Creative corpus
Hooks, scripts, cuts, creator notes, winning angles, failed tests, thumbnails, landing-page screenshots, and the reason each asset worked or died.
Feisty Agent-First Audit
Stop selling dashboards. Build the creative brain clients cannot replace.
We map the data, memory, and feedback loops that turn a UGC ads service into an agent-assisted creative operating system. The first version runs in shadow mode before it touches client work.
Agent loop
1Ingest client context
2Read creative performance
3Recommend next tests
4Capture human decisions
5Update memory
The moat is not a prettier report. It is the memory of what worked, why it worked, and what the operator decided next.
What we audit
Four layers that decide if Feisty becomes defensible.
Performance memory
Meta spend, CTR, thumb-stop, hook retention, CPA, comments, offer fit, fatigue timing, and which claims or visuals moved buyers.
Client operating context
Brand constraints, approval patterns, margin, stock, seasonality, audience objections, legal limits, and what the client actually says yes to.
Agent loop
The weekly system that turns new results into better briefs, sharper scripts, smarter tests, and fewer repeated mistakes.
The deliverable
A practical map, not an AI transformation deck.
The audit ends with one shadow-mode agent to build first. No client-facing automation until the recommender beats the current workflow on quality.
✓
Data moat scorecard: what you already collect, what is missing, and what a generic model cannot know.
✓
Creative memory schema for hooks, angles, claims, offers, assets, outcomes, and client preferences.
✓
Shadow-mode agent plan: the first recommender to run beside your current workflow without touching live campaigns.
✓
90-day transition map from dashboard-first reporting to agent-assisted creative operations.
Shadow-mode candidates
Start with an assistant that helps the operator, not a bot that pretends to replace them.
Hook recommender
- Input
- Product, offer, persona, past winners, comments, objections
- Output
- 10 ranked hook angles with why each deserves a test
Creative fatigue scout
- Input
- Ad-level spend, CTR, frequency, CPA, comments, creative age
- Output
- Which ads need a new hook, new proof point, or a kill decision
Client approval brain
- Input
- Prior feedback, banned claims, preferred tone, legal limits
- Output
- Creative brief that is less likely to die in approval
Landing-page gap finder
- Input
- Ad promise, offer, search intent, page sections, objections
- Output
- Above-fold and section fixes that match the ad angle
Scorecard
If it does not compound, it is just another tool.
This is the filter before building anything. A Feisty agent must make future work better because of what happened in past work.
Data uniqueness
Do we know anything Anthropic, OpenAI, or a competitor scraping public ads would not know?
Feedback speed
How fast does a campaign result become a new creative decision?
Memory depth
Can the system remember why a client rejected an angle three months ago?
Switching cost
Does the agent become more useful the longer the client stays?
Operator leverage
Does it remove low-value dashboard work or just add another reporting screen?
First Feisty agent target: creative memory and hook recommendations from real performance data.
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