Deterministic runtime trust for AI-generated software

The Deterministic Immune System for AI-Generated Software.

Bridging the gap between LLM logic and production reality. Capture one-in-a-million crashes with <5% overhead and Automated Root Cause Analysis. Zero-privilege deployment for instant, deterministic insights in any production environment.

Request Stealth Access Watch the PoC

Founder-market fit Enterprise-grade posture Systems-first execution

Why it matters

One-in-a-million capture: Persist elusive crashes before they disappear into noise.
<5% runtime overhead: Full-fidelity observability without sacrificing production speed.
Zero-privilege deployment: Drop-in instrumentation aligned with strict enterprise policies.

THE PRODUCT

Meet Watcher: Deterministic Engineering at Scale.

Watcher is a deterministic debugging and reliability engine for AI-generated and high-performance software, making execution states reproducible and failures actionable under real-world load.

Production-Grade Record-and-Replay: Based on 10+ years of SOSP/PLDI research, captures exact execution state of any failure for deterministic forensics.
Invisible Overhead: Optimized for under 5% performance impact, unprecedented for deterministic engines at production scale.
CUDA & GPU Native: First engine to support high-concurrency GPU/CUDA debugging for ML infrastructure and AI-native workloads.
Zero-Privilege Security: Deploy in strict enterprise environments without root access or kernel modifications.

RLEF Ready: transforming production ground truth into the feedback loop for self-healing software.

The Problem

AI Scales Code. Teyon Scales Trust.

LLMs are transforming software development, but they remain runtime-blind. Hallucinations that pass CI/CD eventually break in production, creating invisible failures that legacy APM tools cannot diagnose.

The Teyon Insight: You cannot fix what you cannot reproduce. Without a deterministic record-and-replay ground truth, AI-generated software remains a liability, not an asset.

Runtime blind spot: what enterprises face

Runtime blindness: model outputs drift in production beyond CI/CD visibility.
Invisible failures: APM tooling cannot isolate AI-generated root causes.
No reproducibility: incidents cannot be replayed to prove correctness.
Deterministic ground truth: required to turn AI code into auditable infrastructure.

The Team

Built by the Grand Slam Winners of Systems Research.

Systems-first execution with industrial-grade rigor. We build deterministic infrastructure where proofs, reliability, and operational clarity are non-negotiable.

Operator-led, execution-biased

We design with enterprise constraints in mind: predictable behavior, clear failure modes, and operational legibility at scale.

Founder & CEO

Leadership

Former UMass/UTSA Professor; Principal Engineer at XXX and Staff Researcher at ByteDance.
Career Grand Slam: SOSP, PLDI, ICSE, OOPSLA.
Inventor of iReplayer and Watcher; 11+ US patents in systems performance and reliability.

Bin Ni — Strategic Advisor

Strategy

Former Head of AI Coding at Google DeepMind.
Pioneer in Autonomous AI Software Engineering.
Led 13 Google I/O announcements.

Youfeng Wu — Technical Advisor

Technical

Former Director, Intel Programming Systems Lab.
World-renowned authority in compilers and binary instrumentation.
30+ years of leadership in systems research.