brief to delivery
- Truly local creative
- Consistent brand standards across markets
- Scalable
What we need from Meta
- Involved from the beginning of the US master core asset production
- Possible or confirmed markets
- A named owner on the Meta side to review and approve at each stage
- Alignment on the quality bar: the same standard as the master shoot
×Better technology is not the ambition.Better global creative production is.
Our role is to help you test what is possible, and turn what works into everyday advantage.
Why us?
Because innovation at global scale needs more than a tool.
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Questions?
Which brief, and which pressure, do we start with?
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