Console is not a chatbot UI bolted onto a dashboard. It's the execution environment, control plane, and trust layer for autonomous AI agents — from a 10-person team to a regulated enterprise. Deployed on-premises, at the edge, or fully air-gapped.
The Execution Environment for Autonomous AI
Deploy autonomous AI agents with complete data sovereignty. No cloud dependencies. No compromises.
Platform Demo
See the Console in Action
Watch how teams use the Console to orchestrate AI agents, search private knowledge, and stay compliant — all from a single interface
Architecture
Seven Layers. One Deployable.
A monolithic-but-modular architecture: a single deployable that owns the API, the agent runtime, the admin surface, and the data layer.
Multi-Tenant Data Foundation
PostgreSQL (Prisma, 120+ migrations), Neo4j knowledge graph, and Redis — every query org-scoped by construction. Three databases, each doing what it’s built for.
Agent Execution Pipeline
A full state-machine runtime: plan → tool-execute → reflect → budget-check → stream. Cost accounting, HITL gates, PII evaluation, and constitutional constraints baked in.
Context Assembly
7-provider context pipeline (RAG, graph-RAG, documents, customer data, compliance, financials, ontology) with security filtering, re-scoring, and token budget enforcement.
MCP Integration Layer
Model Context Protocol as the universal tool bus. 241+ tools across 14 integrations — discovered at runtime, not hardcoded. HubSpot, Autodesk, Google, and custom connectors.
Learning Loop
RLHF in production. Human feedback → preference pairs → principle extraction → adapter training → atomic promotion with safety dialogs. The system improves from its own operation.
Compliance Automation
20+ regulatory frameworks (HIPAA, CMMC, FedRAMP, SOC 2, NIST 800-171, GDPR, ISO 27001) modeled as code with enforcement handlers, evidence collection, POAM tracking, and SPRS scoring.
Trust & Identity
Three interlocking layers: PBAC (privilege-based access control), SSI (DID-based cryptographic identity for agents and humans), and AWP (hardware device attestation via Ed25519).
Differentiators
What Sets Console Apart
Agent-native runtime
The loop engine, state machine, reflection, and budget tracking are core infrastructure — not a wrapper around an LLM SDK.
Constitutional AI + cost gates
Agents can’t overrun token budgets or bypass PII filters — enforced in the pipeline, not by prompt.
Graph + vector context
Neo4j ontology-aware reasoning on top of vector RAG — richer context than embeddings alone.
RLHF in production
Feedback → preference pairs → adapter training → atomic promotion, all live.
Compliance as code
20+ frameworks with enforcement handlers and evidence collection, not just dashboards.
SSI for agents
Cryptographic decentralized identity for non-human principals — trust infrastructure for the agent economy.
PBAC over RBAC
Privilege-based access control scales to fine-grained multi-tenant scenarios that role identity cannot.
MCP as integration standard
Adopts the emerging protocol standard early — tools and resources are pluggable, not wired.
Device attestation layer
AWP / Ed25519 hardware trust — relevant for regulated industries and sovereign deployments.
What Ships Today
Production capabilities for secure, private, autonomous AI
“Console is built for the assumption that AI agents become autonomous principals in enterprise workflows — not assistants. Every architectural decision is load-bearing for that thesis.”
Enterprise-Grade Security
Built for organizations with the strictest compliance requirements
SOC 2 Type I
Targeted Q2 2026
HIPAA
Pathway in progress
FedRAMP
Pathway in progress
IL4/IL5 Designed
Architecture validated
22 Frameworks
NIST 800-53, CMMC & more
Your AI. Your Infrastructure. Your Rules.
See how the Console delivers secure, compliant AI for your team — whether you're a 10-person shop or a regulated enterprise.