Frameworks, techniques, and tools for evaluating businesses using public data, financial signals, and AI-powered diagnostics.
Financial statements measure outcomes. The qualitative signals that don't show up in any spreadsheet often tell you what's about to happen.
Job postings are strategic documents. When analyzed collectively, hiring patterns form one of the clearest maps of where a company is actually headed.
If you pull ESG ratings for Tesla from three different providers, you will get three meaningfully different answers. This is not a bug in the system. It is the inevitable result of measuring something genuinely complex using different frameworks.
Remote work has weakened traditional analysis signals like office lease data while strengthening digital signals like job postings, employee distribution, and technology stack choices.
Automation handles data collection and pattern detection. Humans handle interpretation and strategic implications. Neither alone produces great analysis.
AI gets company analysis wrong in specific, predictable ways. Entity confusion, temporal errors, and source conflation produce polished output that looks correct but is not. Here is how to catch it.
A bad client can cost more than they pay. The agencies that grow profitably vet potential clients with the same rigor clients use to evaluate agencies.
Professional services firms are unusual because their primary assets walk out the door every evening. But the right indicators can reveal business health with unusual clarity.
Coaches working with small business owners often rely on the owner's narrative alone. Access to independent company diagnostics changes the coaching conversation entirely.
Coaches working with small business owners often rely on the owner's narrative alone. Access to independent company diagnostics changes the coaching conversation entirely.
Analyzing private companies presents unique challenges due to limited public data. Discover strategies and tools for evaluating non-public companies with confidence.