Sigma Empowers Exploration. Context OS Delivers Governed Outcomes.
From spreadsheet-native analytics to decision infrastructure, Sigma lets teams explore and collaborate on cloud data quickly. Context OS turns those explorations into governed, auditable decisions that can be executed confidently across the enterprise
Enterprise Foundations
Three Foundations Every Enterprise AI Needs
Every production AI deployment that fails is missing one or more. Context OS delivers all three as architectural primitives — not bolted-on features
Decision Context
Decision-time projections assemble relevant data, signals, and policies to explain why outcomes occur and support confident execution
Sigma explorations structured for decisions
Source-backed data assembled at decision time
Time-scoped projections for each decision
Causal signals, not surface correlations
Context compiled specifically for execution
Outcome: Decisions grounded in real context, not disconnected analytics
Execution Lineage
Every automated decision preserves evidence, reasoning, approvals, and actions in a verifiable execution record
Evidence retrieved directly from Sigma explorations
Assumptions and reasoning steps captured
Policy checks evaluated before execution
Approvals and automated actions recorded
End-to-end trace from trigger to outcome
Outcome: Complete, auditable lineage for every automated enterprise decision
Adaptive Guardrails
Policy constraints enforce safe automation while adapting to changing conditions, exceptions, and defined escalation paths
Permissions define what agents can execute
Constraints evaluated at decision time
Exceptions routed through escalation paths
Policy checks enforce enterprise compliance
Boundaries adjust with operational context
Outcome: Automated decisions operate safely within enterprise governance boundaries
Context OS Architecture
The Five-Layer Decision Infrastructure
Each layer builds on the one below — creating a complete execution environment for enterprise AI agents
Data Build Layer
Connect, normalize, version, secure. Multi-source telemetry from systems of record. Zero-copy architecture — data stays authoritative in source systems
Semantics & Context Layer
Ontology + entity resolution + context compilation + causal graphing. 17 Cs Framework. Decision-time projections — not memory graphs. Converts correlation into causation
Multi-Platform Agent Build Layer
Model and tool agnostic. Four execution primitives (State, Context, Policy, Feedback). Safe action primitives + tool contracts. 60% token cost reduction through context-aware optimization
Observability Layer
Wide-event telemetry for agents + workflows. Complete Decision Trace capture. Drift, latency, cost, failure monitoring. Powers 10–17% quarterly accuracy improvements through ACE
AI Trust & Responsible AI
Policy gates with approval workflows. Audit pack generation. Risk scoring + compliance evidence. Authority verification. Governance as a Gradient: adaptive controls that balance autonomy with accountability
Four Execution Primitives
The atomic units of trustworthy AI execution. Every agent action flows through these primitives.
STATE
Canonical, versioned world state + execution lineage
CONTEXT
Scoped, time-bound projection compiled for reasoning
POLICY
Explicit constraints at decision + commit time
FEEDBACK
Closed-loop signals tied to execution traces
Outcome-as-a-Service
Operational Efficiency Analysis
An operations team uses Sigma to explore warehouse efficiency metrics and identify performance trends. AI agents then optimize staffing, routing, and scheduling while adhering to defined labor policies
With Sigma Computing Alone
Sigma workbooks surface operational metrics and trends, but translating insights into action requires analysts and managers coordinating manually
Metric Exploration
Sigma workbooks display efficiency and operational performance metrics
Manual Opportunity Detection
Analysts review data to identify optimization opportunities
Operational Coordination
Managers translate insights into scheduling and operational changes
With Sigma Computing + Context OS
Context OS transforms Sigma insights into governed operational actions by compiling decision context, enforcing policies, and executing auditable automation
Decision Context Compilation
Context OS assembles decision-grade context from warehouse data
Policy-Governed Automation
Policy Gates enforce labor, scheduling, and operational constraints
Auditable Decision Execution
Agents execute optimizations with traceable Decision Traces
Context Intelligence
Explore data efficiently and understand why outcomes happen — moving from simple metrics to decision-ready insights that teams can act on confidently
Sigma Computing
Sigma provides a familiar spreadsheet interface on live warehouse data, enabling analysts to explore efficiency metrics, performance trends, and operational patterns quickly
However, while Sigma excels at presenting raw data and correlations, it does not compile causal reasoning or decision-grade context, leaving critical “why” questions unanswered for operational decisions
ElixirData Context OS
Context Graphs compile decision-time projections from Sigma explorations, capturing causal signals and patterns that spreadsheets cannot model
These projections are scoped, time-bound, permissioned, and source-backed, transforming insights into actionable context that supports confident, operationally relevant decision-making
Decision Governance
Ensure AI actions follow policies and constraints while remaining auditable and aligned with enterprise standards, protecting both compliance and operational integrity
Sigma Computing
Sigma uses workspace and data access controls to manage collaboration and secure sensitive data, making it effective for team-level governance
However, it does not provide a framework for governing AI agent actions, enforcing operational policies, or ensuring that automated decisions comply with enterprise rules
ElixirData Context OS
Policy Gates enforce constraints at both decision time and commit time, enabling business users to trigger AI actions safely within defined boundaries
Governance includes dual-gate checks, exception handling, escalation paths, and accountability, ensuring all AI-driven decisions are compliant, auditable, and aligned with organizational policies
Execution & Outcomes
Turn insights into measurable, operational outcomes, optimize resource usage, and continuously improve agent-driven processes across the enterprise
Sigma Computing
Sigma connects quickly to cloud warehouses and provides per-user licensing, enabling fast setup for BI teams and exploratory analysis
However, it cannot preserve reasoning, optimize execution, or provide a closed-loop improvement mechanism for AI-driven decisions, leaving opportunities for operational gains untracked
ElixirData Context OS
Decision Traces preserve complete reasoning from evidence through policy checks, approvals, actions, and results, creating a fully auditable workflow
Deployment alongside Sigma reduces AI operational costs, supports continuous improvement, and delivers measurable quarterly gains as agents learn and optimize from real-world execution
Platform Comparison
Sigma Computing vs. ElixirData Context OS
Side-by-side: what each platform delivers and where decision infrastructure makes the difference
| Dimension | Sigma Computing | ElixirData Context OS |
|---|---|---|
| Category | Spreadsheet-native cloud analytics | Decision Infrastructure for Agentic Enterprises |
| Where It Sits | Exploration layer — familiar spreadsheet UX | Deterministic execution layer — governed outcomes from exploration |
| AI Capability | Formula-based spreadsheet analysis | Bounded, auditable autonomy: policy, authority, evidence — before AI executes |
| Understanding | Spreadsheet on live warehouse data | Context Graphs: causal reasoning beyond spreadsheet capabilities |
| Governance | Workspace + data access controls | Dual-gate policy enforcement at decision time AND commit time |
| Accountability | Collaboration history | Decision Traces: evidence → policy → approval → action → result |
| Autonomy | No agent autonomy — manual exploration | Governance as a Gradient — bounded, auditable execution |
| Value Model | Per-user licensing | Outcome-as-a-Service: outcome economics, not seat count |
| Improvement | Static analysis | Closed-loop ACE: 10–17% quarterly gains from real work |
| Deployment | Fast warehouse connection | 4-week enterprise deployment alongside Sigma |
| Agent Support | No agent framework | Model and tool agnostic — works across LLMs, vendors, frameworks |
Category
Where It Sits
AI Capability
Understanding
Governance
Accountability
Autonomy
Value Model
Improvement
Deployment
Agent Support
Capability Matrix
Decision Infrastructure Capabilities
Context OS extends Sigma with governed execution, causal reasoning, and auditable AI, turning insights into trusted, outcome-driven decisions
| Capability | Context OS | ElixirData Detail | Sigma Computing | Sigma Computing Detail |
|---|---|---|---|---|
| ✔ | Policy Gates at decision + commit time | ✕ | No execution governance | |
| ✔ | Evidence → policy → approval → action → result | ⚠ | Collaboration history | |
| ✔ | Causal reasoning beyond spreadsheets | ⚠ | Spreadsheet-level analysis | |
| ✔ | Governance as a Gradient — auditable | ✕ | No agent framework | |
| ✔ | Governed outcomes from explorations | ✕ | Workbook sharing only | |
| ✔ | ACE: 10–17% quarterly gains | ✕ | Static analysis | |
| ✔ | Alongside Sigma exploration | ✔ | Fast warehouse connection | |
| ✔ | Outcome economics | ⚠ | Per-user licensing | |
| ✔ | Works across LLMs, vendors, frameworks | ✕ | No agent framework | |
| ✔ | Governed actions via NL | ✔ | Spreadsheet-native (familiar) | |
| ⚠ | Decision audit collaboration | ✔ | Live multi-user editing |
Honest Assessment
When Each Platform Shines
When Sigma Computing drives exploration and Context OS enables governed execution, organizations gain both insights and trusted outcomes
When Sigma Computing Shines
Sigma provides spreadsheet-native cloud analytics, fast insights, and collaborative exploration for business users
Familiar interface for business users
Direct connection to cloud warehouses
Multi-user editing and sharing
Quick time-to-analysis
Outcome: Enables efficient exploration and rapid decision-making
When Context OS Wins
Context OS empowers AI agents with policy enforcement, causal reasoning, and continuous improvement for real outcomes
Context Graphs beyond spreadsheet analysis
Dual-gate checks for AI actions
Preserves reasoning for auditability
60% lower operational cost
Outcome: Turns insights into trusted, optimized business decisions
Decision Infrastructure for Your Sigma Computing Investment
Policy, authority, and evidence — before AI executes. See how Outcome-as-a-Service delivers governed decisions on your Sigma Computing data