Lucidic AI
Lucidic AI is a YC Winter 2025 (W25) company. Based in San Francisco, CA, USA. Team of 4.
About Lucidic AI
Summary
Lucidic AI builds a continuous testing and optimization platform for enterprise AI agents. It ingests a company's production logs, edge cases, and operational policies, then runs controlled simulations and automated optimization to find failure modes and apply fixes before they reach production. The result replaces manual prompt engineering and ad-hoc debugging with a closed-loop system that keeps agents aligned to a company's specific domain logic and rules.
Go-to-market
Targets engineering and AI ops teams at B2B companies deploying production AI agents, reached through direct sales outreach and demo bookings via the company's website.
From the Y Combinator listing
AI Agent Training via Simulations
As AI agents take on more consequential workflows, the hard part isn’t just whether they work—it’s whether they behave consistently with your company’s knowledge, policies, and expectations. Lucidic AI turns that institutional knowledge into consistent agent behavior by continuously testing, stress-simulating, and auto-optimizing agents against your real production scenarios. Lucidic ingests your real logs, edge cases, and operational rules, then uses controlled simulations, reinforcement learning, and Bayesian optimization to automatically discover failure modes, propose targeted fixes, and verify improvements before anything reaches production. Instead of relying on manual prompt fiddling or guesswork, your agents get a continuous improvement loop: they’re tested, corrected, and optimized based on what your business actually requires—not what a generic model assumes. The result is AI agents that reliably follow your domain logic, adapt to changes, and stay aligned across clients, configurations, and environments—without you needing to hand-engineer every prompt or behavior.
Facts
- B2B / B2B
- San Francisco, CA, USA
- 4
- Active
- Early
Founders (3)
- Abhinav Sinha
- Andy Liang
- Jeremy Tian
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