Independent applied AI research
Applied AI systems for messy business workflows.
Vorp Labs studies and builds the infrastructure behind useful AI: coding-agent cost reduction, workflow evals, model routing, retrieval systems, enterprise data agents, and small-model specialization.
Benchmarks should measure workflows, not isolated model trivia.
Repeated context is usually a systems problem, not a user discipline problem.
Small models can win when the task, harness, and verification loop are narrow enough.
Cost, latency, and auditability are product requirements, not implementation details.
Private programs
Productized where the pain is clear, exploratory where the shape is still emerging.
AI Coding Cost Audit
Reduce Claude Code, Cursor, Codex, Copilot, and agentic coding spend by improving context, memory, prompts, tools, and model routing.
Research trackEnterprise Data and Legacy Workflow Agents
Spreadsheet, finance, reconciliation, reporting, and legacy-system workflows where AI has to inspect, transform, and verify structured data.
Design partnerWorkflow Eval Harnesses
Task definitions, graders, traces, and acceptance checks for systems that need more than a clean demo.
Research trackInternal Knowledge Systems
Retrieval, recall, source-grounded answers, and company memory systems that make AI useful inside teams.
Cost diagnostic
Most teams know usage is rising before they know why.
The useful signal is not a perfect diagnosis. It is the moment when Claude Code, Cursor, Codex, Copilot, MCP tools, or internal agents start feeling expensive, slow, repetitive, or hard to govern.
The audit turns that vague cost anxiety into a concrete map of repeated context, prompt drift, tool overhead, missing memory, and work that should move to retrieval, scripts, smaller models, or reusable commands.
Follow the research
New benchmarks, tools, and field notes. No launch spam.