Drip runs the whole user-acquisition loop — diagnose, decide, allocate, create — as a team of agents. The decision core is deterministic and auditable; the LLM only explains. Self-host it, fork it, see exactly why before you spend a dollar.
The closed UA agents say “trust us.” Drip shows the work — every scale, pause, and budget move opens its full reasoning.
Eight signals → red/amber/green → a deterministic rule chain produces the action, confidence, and guardrails. The LLM only writes the human “why”. Replayable, byte-for-byte.
collect → diagnose → strategize → create → allocate → learn. drip run walks the entire cycle, cross-platform, and reallocates budget from losers to winners within your cap.
A ladder, not a switch: shadow (plan only) → copilot (human approves each write) → autonomous (within a hard budget cap). A person signs off before money moves.
Platform AI won the bidding auction. The new battleground is the decision layer — and that's exactly what Drip opens up.
CPP, ROAS, CVR, CTR, frequency, spend, purchases, headroom. Each goes red/amber/green, the rules fire, and you get a decision card with the full vector + rule chain + confidence.
Nobody optimises across walled gardens — that's the open, neutral gap Drip fills. Freed budget from pauses flows to the scalers in proportion to value, inside your daily cap.
10 hand-curated decision cases, a three-part rubric, a pluggable agent interface. Score any model — yours, ours, a competitor's — and every run writes a bundle anyone can re-run and diff.
drip apply pushes scale / pause to Meta · 腾讯 · 巨量 · 快手 (auto-routed); drip watch guards pacing intraday; drip autopilot runs the whole loop, signal-routed behind a circuit breaker. shadow → copilot → autonomous, every write capped + audited.
We're not claiming Drip beats them on raw performance today. We're claiming it's the only one you can audit, fork, and run in your own environment.
Open the live console — no install, no signup.