Shadow AI
Employees and teams adopt AI tools independently, leaving leadership with no visibility into what's actually being used.
Enterprise AI Intelligence Platform
Enterprise AI.
Enterprise AI.
Smarter. Faster. Measured. Governed. Optimized.
The Enterprise AI Operating System.
Daiahn is the Enterprise AI Intelligence Platform — the AI operating system that helps organizations measure, govern, optimize, and scale AI across people, agents, models, and business operations, combining AI analytics and observability, AI-powered business intelligence, and enterprise AI professional services in one place.
the notebook environment for agent sessions · Daiahn (“Diane”)
The Enterprise AI Problem
AI adoption is outpacing AI visibility.
Enterprises are deploying AI across every team, tool, and workflow — often faster than they can see, measure, or govern it. That gap creates real risk.
Token Waste
Without usage and cost analytics, AI spend scales unpredictably — and no one can explain where the budget is going.
Agent Sprawl
As agents multiply across the business, performance, reliability, and failure modes become impossible to track manually.
Compliance Exposure
Regulated industries need audit trails and governance controls that most AI tools were never built to provide.
Executive Blind Spots
Leadership lacks a single view of AI adoption, performance, and ROI across the organization.
Unproven ROI
Without a link between AI activity and business outcomes, it's difficult to justify — or expand — AI investment.
AgentOS Engineering
Deploy developer agent teams. Multiply your engineering output.
AgentOS Engineering embeds trained developer agent teams directly into your engineering organization — working inside your existing repos, tools, and workflows to ship faster and clear backlog without multiplying headcount. Enterprises running AgentOS teams report a minimum of 5x more engineering output.
- Embedded developer agent teams
- Works in your existing repos
- Backlog and velocity recovery
- Enterprise AI enablement
- Governance from day one
- Senior engineering oversight
Minimum of
5x
More engineering output
The Daiahn Platform
Enterprise AI intelligence, built for every stage.
Whether you're exploring AI on your own or managing thousands of AI users across your organization, Daiahn provides the visibility, intelligence, and governance to deploy AI with confidence.
Agent Intelligence
Measure, monitor, and optimize AI across your organization. Gain complete visibility into AI usage, model performance, token consumption, cost, and business impact through a unified analytics platform built for enterprise AI.
- AI usage analytics
- Token & cost analytics
- Agent performance monitoring
- Prompt intelligence
- Model benchmarking
- Governance & compliance
- Executive dashboards
- Enterprise reporting
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Analyst Studio
Turn fragmented enterprise data into executive-ready intelligence. Analyst Studio connects business information and AI-generated insight to help leaders make faster, smarter decisions.
- Fragmented data unification
- AI-powered business analysis
- Operational intelligence
- Predictive insights
- Decision support
- Executive reporting
- Custom data integrations
- Governance & access controls
BetaApp Store — Coming Soon
TeamConnect to Learn More
EnterpriseConnect to Learn More
Join the waitlist
Industries
Built for regulated, complex, enterprise environments.
Daiahn is designed for organizations where AI oversight isn't optional — where usage has to be explainable, spend has to be attributable, and controls have to hold up to an audit.
- Financial Services
- Healthcare
- Manufacturing
- Professional Services
- Retail
- Government
- Defense
- Technology
Enterprise Ready
Security and control built for the world's most demanding organizations.
On-premise & air-gapped deployment
Run Daiahn entirely inside your perimeter — including fully disconnected environments.
SSO and role-based access control
Integrate with your identity provider and scope who can see which teams, costs, and traces.
Audit-grade event history
A lossless, append-only record of original trace lines — analytics are rebuildable projections.
Local-first, private by default
Nothing leaves your environment unless you explicitly opt in to a feature that requires it.
Multi-model and multi-agent
One view across every model and agent you run, not a single vendor's slice of the picture.
Open, portable output
Sessions and rollups render as standard notebooks — viewable anywhere, with no lock-in.
Why Daiahn
One platform. Not another point tool.
Most tools in this space cover a slice of the problem. Developer-first tracing tools are built for debugging individual LLM calls — not enterprise-wide governance or executive reporting. General observability platforms weren't built for the semantics of prompts, agents, and tokens. And single-vendor consoles only see their own model.
Daiahn unifies AI analytics, agent observability, governance, and business intelligence into one platform — with the professional services to help you operationalize it.
// Deterministic SQL for most analytics: fast, reproducible, auditable, and free per query. LLM inference is reserved for the genuinely fuzzy work.
More about the company →
Outcomes
What enterprise AI intelligence makes possible.
Spend you can explain
Attribute AI cost to teams, workflows, agents, and models — then right-size it with evidence.
Agents you can trust
Catch failures, latency, and regressions before they reach production workflows.
Governance that holds up
Policy, access control, and audit trails that satisfy risk, legal, and compliance review.
ROI you can defend
Board-ready numbers connecting AI activity to time saved and work automated.
Adoption you can steer
See which teams are getting value from AI, which are stuck, and where to invest next.
Shadow AI you can surface
Replace guesswork about unsanctioned tools with an accurate picture of real usage.
Resources
Common questions from AI, data, and executive leaders.
What is enterprise AI analytics?
Enterprise AI analytics is the practice of measuring how AI is used across an organization — including model and agent usage, token consumption, cost, performance, and business impact — so leaders can see what's working, what isn't, and where to invest.
How do enterprises measure AI ROI?
Enterprises measure AI ROI by connecting usage and cost data to business outcomes: time saved, tasks automated, agent reliability, and adoption across teams. A unified analytics layer that ties AI activity to outcomes is what makes ROI measurable rather than anecdotal.
What is AI observability?
AI observability is the ability to monitor models, prompts, and agents in production — tracking performance, errors, latency, cost, and behavior over time — so teams can detect issues, debug failures, and improve reliability.
How do you govern AI across an enterprise?
Enterprise AI governance combines policy, access controls, audit logging, and usage visibility so organizations can enforce responsible AI use, meet compliance requirements, and maintain oversight as adoption scales across teams and business units.
How do you monitor AI agents in production?
Agent monitoring tracks each agent's sessions, tool calls, failures, latency, and cost over time, then aggregates those traces into reliability and performance patterns so teams can see where agents get stuck and fix it.
How can enterprises reduce AI and token costs?
Cost reduction starts with attribution: knowing which teams, workflows, agents, and models consume spend. From there, organizations can right-size models, cut redundant calls, cache repeated work, and set budgets per team.
Can Daiahn be deployed in regulated or air-gapped environments?
Yes. Daiahn supports on-premise and air-gapped deployment with SSO, role-based access control, and audit logging, which is what regulated industries such as financial services, healthcare, government, and defense require.
See it on your own traces
Bring Daiahn to your engineering org.
A founder-led walkthrough against the kind of sessions your teams actually run. Response within one business day.