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Reimagining how Machines Learn
Lucidic emulates model training without changing model weights. The last decade made models intelligent, but intelligence is not the same as experience. Humans do not get better by memorizing thousands of examples; we build learning systems around ourselves: skills, memories, critique, practice, tools, and guidance. And every person learns differently because every task is different. Lucidic brings that idea to AI agents by training a custom learning system for each agent, so it learns what to remember, what skills to build, when to ask for help, and how to improve from experience. The model provides the intelligence, Lucidic gives it a way to learn.
Lucidic AI provides a training platform to parameterize, simulate, and optimize AI agents to improve reliability and safety in production. It focuses on measurable improvements without changing model weights, using controlled simulations and optimization algorithms.
The platform lets users expose and formalize agent components (LLM, memory, prompts, context, tools, guardrails, etc.), run targeted simulations that vary design choices, and use algorithms inspired by genetic optimization and reinforcement learning to find configurations that improve metrics. Features include integration with existing stacks (LangChain, LangGraph, Langfuse, OpenAI, Anthropic, Gemini, etc.), intelligent candidate exploration across prompts and tool orders, continuous production deployment with safe rollouts, and metrics-driven optimization toward outcomes like accuracy and CSAT.
Who it’s for: Enterprises deploying AI agents who need measurable, safe, and reliable performance improvements in production.
Customers: Cresta
Hiring and growth signals implied by a careers page and case studies; enterprise customer references and ongoing research/articles referenced
Hi, I’m Abhinav the founder and CEO for Lucidic AI! I’m from Stanford where I studied Computer Science with an AI specialization (both bachelor's and master's) and did research at Stanford's AI Lab. I’ve worked at Apple as a software engineer and at Citadel Securities and Susquehanna International Group as a quant. In my free time, I like to play basketball, pickleball, lift, and rewatch Christopher Nolan movies! Feel free to reach out, would love to chat -- abhinav@lucidic.ai
Founder/CTO at Lucidic AI, working on an analytics, testing, and simulations framework for AI agent devs. Stanford BS/MS Computer Science (AI specialization). Ex Citadel, AppLovin. Previously built GrocerCheck, a COVID-19 web app helping shoppers social distance effectively, with over 200,000 users and endorsed by the Government of Canada. Reach me at andy@lucidic.ai
Hi! I'm Jeremy Tian, one of the founders and the Chief Scientist at Lucidic AI, working to explain your AI models' decisions. I have a BS/MS in Computer Science with an AI specialization from Stanford University. Ex. Quantitative Trader at DRW and software engineering/machine learning engineering at Steel Dynamics. Contact me at jeremy@lucidic.ai!
Cut AI agent debugging time from days to minutes with visual workflows and simulations
Lucidic launches an AI agent analytics platform that maps and visualizes every step of an agent’s workflow, simulates performance at scale, and provides searchable workflow replays, decision nodes, and side-by-side comparisons. It targets teams building AI agents and aims to reduce debugging and iteration time from weeks to minutes by offering real-time, structured feedback and scalable testing.
interactive video models
Live Environments for Training AI Agents
