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Guides
Practical guides for building and assessing software teams
Madewithlove audits, builds and leads software teams. These guides write down what 190+ technical audits have taught us: what investors should check before they commit, how founders and CTOs can judge their own team, and when outside technical leadership makes sense. Each guide answers its question in the first paragraph, then goes deeper.
For investors and acquirers
Technical Due Diligence Checklist for SaaS Acquisitions
A practical technical due diligence checklist for investors and acquirers evaluating a SaaS company, the five areas to assess, the red flags that matter, and how to turn findings into a deal decision. Based on 190+ audits by madewithlove.
Questions it answers
- How strong is the team and leadership?
- How does the team build and ship software?
- How healthy is the codebase?
For founders and CTOs
How to Evaluate a Software Engineering Team
How to evaluate a software engineering team across delivery, reliability, code quality, team health and business impact, using DORA and SPACE, not lines of code. A practical framework from madewithlove's 190+ engineering audits.
Questions it answers
- How fast and predictably does the team deliver?
- How stable and reliable is delivery?
- How maintainable is the code and system?
How to Choose a Fractional CTO
How to choose a fractional CTO for your startup or scale-up: what the role actually does, the criteria that matter, the engagement models, and the red flags to avoid. Practical guidance from madewithlove's fractional CTO practice.
Questions it answers
- Does the leader fit your stage?
- Have they done the job, not just advised on it?
- What is the scope and engagement model?
How to Implement AI in Your Engineering Team
How to implement AI in a software engineering team: treat it as an operating-model shift, not a tool rollout. The readiness checks, tool-selection criteria, onboarding, review changes and metrics that make adoption work. Drawn from 190+ technical audits by madewithlove.
Questions it answers
- Why start with the operating model, not the tool?
- Is your team ready for AI?
- How should you select AI tools?