Enterprise Intelligence Infrastructure

Rule your enterprise AI.

Build your own enterprise intelligence infrastructure — from governed knowledge to controlled AI execution.

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Trusted by & Partnered with global Leaders

Global AI Leaders

Enterprise & Institutional Partners

01 / What we build

Five capabilities for enterprise AI at scale.

Knowledge, agents, enterprise integrations, runtime visibility and control — designed as one operating infrastructure.

Enterprise KnowledgeTurn business context into AI-readable structure — permissioned and traceable to source.

Agent BuilderGenerate agents from conversation, then make them policy-aware and production-ready.

MCP & IntegrationsRegister, version and control the systems where real enterprise work happens.

Trace IntelligenceCapture every decision, tool call and data flow as a runtime evidence trail.

AI Cost & GovernanceSet policy, route requests, manage access and attribute AI spend across teams.

02 / Architecture

The Enterprise Intelligence Infrastructure

Knowledge, agents, enterprise integrations, runtime visibility and control — designed as one operating infrastructure.

GovernablePolicy enforced in real time.

AccountableEvery decision, fully traceable.

SovereignYour data. Your models. Your control.

Enterprise Intelligence Infrastructure

Enterprise AI Context Core

AssistantBuild your AI work partner.

AgentTurn experience into tools.

CredentialsControl access to knowledge.

Xerno Intelligence

Workspace

Tacit Knowledge

Data Ops

Enterprise AI Ruler

Identity IntegrationControl who can use what they access.

Member

Groups

Tenant

AI Gateway / GuardrailRoute AI requests and enforce safeguards.

Policy

Sandbox

Budget

InfrastructureConnect models, compute, and tools.

MCP Server

Fleet

Resources

MonitorTrack AI usage, costs, and activity.

Dashboard

Runtime

Audit

External Connections

GPU

Model

MCP

03 / ProductXernoAI Context Core

Make enterprise knowledge AI-native.

Connect your data. Empower your agent. Build a smarter organization.

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AI agents can carry out complex tasks from start to finish—not just answer one question. They can plan steps, use tools, track progress, recover from errors, and continue working over longer periods. Example: Research competitors, analyze findings, create a report, and send it to the relevant team.

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04 / ProductXparseAI Ruler

Make every AI action governable.

Every request enters with context, passes one control path, executes only within approved boundaries, and leaves evidence behind.

Records the full path of an AI interaction, including prompts, model calls, retrieved information, tool usage, decisions, outputs, errors, latency, and cost. These traces help teams monitor performance, investigate problems, and improve the system. Example: If an agent produces an incorrect answer, the team can see which source, model, or tool call caused the problem.