The BFN Manifesto
A disciplined approach to applied AI
What BFN stands for
Start with the problem. Define the business workflow, the failure point, and the decision that must improve before discussing models.
Measure value in business terms. Track time saved, quality gained, risk reduced, and cost controlled. If the lift is unclear, the work is not ready.
Choose the simplest approach that works. Use rules when rules are enough, ML when prediction is justified, and GenAI when it creates a clear advantage.
Build on trusted data. Know the source of truth, data gaps, ownership, privacy constraints, and quality risks before building anything critical.
Design for safety and governance from day one. Security, privacy, auditability, and human oversight belong in the architecture, not in late stage cleanup.
Test against reality. Use acceptance criteria, regression checks, human review, and red team pressure to prove the system works in practice.
Operate what you ship. Monitoring, incident response, rollback paths, and cost controls are part of delivery. A system is not done when it launches.
Improve continuously. Log decisions, measure outcomes, learn from failures, and update the system on a clear cadence.
Our standard is simple:
If it cannot show value, ship safely, and operate reliably, it is not done.