Measurable Impact
R&D Capital Audits produce quantifiable results. Here are representative examples of how we've helped PE firms, boards, and engineering leaders.
PE Due Diligence: Series C SaaS (FinTech)
Situation
Private equity firm evaluating a $180M Series C FinTech acquisition. CTO presented a clean technology story. The board needed independent validation before closing.
Findings
- ⚠Technical Insolvency Date: 14 months away
- ⚠Innovation Tax: 58% (critical)
- ⚠Hidden infrastructure debt: $4.2M in deferred cloud migration
- ⚠AI model retraining pipeline: no automation, manual-only
Impact
- ✓Renegotiated acquisition price by $12M based on debt quantification
- ✓Created 18-month remediation roadmap with quarterly milestones
- ✓Avoided a potential $8M surprise infrastructure migration post-acquisition
SaaS Scaling: Growth-Stage Product (B2B)
Situation
B2B SaaS company with 45 engineers shipping 3x slower than 12 months prior. CEO couldn't explain to the board why feature velocity collapsed despite growing the team 2x.
Findings
- ⚠Maintenance load consuming 65% of engineering capacity
- ⚠No feature flags — every deploy was a full release
- ⚠Monolith architecture creating merge conflicts that cost 15 hours/week
- ⚠Zero observability — MTTR averaged 4+ hours
Impact
- ✓Reduced Innovation Tax from 65% to 32% in 9 months
- ✓Implemented strangler fig migration from monolith
- ✓Deployed feature flag infrastructure (LaunchDarkly)
- ✓MTTR reduced from 4+ hours to 28 minutes
AI Cost Optimization: ML-Heavy Product (HealthTech)
Situation
HealthTech company with AI-powered diagnostic tool. Gross margins dropped from 72% to 41% as AI inference costs scaled with customer growth. Board demanded margin recovery.
Findings
- ⚠AI COGS: $0.47 per inference (target: $0.08)
- ⚠No model optimization — running full-precision GPT-4 for every query
- ⚠Redundant API calls: average 3.2 LLM calls per user request
- ⚠No caching layer — identical queries re-processed every time
Impact
- ✓Reduced inference cost from $0.47 to $0.06 per query
- ✓Implemented response caching (85% cache hit rate)
- ✓Migrated simple queries to fine-tuned open-source model
- ✓Gross margins recovered from 41% to 68%
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