Traditional M&A tech due diligence takes 2-3 months and costs $150-300K in consulting fees. Most of that time is spent doing what an AI can do in hours: mapping features, identifying technical debt, and assessing team knowledge distribution.
The Traditional Process
A typical tech due diligence engagement:
Week 1-2: Consultants get repository access. They manually review the codebase, interview engineers, and catalog features.
Week 3-6: Deep analysis. Architecture diagrams (drawn manually). Dependency mapping (done by reading code). Technical debt assessment (based on gut feeling and code smells).
Week 7-10: Report writing. The 80-page PDF that the acquiring company's CTO will skim.
Week 11-12: Follow-up questions, clarifications, and final presentation.
Cost: $200K+ for a mid-market deal. Timeline: 12 weeks. Accuracy: depends entirely on the consultants' ability to understand an unfamiliar codebase quickly.
What Automated Analysis Delivers
With automated code intelligence, the same analysis takes 3 days:
Day 1: Indexing and Feature Discovery
- Connect to repositories (30 minutes)
- AI agents analyze the codebase: symbol extraction, dependency mapping, feature clustering (2-4 hours)
- Output: complete feature inventory with boundaries, dependencies, and complexity metrics
Day 2: Risk Assessment
- Team knowledge analysis: who knows what, bus factor by feature
- Technical debt scoring: dependency complexity, code duplication, test coverage gaps
- Architecture assessment: coupling metrics, service boundaries, data flow patterns
Day 3: Competitive and Strategic Analysis
- Automated competitive gap detection: what does the target build vs. what competitors offer?
- Feature maturity assessment: which features are production-ready, which are prototypes?
- Integration risk: how compatible is the target's architecture with the acquirer's?
What You Get
Instead of an 80-page PDF written by consultants who spent 2 months reading code:
- Feature catalog generated from actual code, not interviews
- Dependency graph showing real architectural relationships
- Team knowledge map identifying single points of failure
- Competitive positioning scored against market alternatives
- Technical debt quantification based on structural metrics, not opinions
- Integration risk assessment comparing tech stacks and patterns
The accuracy is higher because it's derived from code analysis, not from engineers describing their own work (which is always optimistic) or consultants interpreting unfamiliar code (which is always incomplete).
When It Matters Most
This approach is most valuable for:
- Acqui-hires where you're buying the team + code: know the knowledge concentration risk before you close
- Platform acquisitions where you'll integrate the code: understand the real dependency complexity
- Competitive acquisitions where you need the features: verify the feature set actually exists in code, not just in the marketing deck
- Portfolio assessment for PE firms: evaluate multiple targets quickly at a fraction of the traditional cost
The ROI
Traditional due diligence: $200K, 12 weeks, accuracy depends on consultants.
Automated analysis: fraction of the cost, 3 days, accuracy verified against actual code.
Even if you still hire consultants for the final assessment, giving them a pre-built feature inventory, dependency graph, and knowledge map cuts their engagement time in half. That alone pays for the tooling.
Originally published on glue.tools. Glue is the pre-code intelligence platform — paste a ticket, get a battle plan.
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