Enterprise AI infrastructure. Post-quantum secure. Compliance-native.

Three databases, each doing the job it is built for, with every query scoped to one organization by construction rather than by convention.

• PostgreSQL as the system of record, 204 schema migrations behind it
• A Neo4j knowledge graph for entity and relationship reasoning
• Redis for session state, tool result caching, and steering queues
• Credentials sealed at rest under a post-quantum key exchange

A full execution pipeline with cost accounting, approval gates, and constitutional constraints. Not a wrapper around a model SDK.

• One loop engine serves both supervisor sessions and delegated sub-agents
• Constitutional constraints enforced by the pipeline, not asked for in a prompt
• A budget ledger gates spend before the call goes out, not after the invoice arrives
• Human approval checkpoints on high-stakes actions, with the wait recorded
• Context compression keeps long sessions inside the window without losing the thread

Before any model call, seven providers assemble the working context in parallel. The model sees a curated window, not a database dump.

• Vector retrieval, graph retrieval, documents, customer records, compliance state, financial context, and corrections the system has already learned
• Graph reasoning on top of vector retrieval, so relationships survive the lookup
• Each provider decides whether a query is relevant before it does any work
• Providers resolve concurrently under a hard timeout, so one slow source cannot stall a turn
• Results are ranked by priority and fitted to the token budget at assembly time

Agents, humans, devices, and nodes are the same kind of thing: a principal with a cryptographic identity, a set of privileges, and an audit record.

• Privilege-based access control across 102 privileges, checked by capability and never by role name
• Every organization holds a commissioner key, and every agent, node, and device identity derives from it
• Agents carry their own DID and signing key, so work is attributable to the agent rather than to a human login it borrowed
• An agent private key is never stored. It is re-derived on demand and zeroized after use
• Device enrollment is gated on hardware attestation, or a TPM quote on a server

Frameworks modeled as code, with evidence collected while the system runs rather than assembled the week before an audit.

• 24 frameworks including SOC 2, HIPAA, GDPR, PCI DSS, ISO 27001, CMMC, FedRAMP, StateRAMP, FINRA, SOX, the EU AI Act, and the NIST AI RMF
• Automated checks and evidence capture where the platform holds the evidence, about 14% of controls today, with structured attestation workflows for the rest
• Evidence ties back to the agent action that produced it, so a sample can be traced
• POAM tracking and SPRS scoring for defense supply chain posture
• Every verification run is itself an audit record an assessor can sample

NIST-standardized algorithms, selectable per deployment. Level 3 by default, Level 5 where the deployment calls for it, and older records stay readable across the change.

• Level 3 by default: ML-KEM-768 key exchange and ML-DSA-65 signatures
• Level 5 for high-assurance deployments: ML-KEM-1024 and ML-DSA-87, aligned to CNSA 2.0
• One setting moves the platform between levels, and a SCIF-tier deployment selects Level 5 on its own
• Verification dispatches on the algorithm recorded on each artifact, so raising the level never strands anything already written
• Security audit entries are hash-chained and signed, then re-verified nightly against published keys