autonomOS
The Operating System for the Intelligent Enterprise
Leading the paradigm shift from rigid legacy software to unified, natural language experiences that enable truly autonomous operations at enterprise scale.
The Crisis: Digital Chaos in the Modern Enterprise
Disconnected Systems
Large enterprises operate 275-370 applications on average, and over 1,000 including Shadow IT.
This is massive operational fragmentation.
This is massive operational fragmentation.
Slow Adaptation
Legacy software cannot support the agentic AI revolution.
Organizations remain "jailed" by decades-old systems that were never designed for intelligent automation.
Manual Orchestration
Teams waste countless hours on "swivel chair" work—manually moving data between systems, reconciling information, and executing repetitive processes.
The result? Organizations are data-rich but action-poor, creating a massive "Insight-to-Action Gap" that paralyzes decision-making and execution.
The Scale of the Problem
1,000+
Average Applications
Large enterprises manage between 275-370 applications, with some studies reporting up to 976 when including Shadow IT
$2.6T
Annual Legacy Spend
Enterprises spend $2.6 trillion annually just keeping legacy systems running—the cost of inaction
16 mos
Modernization Time
Average application modernization takes 16 months and costs $1.5M per project
The True Cost of Software
Initial purchase is just the beginning
The software license or development cost represents only a fraction of total lifetime expense. Industry data reveals a stark reality:
- On-premise software: Annual maintenance fees of 16-25% of license cost
- Total IT budget: 55-80% spent on maintenance and operations
- TCO over lifespan: Maintenance accounts for 50-80% of total cost
Rule of thumb: For every $1 spent acquiring software, budget $3-4 to maintain it over its lifespan.
The Foundation of Complexity: Application Sprawl and Shadow IT
Application Sprawl by Size:
- Small Business: Estimates range from an average of 42 SaaS applications (Backlinko, 2025) to 253 (DemandSage, 2025).
- Medium Enterprise: Estimates range from 103 (Backlinko, 2025) to 335 (DemandSage, 2025).
- Large Enterprise: Estimates range from 158 (Backlinko, 2025) to 473 (DemandSage, 2025). In very large organizations, the total application count (including custom and on-prem) often exceeds 1,000.
The Shadow IT Factor:
- Decentralized Control: IT departments now manage only 26% of SaaS spending, with business units controlling the majority (70%). (Source: Zylo, 2025)
- Lack of Oversight: An estimated 85-90% of SaaS applications operate outside of IT oversight and control. (Source: Grip Security, 2024)
- Future Trend: By 2027, it is predicted that 75% of employees will acquire, modify, or create technology outside of IT's visibility. (Source: Gartner, cited by Xensam, 2025)
The Legacy System Burden
$2.6T
Global annual spend
To maintain legacy systems
$1.5M
Average modernization
Cost per application project
$361K
Technical debt
Per 100,000 lines of code
Modernizing legacy applications requires substantial investment in both money and time, with average projects taking 16 months to complete. The cost of not modernizing—technical debt and operational inefficiency—is even higher.
The New Challenge: Agent Chaos
Simple Agents ≠ Autonomous Action
The barrier to creating AI agents has collapsed. Every vendor now offers agent-building tools. But this has created a new layer of chaos: unmanaged "Agent Sprawl."
Uncoordinated Execution
Agents operating in silos without shared context
Governance Risks
No centralized control or audit trails
Increased Brittleness
More agents on the same failing infrastructure
The Market Need
Enterprises need an Operating System to orchestrate agent proliferation, provide unified context, and ensure reliable autonomy.
This Unsustainable Paradigm is Leading to the Fundamental Market Shift: From Software to Intelligence
The Legacy Model: Software Navigation (Obsolete)
- Users must navigate rigid, disconnected applications (ERP, CRM, etc.).
- Operations require manual orchestration and specialized technical expertise.
- Siloed systems create complexity and limit operational visibility.
- Value is trapped within the applications.
The New Reality: Autonomous Orchestration (Emerging)
- Users engage directly with data through natural language (Intent-driven).
- Operations are managed autonomously by multi-agent AI systems.
- A unified interface abstracts away underlying technical complexity.
- The stack is commoditized; value shifts to the intelligence layer.
The enterprise is transitioning from a software-centric model to an intelligence-centric model. This shift demands a new operating system.
Introducing autonomOS
The Operating System for the Intelligent Enterprise
autonomOS abstracts complexity from disparate enterprise systems to enable intent-driven operations. We are the category-defining Operating System that orchestrates operations through natural language, closing the gap from insight to autonomous execution.
Two Perspectives, One Platform
For Business Users
- Intent-Driven Operations: Describe what you want; we execute the how
- Insight-to-Action: Analytics that prescribe and execute optimal actions
- Autonomous Processes: Continuous optimization of business functions
For Technology Leaders
- Light, Fast Connectivity: Connect any data source without complex ETL
- Freedom from Legacy Jail: Agnostic orchestration across all systems
- Security by Design: No data storage—we process metadata only
autonomOS Platform (AOS) Components
Natural Language Query (NLQ)
Transform business intelligence into natural conversation. NLQ is a breakthrough conversational interface that eliminates the complexity of data analysis, empowering users to ask business questions in plain English and receive immediate, intelligent insights. It interprets user intent, presenting not only a direct answer, but also ancillary information in an easy-to-digest format.
Discover (AOD)
AOD autonomously fingerprints and catalogs your entire technology environment—spanning 100s of apps, DBs, and tools. It rapidly infers relationships and establishes connections without manual configuration (minimal HITL). This engine creates a complete, secure architectural view using metadata only, forming the foundation for autonomous orchestration.
Adaptive API Mesh (AAM)
Self-healing integration layer that monitors API health, detects schema changes, and autonomously adapts—eliminating the primary cause of automation failure.
Data Connectivity Layer (DCL)
Unified enterprise ontology mapping disparate sources into a coherent knowledge graph. Provides the contextual "brain" for intelligent decision-making.
Agentic Orchestration Architecture (AOA)
Governance engine managing agent proliferation at scale. Coordinates workflows with audit trails, HITL mechanisms, and observability for trusted autonomy.
NLQ - Natural Language Query Engine
Transform business intelligence into natural conversation. NLQ is a breakthrough conversational interface that eliminates the complexity of data analysis, empowering users to ask business questions in plain English and receive immediate, intelligent insights.
Instead of wrestling with SQL queries or navigating labyrinthine dashboards, simply type questions like "what's the margin?" or "how's pipeline looking?" and get instant answers. NLQ bridges the gap between human curiosity and data-driven decision making.
Powerful Capabilities That Make Data Accessible
Natural Language Understanding
NLQ's intelligent engine interprets casual business questions with remarkable accuracy. Whether you ask "churn?", "are we profitable", or "show me Q3 performance", the system understands context and intent without requiring precise technical terminology.
The platform supports questions across multiple business domains including Finance, Sales, Operations, and HR, making it a universal tool for enterprise-wide insights.
Dual Visualization Modes
Galaxy View
Interactive node-based visualization showing primary answers with semantically related metrics. Color-coded confidence indicators (green, yellow, red) reveal data reliability at a glance.
Text View
Structured responses with values, units, time periods, and confidence scores. Includes parsed intent for complete transparency into how your question was interpreted.
AOS Discover (AOD)
Autonomous Asset Fingerprinting
Agentless, multi-protocol scanning across the entire enterprise landscape (cloud, hybrid, and on-premise). Rapidly identifies and fingerprints applications, databases, APIs, and infrastructure components, eliminating shadow IT without manual configuration.
Dynamic Inventory & Catalog
Builds a centralized, continuously updated source-of-truth catalog for all digital assets. Automatically deduplicates entries, infers ownership, and enriches asset profiles with comprehensive infrastructure and network metadata.
Intelligent Dependency Mapping
Analyzes relationships and metadata to automatically map interdependencies and data flows between disparate systems. Creates a "digital twin" of the architecture and prioritizes integration opportunities with minimal human intervention (HITL).
Zero-Trust Discovery Model
Security by design. AOD operates strictly on configuration and network metadata; sensitive business data is never read or stored. Adheres to least-privilege access, ensuring comprehensive discovery with zero data exposure.
In Practice
How AutonomOS Connects
AutonomOS does not connect to every application.
Instead:
01
Connect once to the integration fabric
It connects once to the integration fabric (e.g., MuleSoft)
02
Discover existing resources
It discovers what APIs, flows, topics, and sinks already exist
03
Consume existing outputs
It consumes existing outputs:
- Mulesoft, Workato etc. System APIs
- API Gateways (Kong, etc)
- Kafka / Event Hub topics
- Snowflake tables / streams
04
Unify and govern
It unifies and governs this data in DCL
MuleSoft keeps the keys. AutonomOS consumes the results.
AAM Connectivity Modalities
01
Control-Plane Attachment
Read-only visibility into APIs, integrations, ownership, environments
02
Declared Interface Consumption
Mulesoft System APIs or enterprise-approved APIs
03
Passive Subscription to Existing Sinks
Kafka topics, Event Hub, Snowflake tables/streams
04
Minimal Tee (Explicit Enablement)
One additional sink added to an existing integration flow
No other modalities scale in enterprises.
Data Platform
DCL Engine: Unified Data Intelligence
DCL answers one question: 'What does this field mean to the business?' It's a semantic translator that takes cryptic field names (KUNNR, acct_id, cust_rev_ytd), maps them to business concepts (Account, Revenue), and shows who in the business uses each concept.
Cryptic Fields
Raw data fields from source systems.
Business Concepts
Mapped to meaningful business terms.
Persona Views
Relevant insights for specific roles.
Auto-Discovery
Finds schemas across source systems automatically.
AI-Powered Mapping
Near-perfect accuracy matching fields to business concepts.
Real-Time Visualization
Interactive Sankey diagram shows data flow instantly.
Persona Views
CFO, CRO, COO, CTO see what matters to them.
Intelligent Learning
DCL learns from every mapping decision:
- Low confidence? AI validates
- GL_ACCOUNT → "general_ledger"
- MRR → "revenue" ✓
Continuous learning improves accuracy over time
Zero-Trust Security
DCL never stores your data. Ever.
Stores: Schema metadata, mapping decisions, pointers
Never stores: Row data, customer records, actual payloads
Prebuilt Domain Agents
RevOps Agent
Optimizes revenue operations by analyzing pipeline health, identifying conversion opportunities, and automating deal routing. Syncs sales, marketing, and customer success data for unified visibility.
FinOps Agent
Monitors spending, forecasts budgets, and recommends cost optimizations. Automates invoice processing, expense approvals, and financial reporting workflows across systems.
HROps Agent
Streamlines recruiting, onboarding, and performance management. Analyzes employee data to predict retention risks and suggest engagement improvements.
CXOps Agent
Enhances customer experience through sentiment analysis, support ticket prioritization, and proactive issue resolution. Coordinates responses across support channels.
Custom Agents
Build specialized agents tailored to your unique business processes. Leverage our agent framework to codify domain expertise and automate complex workflows.
From Insights to Automated Actions
Live Dashboards
Real-time visualization of key metrics across all connected systems. Customizable views for different roles and teams.
AI Recommendations
Contextual suggestions based on pattern recognition and predictive analytics. Learn from outcomes to improve over time.
Workflow Execution
Trigger actions automatically based on conditions and business rules. No-code workflow builder for business users.
Decisions Deployed
Changes propagate instantly across all connected systems. Complete audit trail of what changed, why, and when.
Why 95% of AI Projects Fail to Achieve ROI
95%
Technology-First Failure
Projects that ignore business processes and start with tech selection
85%
Data Quality Issues
Derailed by poor or irrelevant data that teaches models the wrong lessons
40%
Last-Mile Abandonment
Great technology fails when adoption and integration aren't owned
Our Approach: Operating in the 5%
Business-Process First
We map every solution to your operating reality and KPIs before touching a model. Strategy drives technology, not the reverse.
Domain Expertise
Our process experts validate, cleanse, and contextualize data so models learn the right lessons from day one—eliminating bias and brittleness.
Last-Mile Engineering
Field Deployment Engineers (FDEs) drive adoption through user training, workflow integration, and measurable change management.
This integrated approach—Technology + Expertise—ensures we deliver the "last mile" of implementation with measurable results.
What This Means for You
Clarity
Direct linkage from initiative to KPI to model behavior to measurable outcomes. No black boxes.
Confidence
Higher-fidelity data pipelines with guardrails that reduce bias and brittleness from the start.
Trust & Adoption
People actually use the solution because it fits how work gets done—not the other way around.
Don't Worry About Your Messy Data
We Connect. We Contextualize. We Execute.
autonomOS is purpose-built to thrive in the reality of enterprise technology: legacy systems, data silos, and constant change. Our light, fast, secure connectivity—combined with unified intelligence and massive orchestration—enables outcome-based automation at scale.
Ready to close your Insight-to-Action Gap? Let's build the intelligent enterprise together.
autonomOS: From Insight to Action, Instantly
We connect your enterprise's brain to its hands, creating a system that doesn't just know, but does.
01
Connect to Anything:
Our universal Data Connectivity Layer (DCL) plugs into any of your systems in real-time. We don't care if it's a modern cloud app, a legacy ERP, or a 30-year-old mainframe.
02
Forget Legacy Headaches:
You do not need to rip-and-replace or spend millions refactoring old systems. Our platform acts as a universal translator, abstracting away the complexity so you can focus on the outcome.
03
Reason Intelligently:
Domain-specific AI agents analyze this live data, understand your business context, and recommend specific, high-value actions.
04
Act Autonomously:
With your approval (or fully autonomously), the system executes those actions across your applications, closing the loop from insight to outcome.