Technical due diligence for an AI SaaS acquisition

A venture buyer was acquiring CoCo.ai and needed to understand where the product stood technically before closing. CoCo had been built fast by a small team, which is normal for a startup at that stage. The buyer's question wasn't whether the code was perfect. It was: what's the real foundation, what will scaling cost, and what should the team prioritize after the acquisition?

• Assess product and platform readiness: quality practices, reliability, security, and observability. Where does the team stand relative to what post-acquisition scale requires?
• Map AI economics: multiple LLM-powered features were running in production, but nobody had modeled what they'd cost at 2x, 5x, or 10x usage. This was the biggest unknown.
• Build a practical post-close roadmap: testing, incident response, data handling, and infrastructure improvements, sequenced so the team can strengthen the foundation without losing momentum.

We ran a technical due diligence review in three days. This covered code quality (structure, testing, security, error handling, documentation) and infrastructure (hosting, databases, backups, monitoring, deployment, scalability). Every area got a pass, warn, or fail rating with evidence pulled directly from the code.

We modeled what infrastructure and AI costs would look like at 2x, 5x, and 10x their current scale. The AI piece got extra attention because it was the biggest wildcard. For each AI use case, we mapped out ways to cut costs: batching requests, caching responses, switching models, moving to async processing.

This was evaluation only. We didn't write code, fix infrastructure, or add tests. The deliverable was a prioritized list of what needed fixing, sequenced for the team to tackle after close.

Why it worked

• The pass/warn/fail ratings let non-technical stakeholders understand risk without needing to read code.
• Cost projections at different growth levels gave the buyer options for different scenarios.
• Breaking out AI costs separately showed where the biggest scaling problems would hit and how to address them.
• Three-day turnaround kept the deal moving.