LimitifySmartifyTrade — the first production deployment.
SmartifyTrade is a live trading-education platform — same ownership as Limitify AI today, architected as a separate client from day one. It is the proof that the Limitify AI framework holds up under real users, not just in design documents.
Visit smartifytrade.com →SmartifyTrade's product logic — pricing, curriculum, trading rules — lives entirely inside SmartifyTrade. Limitify AI owns none of it. What Limitify AI owns is the agents that serve SmartifyTrade: their permissions, their learning data, their version history. That boundary is what makes the same agent architecture deployable for a second client without a rewrite — client configuration changes, the governance layer does not.
11 of Limitify AI's 14 agents run here today — the remaining 3 are in active development.
FPA
Master control agent — coordinates every other agent, gated by human approval on every state change.
Kuku
Public chatbot on the live site — multilingual support with structured lead capture and escalation.
FOPS
External operations agent — isolated from production systems by design.
Aakruti
Support-ticket and technical-report classifier, drafting responses for human review.
EAS
Educator application screening against platform policy.
Educator Co-Pilot — Readiness Scan
Public lead-generation tool analyzing a creator's content library.
Educator Co-Pilot — Deep Channel Scan
Authenticated tier: full-catalog analysis, suggested learning paths.
Trial Conversion Scanner
Scores free-trial students on likelihood and timing to convert.
GP Outreach Assistant
Drafts personalized outreach messages for the human sales network.
Paid Retention Scanner
Scores active subscribers for churn risk ahead of renewal.
Matter AI
Multi-matter portal engine — powers the public AP recruitment portal, with private OTP-gated matters queued behind the same engine.
Governance survives contact with real users.
Real escalation paths
Kuku's ticket-routing and complaint classification handle actual student, educator, and GP queries in three languages — not a demo dataset.
Human-in-the-loop, enforced
Every category-C/D output on SmartifyTrade — scans, drafts, scores — routes through a human reviewer before it reaches a user. No exceptions logged.
Continuous improvement, on record
Version history and learning logs accumulate from each agent's first live run — the exact track-record model this site's architecture page describes.
Want the technical detail behind this? Read the architecture page or get in touch for a deeper walkthrough.