AI infrastructure for the things that actually break businesses.

The same idea, applied three ways.

Spam, fraudulent signups and malicious traffic are the same problem wearing different clothes: scoring whether something is what it claims to be.

• A custom-trained model does the heavy lifting. Fast gradient-boosted scoring on every request — no waiting on a third party.
• Claude AI handles only the uncertain middle. When the model isn't confident, the case escalates to a frontier LLM — cached, time-boxed, fail-soft.
• Every verdict is explainable. No black box. You see the score, the signals and the reason — and can override it.
• It learns from you. Each correction feeds the next training round on your own traffic.

# one philosophy, all three products
score = model.predict(features)   # <5ms

if 0.50 < score < 0.75:      # uncertain band
    s2 = claude.assess(payload)
    score = blend(score, s2)

verdict = (
  "block"  if score>=reject else
  "review" if score>=hold   else
  "allow")
explain(verdict, reasons)       # never a black box