writing
building in public: what we ship, what breaks, what we learn.
what to ask an ai vendor before you sign anything
the questions that actually matter when an AI vendor pitches your firm: auditability, fallback, data residency, and exit terms.
how to run AI on client data without a compliance headache
a practical way to put AI on client data without handing it decision power: split reading from deciding, keep infrastructure yours, keep a human in the loop.
why an audit trail matters more than accuracy
a 99% accurate model that can't show its work is a bigger liability than a slower system you can audit line by line. here's the actual split.
how to avoid a hostage situation with your automation vendor
a working system, held in someone else's cloud, is a hostage negotiation waiting to happen. here is how to structure ownership before you sign.
why the AI should extract facts and never make the call
in regulated work, AI should extract facts from documents. a deterministic system, not the model, should make the decision. here's why that split matters.
the difference between an AI that grades facts and one that grades merit
AI can extract facts from a file reliably. Assessing legal merit is a different task entirely, and confusing the two is how firms get burned.
what an auditable ai decision actually looks like
most vendors log model inputs and outputs and call it auditable. that tells you what happened, not why. here is the difference.
why deterministic code should make the final call, not the model
in regulated work, the model should read and route. only deterministic code should decide. here is why that split is the whole product.
how to let AI touch regulated work without deciding
a working method for putting AI inside regulated processes while keeping every decision in a deterministic, auditable function a model never touches.
how to explain an automated decision to a regulator
a regulator does not need to trust the model. they need a decision they can trace to a rule. here is how to build that trail.
which parts of your workflow are safe to automate
the sort test isn't repetitive vs. rare. it's fact vs. judgment. here's the six-step way to find the real boundary in a regulated workflow.
parallel agents won't save you (the part the demos skip)
throwing 300 agents at a problem isn't the unlock everyone thinks. the unlock is what brackets the swarm: before and after.