Most of adtech optimizes delivery and targeting. With SmartAssets we start earlier, at the creative itself. We’ve built the AI data and analytics platform that analyzes, predicts, and enhances ad content before it goes live, so brands launch work that hits harder with less waste and better ROAS.
Under the hood, SmartAssets surfaces the outputs of roughly a dozen machine-learning models across text, image, and audio. Brands can start with simple checks and progress to sophisticated, multimodal policies. Teams build a library of custom rules that all assets need to abide by, and then the AI is able to vet and score new creative, at scale.
These rules might be things like a textual requirement that the brand is clearly referenced; an image rule enforcing logo presence and specific size; and an audio rule confirming that the brand name is spoken in voiceover and end card. If the goal is consistent across all three modalities (text, image, audio) you compose it once and the platform enforces it everywhere.
Because the models see inside the asset, rules can be object-based and context-aware: specific products, people, colors, scenes, or placements.
Consider a sponsor-heavy brand environment like Formula 1. With just a few custom rules tweaks, the marketing team there could ensure the correct season’s car appears, that sponsor marks are current and in-spec, and that nothing outdated slips into an edit. As your creative library changes, your rules change with it.
Two axes matter when comparing solutions: how much you can check, and how fast you can check it.
Today, SmartAssets evaluates around 300 rules and can process a two-minute video in about two minutes. We’ve built a common structure for what a rule is, and an engine to assess it at scale.
Now we’re pushing control to marketers so they can shape their own policies, and we’re paving the way for rules that the system proposes proactively. That’s not sci-fi, it’s just where the tech is at.
What does that look like in practice? It means SmartAssets could internalize your brand identity well enough to surface new, high-value rules and checks you haven’t written or even thought of yet—and it will ensure those checks conform to your own guidelines.
That’s where the platform becomes not just a validator of creative assets, ticking off pre-determined boxes, but an actual generator of brand intelligence.
Agentic systems are the logical next step. We don’t need more engines; we need more rules, drafted and iterated continuously.
Picture an embedded agentic AI researcher that crawls brand docs, scans the latest behavioral science and neuromarketing literature, and translates insights into potential new rules to optimize your brand’s creatives.
The agent explores and recommends, and humans approve with a click after reviewing outputs across a sample of assets. Accepted rules join your live matrix, and rejected ones become lessons that refine the next batch.
But first, an important reminder that agentic does not mean ungoverned! Exploration and recommendation can be autonomous, but implementation stays human-in-the-loop. That balance keeps speed and scale without giving up accountability.
Generative tools have multiplied output. Without enforcement, volume becomes volatility (and a whole lot of AI slop).
Custom rules in a tool like SmartAssets turn brand guidelines into executable standards, so that no creative slips out the door without being properly buttoned up.
Next, a move toward agentic AI workflows make sure the rules you have in place for creative can evolve as markets, formats, and consumer behavior shift.
While your human team focuses on what they do best, a choreographed team of AI agents could suggest changes to creative rules based on shifting trends in the real world (preferences for new color palettes, a sudden allergy to overly prominent logos). That equals a surefire boost to efficiency and quality, at scale.
