We're building a single AI-native system that replaces the entire agency stack — creative production, media buying, attribution, and optimization — in one closed loop. Every piece talks to every other piece. Every dollar spent teaches the system something. Here's how it works.
A DTC brand connects their Shopify store and uploads their brand assets. The system runs a Claude-powered interview, ingests their brand guide, and builds a "Brand Brain" — a structured understanding of their voice, audience, positioning, and products.
Why this matters: Every piece of creative the system generates downstream is informed by this brain. It's not generic AI output — it's brand-aware from day one. The brain gets smarter over time as performance data flows back in.
Connect → Upload → Interview. Background brain builder runs automatically after the interview completes.
Claude ingests brand guide + interview answers and generates structured brand context: voice, audiences, hooks, angles, product positioning.
Each brand gets isolated data, their own brain, their own creative library. Built for hundreds of brands from day one.
Using the Brand Brain, the creative engine generates ad copy, images, video scripts, UGC-style content, advertorials, and landing pages. Not templates — original, brand-aware creative informed by what's actually working in the market.
Multi-format ad copy (primary text, headlines, descriptions) with hook variations, angle testing, and platform-specific formatting for Meta, Google, TikTok.
Full landing pages with AI-generated long-form content, product integration, social proof blocks, and conversion-optimized layouts.
Hook → problem → agitate → solution → CTA scripts. ElevenLabs voiceover generation. MoviePy video assembly.
Brief creation, type selection, cost estimation, and generation dispatch. Clients see cost before generating. Credits deducted per asset.
Flux/Replicate and Kie.ai provider abstraction. Code written, provider routing built. Needs API keys to go live.
Kling AI + HeyGen + Creatomate pipeline. Hybrid AI+static approach. 105 source videos catalogued for remix.
The difference: Most AI creative tools generate generic content from a prompt. Our system generates from a Brand Brain — it knows the brand's voice, their best-performing angles, their audience's pain points. And it gets better because performance data feeds back into the next generation cycle.
The media engine handles campaign structure, budget allocation, audience targeting, and bid optimization across Meta, Google, TikTok, Snap, Taboola, and Outbrain. Smart budget allocation with safety rails built in.
Tier-based allocation across channels. Slider-driven budget distribution with recommended splits based on brand stage and vertical.
95+ Python tools including allocation logic, decision frameworks, budget safety rails. Full playbooks for 6 ad platforms encoded into executable rules.
AI-generated campaign suggestions based on brand context, budget, and goals. Batch approve/reject flow in the client portal.
Direct API deployment to Meta, Google, TikTok. Campaign structure, ad sets, targeting, and bid strategy. Code written — needs OAuth credentials to activate.
Most brands fly blind. They know how much they spent and how much revenue came in, but they can't connect the two at the creative level. Our attribution engine closes that gap — tracking every touchpoint from ad click through landing page through store through checkout.
Click → lander → store → purchase. Every event captured with creative-level attribution. Three models: last-click, first-click, linear.
JavaScript snippet for Shopify. Tracks page views, product views, add-to-cart, checkout initiation, and purchase events. Drop-in installation.
Meta Conversions API, Google Enhanced Conversions, TikTok Events API. Server-side forwarding for accurate attribution even with ad blockers.
Cross-device tracking via email hash. Links anonymous sessions to known customers. Post-purchase survey widget for self-reported attribution.
Real-time order + refund processing. Revenue data flows in automatically. Ad spend sync (Meta, Google, TikTok) runs daily for blended ROAS.
Spend sync, attribution batch processing, and identity resolution run on automated schedules. No manual data pulls.
Why we built our own: Existing attribution tools (Triple Whale, Northbeam) are standalone dashboards. They can tell you what happened, but they can't act on it. Ours is wired directly into the creative and media engines — when a creative wins, the system knows instantly and can generate more like it.
Every other tool in this space is a point solution. We built the full circuit. Attribution data feeds back into the creative engine. Winning patterns get identified. New creatives get generated based on what's actually working. The system compounds.
Every cycle makes the next one smarter
Every creative gets tagged with structured metadata: hook type, angle, format, audience, emotion. When a creative wins, the system knows exactly which elements drove it.
Creative variety pools weighted by performance. Winners get more distribution. Losing patterns get deprioritized. The creative mix evolves automatically.
Per-call API cost tracking by service. Clients see exactly what each creative costs to produce. Full transparency, zero markup on generation costs.
The compounding effect: Brand #1 teaches the system what hooks work in health/wellness. Brand #50 gets those patterns on day one. By brand #500, the system has cross-vertical creative intelligence that no agency, no tool, and no human team can match. The data moat grows with every dollar spent on the platform.
Before writing a single line of platform code, we built the agency first. 56 operational documents encoding methodology, playbooks, copy frameworks, and deployment processes. Then we transcribed and synthesized 114 courses from the best performance marketers in the world. This knowledge powers every decision the AI makes.
This is the IP moat. You can't prompt-engineer your way to this. These aren't blog posts — they're operational documents that encode how to run campaigns, write copy, structure funnels, and deploy ads across every major platform. The AI doesn't guess. It executes from a knowledge base built by studying the best in the industry.
Not a prototype. Not a Figma file. A live Next.js + FastAPI + Supabase application deployed on Railway with 30+ API endpoints, auth, role-based access, and a client portal that brands actually use.
We're building this inside a real agency with real clients because that's how you validate. But the endgame isn't services — it's a self-serve platform where any DTC brand can plug in and get the same system running for them.
56 agency docs. 114 course transcripts synthesized. Full methodology, playbooks, copy engine, and deployment processes encoded. The knowledge layer is built.
30+ API endpoints. Client portal. Creative studio. Brand brain. Attribution engine. Cost tracking. Auth. Deployed on Railway + Netlify + Supabase.
Run the full system with 3–5 DTC brands. Real ad spend. Real creative generation. Real attribution. Prove the loop works and generates measurable ROI.
Package the system into self-serve workflows. Brand onboarding, creative generation, campaign deployment, reporting — all accessible without us in the loop.
Every brand on the platform feeds the intelligence layer. Cross-brand pattern recognition creates compounding data advantage. The more brands use it, the smarter it gets for everyone. This is the moat.
The 1% model: We charge 1% of the brand's revenue (with a $99/mo minimum). A $50k/mo brand pays $500. A $500k/mo brand pays $5,000. Price is never the objection — the entire DTC mid-market becomes addressable. And because our revenue scales with theirs, incentives are perfectly aligned. We only win when they win.