Series A to Series B: Building AI Architecture That Passes Technical Due Diligence

About the Client

This case study illustrates my Startup-to-Scale methodology for transforming demo-quality AI products into investor-ready platforms. The scenario reflects the pattern I see in Series A→B transitions: real product traction but demo-grade architecture that fails technical due diligence. Metrics represent typical outcomes. Illustrative scenario, not a specific client engagement.

Size: Typical engagement: 20–80 employee AI-native startups

The Challenge

Series A raised on traction and demo. 12 months later, needed to show Series B investors production-grade AI — not a GPT wrapper — to survive technical due diligence.

• Architecture built as a monolith wouldn't scale past 1,000 concurrent users — Series B required proof of 10,000+
• No model evaluation framework: couldn't demonstrate AI accuracy, reliability, or improvement over time to investors
• Critical security vulnerabilities in the RAG pipeline including prompt injection risks and data leakage between tenants
• Zero SOC 2 compliance — a hard requirement for enterprise B2B customers and Series B institutional investors
• Technical due diligence from three separate firms would examine architecture, security, and AI capabilities
• 12-month runway remaining: needed Series B closed within 6 months or face a down round

My Solution

Hyperion embedded as fractional CTO for 90 days, transforming the architecture from a demo-quality monolith into an investable, production-grade AI platform.

Three-phase transformation: architecture redesign for scale, production-grade AI pipeline with security hardening, and investor-ready compliance and documentation. Every change designed to pass independent technical due diligence.

Implementation Phases

• 3 weeksArchitecture RedesignMigrated from monolith to event-driven microservices architecture. Implemented production-grade RAG with evaluation harness, vector store optimisation, and multi-tenant isolation.
• 5 weeksProduction AI Pipeline & SecurityBuilt model versioning and A/B testing framework. Implemented prompt injection defenses, data privacy controls, tenant isolation in the RAG pipeline, and comprehensive logging for audit trails.
• 4 weeksSOC 2 & Investor ReadinessPrepared and achieved SOC 2 Type I certification. Built investor data room with complete technical documentation, architecture decision records, and performance benchmarks for due diligence.

TypeScript · Python · PostgreSQL · Redis · Apache Kafka · Kubernetes · Pinecone · LangChain · OpenAI API · Datadog · Terraform · GitHub Actions · Vanta (SOC 2)

Results & Impact

Illustrative scenario: transforming a demo-quality AI startup into an investor-ready platform that passes institutional technical due diligence.

Services Delivered

Startup-to-Scale · AI Tech Due Diligence · SOC 2 Fast-Track

Want Results Like These?

Every engagement starts with a 30-minute diagnosis. Describe your situation, and I will tell you — honestly — whether I can help and how fast.