Project Info
Inspiration
We were done debating “which ad feels better.” AdSensei turns creative gut-feel into measurable signals so designers know what to tweak and marketers know what to scale.
What it does
AdSensei converts a folder of ad creatives into a clear, prioritized playbook: Instant per-asset readout: tone & sentiment, product/category, objects/logos, OCR’d copy, color palette, composition notes, target audience, and best-fit platforms. Actionable scoring across five core dimensions—Attention, Readability, Aesthetics, Brand Fit, Memorability—each with a one-line rationale so every number is defensible. Smart recommendations: concrete fixes (CTA presence/placement, headline length bands, contrast/whitespace, logo size and anchor, modernized styling, eco-claims surfacing). Campaign roll-ups: Top 10 with reasons, sentiment & tone maps, color trends, and Dimension Profiles (e.g., most readable vs. most memorable). Example: single-image insight (condensed) Scores: Catchiness 7, Aesthetics 6, Readability 8, Brand Fit 9, Memorability 7 Detected: logo “Rinso,” benefit copy (“WHITER THAN BRAND NEW”), detergent box, flags, dresses Audience & platforms: 25–45, household care; strong for social, print, and nostalgia channels Suggestions: add CTA, modernize layout, surface eco-benefit Why: vibrant palette + crystal-clear benefit line; nostalgia boosts brand fit Challenge alignment — high-value signals for recommendations We extract diverse, high-value features that feed a recommender and generalize across campaigns: Tone & sentiment → audience resonance and platform fit Product/category → clustering and cold-start suggestions Text/OCR & copy shape → headline length bands, readability, CTA presence/placement Logos & brand anchors → recognition and consistency across variants Objects & context → scene elements to inform targeting/use cases Color & density → contrast/whitespace patterns tied to attention and legibility Composition notes → focal order, safe-zone usage, rule-of-thirds hints (Video-ready) AV signals → embeddings for distinctiveness/memorability Why these help: they’re predictive proxies for clarity, fit, and distinctiveness—features a recommendation model can weight beyond historical CTR.
How we built it
(high level) We optimized for clarity, consistency, and speed—less black box, more “why”: Rubric first. Five dimensions that creative teams already speak: Attention, Readability, Aesthetics, Brand Fit, Memorability. Signals → explanations. Every score ships with a one-line rationale and a suggested change. Batch to decisions. Per-asset outputs aggregate into campaign-level Top-N, distributions, and Dimension Profiles. Sponsor tech in the loop. creao.ai for a clean, skimmable UI. Lava to route rationale prompts across models for sharper, less generic guidance. Reka for robust vision-language grounding. Inspired by AppLovin, we prioritized pre-spend insights (clarity, platform fit, iteration speed). (The pipeline is model-agnostic and easy to swap as needs grow.) Performance & parallel batch processing Designed for fast (< 5 minutes) campaign reads and horizontal scale: Parallel workers process assets independently (embarrassingly parallel). Stateless jobs pull from a queue, emit compact feature vectors + rationales. Reducer stage builds sentiment/tone maps, color trends, and Top 10 with reasons. Caching reuses OCR/embedding results for near-duplicates. Scale knob: increase worker count to hit tighter SLAs. A simple, transparent scoring form keeps things explainable: [ S_{\text{composite}}=\sum_i w_i,z(d_i),\quad \sum_i w_i=1 ] where (d_i) are normalized signals per dimension and (w_i) are tunable weights.
Challenges we ran into
Subjectivity vs. consistency across wildly different aesthetics No ground truth (early on) → relied on strong proxies before CTR/CVR labels Signal fusion without letting a single metric (e.g., contrast) dominate Edge cases: tiny text, busy backgrounds, logo-as-background patterns Latency/cost while keeping batch UX snappy for 40–50 assets Accomplishments we’re proud of Explainable scorecards teams actually read—every number has a reason Specific recommendations (e.g., “raise CTA ~120px,” “logo +18%”) instead of “make it pop” Dimension Profiles that pick winners for a purpose (readable vs. memorable) A clean campaign roll-up: sentiment/tone maps, color trends, and a demo-ready Top 10 with reasons
What we learned
Clarity beats cleverness—adoption rose when the rubric was transparent Small, honest heuristics travel well: WCAG contrast, copy length bands, whitespace% Rationales drive trust—teams change creatives faster when they see the “why”
What's next
Performance linkage: learn (w_i) from CTR/CVR labels as they arrive Platform presets: TikTok/IG/YT rules (safe zones, duration bands, subtitle norms) Variant helper: headline trims, palette swaps, CTA placement experiments Workflow & exports: reviewer notes, shareable one-pagers, and API hooks into creative pipelines AdSensei: clarity, not guesswork.
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- Node.jsUnchecked
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