ARKADIN is a neuro-symbolic AI architecture built around a generate-and-verify loop. It is designed to run inside your walls, record provenance, check proposals against explicit constraints, and abstain when confidence is insufficient.
A generative hemisphere proposes. A symbolic verifier checks the proposal against formal constraints before anything is trusted. Two minds, one answer.
When confidence is low the system says so, and says what would resolve it. For regulated work, a known unknown is worth more than a confident guess.
Designed for air-gapped deployment, with local identity, tamper-evident audit and an operator surface your own IT team can inspect.
Most AI failures in regulated settings are not failures of capability. They are failures of accountability: an answer arrives with no way to check how it was reached, no record of what it relied on, and no signal when it should not have been trusted at all.
ARKADIN is built the other way round. Every proposal carries its provenance. Every verification is logged. Every abstention is explicit. The audit trail is not a compliance afterthought, it is the architecture.
The generate-and-verify core, multi-agent orchestration, selective abstention, on-premises inference on open-weight models, tamper-evident audit, and the operator surface.
Adaptive workspace routing, self-improving scaffolding, and a domain-tuned model shipped as a matched pair with the harness. Scheduled means planned and resourced, not delivered.
Recursive weight improvement and formal verification of arbitrary natural language remain unproven by anyone. We do not describe them as capability in hand.
We would rather be held to what we can defend than impress anyone with what we cannot.