How organizations turn generative AI into controlled operational workflows.
AI governance becomes useful when it is embedded into workflow design, approvals, logs, and evidence, not left as a static policy document.
A scorecard for deciding whether an AI workflow should scale, stay in pilot, be redesigned, or be rejected.
Before building a copilot or agent, map the process friction, decision latency, rework, and evidence needed to prove value.
EigenKV explores KV-cache reduction for long-context AI workflows where memory cost, latency, and quality must be evaluated together.
EigenWeights explores model footprint reduction for controlled deployments where latency, infrastructure limits, and quality thresholds matter.