The Diligence Machine
Generate a customized, prescriptive technology discovery agenda.
Your selections stay in your browser. No deal data is transmitted.
Transaction Type
What type of deal is being evaluated?
Product Type
What does the target company build or deliver?
Tech Stack Archetype
How is the technology infrastructure provisioned?
Benchmark infrastructure costs against peers with TechPar .
Company Profile
Describe the target company's scale and maturity.
Target Size (Headcount)
Revenue Range
Growth Stage
Company Age
Geography
Where does the target operate? Select all that apply.
Business Model
What is the primary delivery and monetization model?
Scale Intensity
What is the operational scale and user volume pressure?
Evaluate cloud cost governance practices with the Infrastructure Cost Governance tool.
Transformation State
What is the current state of technology modernization?
Estimate technical debt remediation costs with the Tech Debt Calculator .
Data Sensitivity
What is the sensitivity level of the data the target handles?
Explore jurisdiction-specific requirements in the Regulatory Map .
Operating Model
How is the engineering organization structured?
How the Diligence Machine works
Question bank
The engine draws from a bank of 68 due diligence questions organized into four topics: Architecture & Scalability, Operations & Delivery, Carve-out / Integration, and Security, Compliance & Governance. Each question carries a priority level (high, medium, or standard), a rationale explaining why it matters, and conditional triggers that determine when it appears.
Conditional matching
Questions are filtered against your inputs using AND logic across 14 dimensions: transaction type, product type, tech stack archetype, headcount, revenue range, growth stage, company age, geography, business model, scale intensity, transformation state, data sensitivity, and operating model. A condition left undefined acts as a wildcard (matches everything). Ordinal fields—headcount, revenue, and company age—use “at least” comparison so that a threshold set at “51–200 employees” also matches larger brackets.
Balancing algorithm
After filtering, the engine balances questions across topics. It reserves a minimum of three questions per topic (where available), then fills remaining slots by priority until the total reaches 15–20. This ensures every topic is represented while surfacing the highest-priority items first.
Attention areas
29 attention areas surface structural risk factors—such as hardware end-of-life exposure, key-person dependencies, or manual operations masking—triggered by specific input combinations. They are sorted by relevance (high, medium, low) and displayed before the question agenda.
Archetype pivot
When the target operates on-premise or self-managed infrastructure, questions that are exclusively relevant to cloud-native architectures are filtered out. Mixed-archetype questions are preserved.
Limitations
The generated agenda is a starting point, not a complete diligence program. Questions reflect common technology patterns and should be extended, revised, or replaced based on target-specific discovery. The tool does not assess financial, legal, or commercial diligence dimensions.
Privacy
All processing runs entirely in your browser—no deal data is transmitted to or stored on our servers. Anonymous page-view and interaction analytics (Google Analytics 4) help us improve the tool. See our privacy policy for details.
Methodology last updated March 2026