Real Results.
Quantified Impact.

Challenge

Series B SaaS company with 40 engineers spending 47% of engineering time on maintenance. Feature velocity had declined 60% over 18 months. Board was questioning engineering leadership.

Approach

Ran PDI audit to quantify maintenance burden in dollar terms. Identified $2.1M annual engineering spend on zero-value maintenance. Implemented Kill Switch Protocol to deprecate 12 zombie features. Restructured team topology.

Results

• ✓Maintenance burden reduced from 47% to 23% in 6 months
• ✓Recovered $1.4M in engineering capacity
• ✓Deployment frequency improved from monthly to weekly
• ✓Feature velocity increased 2.8x

Challenge

Growth-equity PE firm with 4 portfolio companies ranging from $5M-$30M ARR. No visibility into engineering efficiency. Two portfolio companies showing declining DORA metrics.

Approach

Conducted cross-portfolio PDI audits. Benchmarked each company against industry peers using APER diagnostic. Identified common patterns: over-investment in custom tooling, understaffed platform teams, and absence of technical debt measurement.

Results

• ✓$2M in annual engineering waste identified
• ✓Standardized DORA metrics tracking across portfolio
• ✓One portfolio company avoided $800K custom build via vendor evaluation
• ✓Technical due diligence framework now used for all new acquisitions

Challenge

Seed-stage AI startup burning $180K/month on inference costs. Each customer interaction cost $0.47 but was priced at $0.10. AI features were popular but economically unviable at scale.

Approach

Used AUEB calculator to map Cost of Predictivity curve. Discovered that 80% accuracy (acceptable for use case) cost 90% less than 95% accuracy. Redesigned architecture: small model for simple queries, large model only for complex ones.

Results

• ✓Cost per interaction reduced from $0.47 to $0.08
• ✓AI feature margin flipped from -370% to +20%
• ✓Runway extended by 14 months without additional fundraising
• ✓Achieved product-market fit with sustainable unit economics

Challenge

Strategic acquirer evaluating a $25M acquisition target. Initial technical due diligence by internal team found "no major issues." Board wanted independent verification before closing.

Approach

Conducted comprehensive technical due diligence using PDI framework. Discovered: 68% maintenance burden (vs. 30% reported), 3 critical dependencies on unmaintained OSS libraries, and Technical Insolvency Date projected at 14 months.

Results

• ✓Identified $4M in hidden technical debt not disclosed in initial DD
• ✓Negotiated $4M purchase price reduction based on findings
• ✓Post-acquisition remediation plan saved additional $1.2M
• ✓Acquirer now uses PDI framework for all technical due diligence

Challenge

Fast-growing fintech with 60% first-year engineer attrition. Traditional coding interviews favored coding speed over engineering judgment. New hires couldn't review AI-generated code effectively.

Approach

Replaced traditional coding interviews with Audit Interview protocol. Candidates evaluate AI-generated code with hidden flaws instead of writing code from scratch. Focused on verification skills, severity ranking, and ship/no-ship judgment.

Results

• ✓First-year attrition dropped from 60% to 18%
• ✓Time-to-productivity for new hires improved by 40%
• ✓Critical bug detection rate improved 3x
• ✓92% of engineering team rated new hires as "strong" or "exceptional"

Challenge

Series B SaaS company preparing for Series C fundraise. NRR was 105% but board believed it should be higher. Engineering metrics weren't investor-ready. No clear story connecting engineering investment to business outcomes.

Approach

Used EV-SE to model valuation scenarios. Identified that improving NRR from 105% to 115% would increase valuation multiple by 2x. Focused engineering resources on expansion features and customer success tooling.

Results

• ✓NRR improved from 105% to 118% in 9 months
• ✓Series C closed at $12M higher valuation than initial board target
• ✓Investor deck included PDI and APER metrics — differentiated from every other pitch
• ✓Engineering story became the strongest section of the fundraise narrative

Challenge

Healthcare SaaS company deploying AI features that process patient data. No AI governance framework. Board concerned about EU AI Act compliance and HIPAA implications of AI-generated recommendations.

Approach

Implemented AI governance framework based on Exogram principles: AI liability gradient assessment, deterministic governance for high-risk decisions, provenance tracking for AI-generated recommendations, and PII air gap for patient data.

Results

• ✓AI governance framework implemented in 12 weeks
• ✓Achieved EU AI Act compliance for high-risk AI features
• ✓Reduced AI-related incident rate by 85%
• ✓Board confidence in AI deployment increased from 2/10 to 8/10

Challenge

E-commerce platform with 8-year-old monolith. Engineering team proposed full rewrite to microservices ($3M, 18 months). Board was skeptical after hearing Joel Spolsky's warnings about rewrites.

Approach

Conducted PDI audit. Found that only 30% of the monolith was causing 80% of maintenance burden. Recommended modular monolith approach with strangler fig pattern instead of full rewrite. Prioritized the 30% that mattered.

Results

• ✓Avoided $3M rewrite — total cost was $400K over 9 months
• ✓Maintenance burden reduced from 55% to 28%
• ✓Deployment frequency improved from bi-weekly to daily
• ✓Team morale improved significantly (NPS +45 points)