Insights
Briefings from the studio.
What we've learned building private models for regulated boundaries — the economics, the provenance, and the evaluation. One argument per piece, grounded in how the work actually runs.
- LeanLogix Model Studio7 min read
The meter is the leak: why per-token billing is a governance decision, not a pricing one
A per-token meter is usually filed under cost. In a regulated boundary it is a data-egress decision in disguise — every metered call is a conversation that left, and a record someone else now keeps. The case for serving private models at a flat license, with no external meter in the inference path.
Read the briefing - LeanLogix Model Studio7 min read
Trust you can re-run: the signed passport, and why a screenshot is not provenance
Most AI trust claims are screenshots of a dashboard you have to believe. We sign every release over its verbatim bytes — data, method, eval, approver — so an auditor recomputes the fingerprint offline, with curl and a public key, and gets the result rather than our word. What separation of duties looks like when the proof is the product.
Read the briefing - LeanLogix Eval Standards6 min read
A correct answer is not a safe one: why regulated AI needs its own benchmark
General-purpose leaderboards grade the answer. A payer or a bank is liable for the run where a correct answer leaked an identifier or obeyed an injection — failures of the journey, not the destination. What a regulated benchmark scores instead, and why the audit trail is part of the score.
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