ByteStack Labs
ByteStack Labs provides production reliability verification for AI systems that pass offline evals but fail under real-world load. The company runs reproducible production ML autopsies, isolating root causes through ablation and delivering reports where every metric can be re-derived from public, runnable code repositories.
- Artificial Intelligence
- Data & Analytics
- Developer Tools
- Software Only
- AI Agents
Funding
Founders
Founder details are not available yet.
Product
Problem
AI systems frequently pass offline evaluations yet fail under real-world production load, creating a dangerous trust gap. Engineering leaders cannot manually review the output of autonomous agents, and conventional testing methods rarely detect the architectural weaknesses, mathematical flaws, or silent errors that emerge only in live environments.
Solution
ByteStack Labs provides a verification floor for AI systems by running reproducible production autopsies. The company reproduces failures on production-realistic inputs, quantifies the gap between eval and production performance by slice, and isolates root causes through systematic ablation. Every finding is delivered with runnable code and raw data, so clients can independently re-derive each number rather than relying on assertions. The approach relies on public "receipts" repositories where all metrics, including error rates and field-moving counts, are transparently committed and verifiable with simple scripts. The company also offers architecture and engineering services to design AI systems with validation at each layer, plus advisory guidance for infrastructure decisions before capital is committed.
Target Audience
Primary customers are engineering leaders and technical executives who oversee AI agent deployment and need reproducible proof that systems hold up under production load, not just favorable offline eval scores.
Features
- Production ML autopsy service that reproduces failures on production-realistic inputs to identify architectural and mathematical weaknesses
- Eval-to-production gap quantification by slice, with root-cause isolation via ablation across agent, model, and data stacks
- Calibration and silent-failure review that finds where a system is confident and wrong, catching well-formed-but-wrong outputs that carry no detectable signal
- Agent trajectory evaluation that assesses the whole agent path rather than per-step passes, identifying the step that introduces failure when each step looks fine
- Public receipts repository containing raw data and verification scripts that let users re-derive every reported metric via git clone and Python execution
- Rigorous verification discipline where every number traces to runnable code, and any failed reproduction exits with a non-zero status