Building an evidence-based AI fundraising narrative
Connect a specific customer problem to observed behavior, a credible product wedge, market logic, operating assumptions, and a clear use of capital.
Read articleBack to all guidance
Guidance library
AI product and company strategy
Adjacent guidance for teams evaluating, funding, launching, and operating AI products.
7 articlesGuidance library
Reviewed enterprise architecture guidance
Narrow the library by controlled topic, audience, pillar, or API product.
Testing product-market fit for an AI product
Use retention, repeated workflows, willingness to pay, expansion, and customer behavior to test demand without relying on enthusiasm or model novelty.
Read article
Understanding the cost structure of an AI startup
Model provider usage, infrastructure, engineering, evaluation, support, security, and acquisition costs as explicit assumptions that can change with scale.
Read article
Scoping an AI product MVP around one customer workflow
Choose a narrow problem, define the human fallback, instrument real use, and postpone autonomy or platform breadth until evidence supports it.
Read article
Build versus buy for enterprise AI infrastructure
Compare control, differentiation, integration depth, security, time, maintenance, switching cost, and the team's ability to operate what it owns.
Read article
Using Claude Code in a governed engineering workflow
Verify installation and commands against current documentation, then place repository access, approvals, secrets, testing, and audit boundaries around the tool.
Read article
Evaluating OpenClaw for controlled agent workflows
Review a self-hosted agent gateway through its channel access, tools, credentials, operating permissions, isolation, review points, and recovery path.
Read article