RELAI
Category: AI Infrastructure
Verifiable continual learning platform for AI agents that turns failures into replayable learning environments for continuous, regression-aware improvement RELAI was founded in 2024. The company is led by Soheil Feizi. Based in Bethesda, Maryland, United States. Team size: 2-10. Total funding raised: $6.9M. Latest round: Pre-Seed. Key investors include .406 Ventures, AI Tinkerers Fund (AITFund), Non sibi Ventures, TEDCO.
- Founded
- 2024
- Headquarters
- Bethesda, Maryland, United States
- Team size
- 2-10
- Total funding
- $6.9M
Value proposition
RELAI provides a learning infrastructure that automatically captures AI agent failures, diagnoses root causes across the full agent stack (prompts, tools, memory, workflows, models), generates improvements, and validates them against prior behavior — enabling enterprises to continuously improve production AI agents without introducing regressions.
Products and solutions
RELAI Verifiable Continual Learning Platform: (1) Learning Environments — converts failures, traces, evaluations, and human feedback into replayable learning environments, (2) Lifelong Agent Optimizer (Maestro) — continuously optimizes prompts, tools, memory, models, workflows, and agent logic as one unified system, (3) Learning System of Record — tracks every failure, scenario, diagnosis, candidate fix, evaluation, and shipped improvement, (4) Agent Simulation — create environments with LLM personas, mock MCP tools, synthetic data, (5) Agent Evaluation — code-based and LLM-based evaluators, (6) relai-sdk (open-source on GitHub)
Unique value
Online, in-loop regression control — validates improvements against a growing library of prior environments during optimization (not after), preventing regressions before they ship. Routes fixes to the correct layer of the agent stack (prompts, tools, memory, workflows, models, code) rather than relying on prompt engineering alone.
Target customer
Enterprise AI teams deploying AI agents in production across financial services, healthcare, and other business-critical domains
Industries served
Financial services, Healthcare, Enterprise AI operations, Customer service
Technology advantage
Verifiable Continual Learning methodology; online in-loop regression control; holistic agent-stack optimization (not just prompts); built on years of academic research in AI reliability by founder Soheil Feizi (UMD Reliable AI Lab); integrates with all major agent frameworks (LangGraph, OpenAI Agents SDK, Google ADK) via CLI and workflow integrations
How they differentiate
Unlike existing observability/evaluation platforms that only check for regressions after changes are shipped, RELAI keeps regression control within the optimization pipeline itself (online, in-loop). It also diagnoses root causes across the full agent stack and applies fixes at the correct layer, rather than relying on prompt engineering alone. Its Verifiable Continual Learning approach turns every failure into a reusable learning signal that compounds over time.
Main competitors
LangSmith (LangChain Labs), Arize AI, Braintrust, Galileo, Robust Intelligence
Key partnerships
NVIDIA Inception program, Google Cloud for Startups program, National Science Foundation SBIR award, .406 Ventures, AI Tinkerers Fund (AITFund), Non sibi Ventures, TEDCO
Notable customers
C3 AI (Nikhil Krishnan, CTO & Chief AI Officer, provided testimonial)
Major milestones
2024: Company founded, 2025: Received NSF SBIR award, Joined NVIDIA Inception program, Joined Google Cloud for Startups program, Won AI 2030 Accelerator Pitch Competition, 2026-06: Launched Verifiable Continual Learning Platform, Raised $6.9M total funding ($5.4M pre-seed led by .406 Ventures + $1.5M prior), Public preview launch June 2026
Growth metrics
Reported improvements: financial services agent validation score from 39% to 80%; healthcare agent from 62% to 96%
Market positioning
Early-stage entrant in the rapidly growing AI agent reliability and observability market, positioned as a learning infrastructure layer rather than just an observability/monitoring tool. Differentiated by its academic-rooted research in AI failure analysis and its focus on continuous, regression-aware improvement.
Geographic focus
United States (DMV/Boston area), with remote team
About Soheil Feizi
Associate Professor of Computer Science, University of Maryland (Director of Reliable AI Lab); PhD from MIT; Postdoctoral Researcher at Stanford University; Recipient of 2025 Presidential Early Career Award for Scientists and Engineers (PECASE); NSF CAREER Award; ONR Young Investigator Award; ARO Early Career Award; 100+ AI research papers, 15,000+ citations
Official website: https://relai.ai