Caliper
A research instrument for observing language-model answers across experiments, models, and time. Model claims about their own training history are hypotheses to test, not provenance evidence.
What happens when people influence AI—and when AI becomes part of the organization doing the work?
We investigate the behaviour of language models, the design of AI-native products, and whether organizational controls can make AI agents measurably more reliable and independently auditable.
Can specialization, independent review, bounded authority, and persistent institutional memory make AI organizations work better? Can an external auditor reconstruct what happened and verify whether important controls operated? The answer requires controlled comparisons—not just convincing examples.
A research instrument for observing language-model answers across experiments, models, and time. Model claims about their own training history are hypotheses to test, not provenance evidence.
OrgDef, RoleDef and durable decision records offer one possible intervention: explicit positions, responsibility, escalation, review, and the possibility of dissent.
Work including airtrōv and catdef explores products that AI can use directly, as well as systems that depend on AI participation.
A founder's account of the question that led to Caliper, and the challenge of distinguishing compelling explanations from evidence.
Founder's essay: in editorial review.
AI-conducted interviews about the work, its assumptions, and its unresolved questions. The human and AI voices are identified openly.
Explore the seriesOur shared Confused Gorilla organization now has a founder-ratified structure: Institute and Labs, nine positions, declared review relationships and two currently staffed roles. You can inspect the actual charter, original MemoDef review trail, dissent, corrections, appointment record, and validation tests. We also document what has not yet been operationally verified.
Explore the organization and its complete governance archive
