We help AI-native teams find production blockers across evals, retrieval, model choice, serving architecture, GPU utilization, sandboxing, regression checks, and provider strategy.
Teams ship promising demos, then stall when hallucinations, retrieval misses, weak eval coverage, and edge-case regressions appear in production.
Serving architecture, batching, routing, cold starts, provider mix, and GPU utilization quietly decide whether the product feels fast and margin-positive.
Code execution, file access, user-generated code, tools, and agentic workflows need strong boundaries before they become customer-facing reliability or security risks.
AI products need repeatable quality gates for retrieval, tool use, summarization, extraction, reasoning, refusal behavior, and user-visible regressions.
Model choice, batching, caching, routing, GPU utilization, provider failover, and observability determine product responsiveness and cost per request.
Sandboxed file and code execution, scoped permissions, audit logs, test fixtures, and abuse-case coverage help agent products move beyond prototype risk.
Finds where quality, latency, cost, retrieval, model choice, or eval coverage is blocking production readiness.
Optimizes serving architecture, batching, model routing, cold starts, cost per request, GPU utilization, and observability.
Secures code and file execution for agents, analysts, user-generated code, AI coding products, and internal automation tools.
Runs ongoing evals, benchmark checks, regression reviews, cost analysis, and model/provider update planning.
Builds test sets, retrieval metrics, source-grounding checks, and release gates for production search and answer workflows.
The team ships features by feel, manually inspects outputs, watches latency and spend drift, and reacts to production regressions after customers notice.
The product has eval suites, benchmark runs, cost-per-request visibility, routing strategy, sandbox boundaries, and release gates tied to production risk.
Sensitive workflows can use hosted private-cloud inference, dedicated cloud or VPC deployment, or local/on-prem inference when customer data, prompts, code, or files cannot leave your environment.
Avoid billing surprises with clear workflow-based packages. We do not penalize you for using more AI like other vendors.
Every system can include benchmark history, eval results, trace IDs, model/provider decisions, sandbox events, reviewer notes, and deployment history.
We choose one production bottleneck: eval coverage, inference cost, latency, retrieval quality, agent sandboxing, or release reliability.
We build the audit, eval harness, optimization path, sandbox design, or benchmark loop needed to make the risk measurable.
You get quality, latency, cost, reliability, and safety metrics plus a roadmap for the next production hardening step.
Founder, Performance AI Lab | Former Meta SuperIntelligence Lab
With 25 years in software engineering and enterprise infrastructure, I've built systems for some of the world's most demanding environments. I founded Performance AI Lab to bring private, auditable AI workflows that reduce manual work and preserve operating know-how to mid-market operators.
If quality, latency, inference cost, eval coverage, retrieval, or agent safety is slowing the roadmap, we can help harden the system.
Private AI workflows for the work that falls between your documents, apps, approvals, and operating knowledge.