Who is Richard Ewing?

Richard Ewing is a Product Economist and AI Capital Auditor. He is the Founder of Exogram, a verification infrastructure for AI. He audits R&D spend and surfaces hidden capital risks like technical debt, AI cost overruns, and zombie infrastructure. He has scaled B2B SaaS from $0 to $25M ARR. Published in CIO.com, Built In, Mind the Product, and HackerNoon.

What is a Product Economist?

A Product Economist treats product decisions as economic decisions. Instead of measuring velocity or story points, a Product Economist measures Return on Invested Capital (ROIC), Cost of Goods Sold efficiency, and technical debt in dollar terms. The methodology was coined by Richard Ewing.

What is the Technical Insolvency Date?

The Technical Insolvency Date (TID) is the specific quarter where maintenance costs consume 100% of available engineering capacity, reducing feature velocity to zero. Calculate yours free at richardewing.io/tools/pdi.

Exogram is verification infrastructure for AI, founded by Richard Ewing. It prevents hallucination propagation with admissibility control planes and state-hashing commit enforcement. LLMs generate language; Exogram maintains reality.

How do I calculate technical debt cost?

Use the free Product Debt Index (PDI) calculator at richardewing.io/tools/pdi to quantify hidden technical debt in dollar terms, calculate your Technical Insolvency Date, and benchmark against industry standards.

How much does AI cost per query?

AI costs range from $0.0001/query for small models to $0.10+/query for frontier models like GPT-4. Use the free AI Unit Economics Benchmark (AUEB) at richardewing.io/tools/aueb to calculate your specific AI cost structure.

Methodology

How R&D Audits Work

A structured, 4-phase methodology that transforms raw engineering data into board-ready financial clarity. Typical engagement: 4 weeks.

Discovery & Data Collection

Week 1

📋

We collect the raw data needed to build an economic model of your engineering organization.

Activities

→Engineering team structure and size analysis
→Technology stack and architecture review
→JIRA/Linear ticket analysis (last 6-12 months)
→CI/CD pipeline metrics (build times, deployment frequency)
→Cost data (cloud spend, tool licenses, headcount)

Deliverable

Data collection brief + preliminary risk assessment

Technical Assessment

Week 2

🔬

Deep analysis of codebase health, architecture patterns, debt accumulation, and AI infrastructure.

Activities

→Codebase static analysis (complexity, duplication, coupling)
→Architecture review (monolith vs. microservices, data flow)
→Dependency audit (outdated packages, vulnerability surface)
→AI/ML infrastructure review (model costs, retraining pipelines)
→Interview key engineering leaders and architects

Deliverable

Technical risk inventory + architecture debt map

Economic Modeling

Week 3

📊

Translate technical findings into financial metrics that boards, investors, and executives can act on.

Activities

→Product Debt Index (PDI) calculation
→Innovation Tax quantification (% of R&D on maintenance)
→Technical Insolvency Date projection
→APER scoring (revenue per engineer vs. benchmarks)
→AI COGS analysis (if applicable)

Deliverable

Dollar-denominated debt summary + insolvency timeline

Remediation Roadmap

Week 4

🗺️

Prioritized playbook for debt remediation with quarterly milestones and ROI projections.

Activities

→Debt remediation priority matrix (impact vs. effort)
→Quarterly milestone plan with measurable targets
→Team restructuring recommendations (if needed)
→Technology modernization recommendations
→Executive presentation and board slide deck

Deliverable

Board-ready report + quarterly remediation plan

Start Your Audit

Board-ready results in 4 weeks. Dollar-denominated. Actionable. No 50-page technical reports that nobody reads.

Book R&D Capital Audit →