Programs

Productized offers first, research tracks underneath.

Vorp Labs is organized around narrow private programs where the pain is concrete, plus research tracks that collect benchmarks, tools, and workflow examples before becoming standalone offers.

Private programs

Current SKUs and engagement shapes.

Available now

AI Coding Cost Audit

A focused audit for teams spending heavily on Claude Code, Cursor, Codex, Copilot, or custom coding agents.

Usage and workflow cost baseline.
Context, memory, prompt, and recall architecture.
MCP/tooling token overhead review.
Model routing across frontier, cheaper API, open-source, retrieval, and scripts.

Private sprint

Agent Workbench Setup

Hands-on implementation after an audit: project memory, prompt libraries, repo instructions, reusable commands, and team playbooks.

Durable knowledge base for coding agents.
Stored prompts and reusable task briefs.
Recall/MCP setup for prior decisions and debugging lessons.
Team conventions for session starts, review loops, and handoffs.

Design partner

Workflow Eval Harness

A scoped build for teams that need to know whether an AI workflow is actually getting better.

Task definitions based on real work.
Graders, traces, and acceptance checks.
Quality, latency, cost, and human-review metrics.
Failure-mode reporting instead of opaque accuracy numbers.

Research tracks

Where submitted workflows become sharper offers.

enterprise data legacy workflows

Enterprise Data and Legacy Workflow Agents

Spreadsheet agents live inside a broader enterprise data problem: financial models, reconciliations, CRM exports, legacy portals, file-heavy operations, and structured data that AI has to inspect and verify.

Where should an AI workflow use a spreadsheet agent, deterministic script, extraction step, or human approval?
How do financial-modeling subagents prove they changed the right cells for the right reasons?
Which legacy systems are best handled through migration scripts, APIs, browser automation, or an AI layer on top?

model cost engineering

Model Cost Engineering

Routing, caching, embeddings, fine-tuning, and small-model substitution for high-volume AI products and internal tools.

When does a smaller model beat a frontier model on cost-adjusted useful performance?
Which requests should route to retrieval, fine-tunes, frontier models, local models, or deterministic code?
How should teams evaluate latency, quality, auditability, and spend together?

internal knowledge systems

Internal Knowledge Systems

Permission-aware retrieval, source-grounded answers, recall systems, and evaluation methods for company knowledge.

Why do internal AI search tools answer confidently with stale or missing context?
How should permissions, freshness, and source quality be tested?
What UX makes asking the organization questions feel dependable instead of performative?

workflow ai

Workflow AI

Patterns for turning model capability into repeatable, auditable business processes.

What separates a good demo from a reliable workflow system?
Where should humans approve, edit, or supervise model output?
How do evals change when the unit of work is an end-to-end business process?

Contribute a workflow

Useful examples beat abstract roadmaps.

If you have a coding-agent cost problem, messy spreadsheet or legacy data workflow, internal search problem, or workflow automation candidate, share a short description. We use submissions to decide which benchmarks, tools, and private programs to build next.

Start a diagnostic