Soma Kiran Gonella
Emerging Technology · Alpha In Development

SigmaGo

Decision Intelligence for Organizations

Organizations already have workflows for making decisions. SigmaGo is being designed to help them understand what those decisions collectively reveal.

The Decision Context Lifecycle
01Policy
02Process
03Approval
04Exception
05Reasoning
06Outcome
07Precedent
Product Philosophy

Moving From Decision Tracking to Decision Intelligence

SigmaGo does not seek to replace existing ERPs, Jira boards, HRIS tools, or communication channels. Instead, it operates as an intelligent contextual layer that captures the relationship between policies, exceptions, approvals, and outcomes.

When an executive approves an exception or a project team changes an architecture, SigmaGo records the reasoning, evaluates the deviation, and builds an indexed organizational memory.

1. Context Capture

Seamlessly prompts decision architects for constraints, hypotheses, and discarded alternatives right at the moment of commitment.

2. Real-time Retrieval

Surfaces relevant historical precedents and outcome audits inside active review meetings, preventing repetitive debate.

3. Adaptive Governance

Monitors exception velocity to alert leadership when static policies have drifted away from operational realities.

The Architectural Engine

The STEP Decision Taxonomy

SigmaGo organizes enterprise choices into four clear layers: Structural boundaries, Process mechanics, Transactional throughput, and Exceptional departures.

Taxonomy

The STEP Decision Hierarchy

Continuous Organizational Telemetry
StructuralProcessTransactionalExceptional (Precedent loop)

Exceptional Decisions

Variable / Critical Trigger Points

Deliberate departures from established rules to resolve unforeseen emergencies, capture strategic opportunities, or solve novel problems.

Real-World Corporate Context
Granting a 35% compensation exception to secure a specialized AI researcher, or waiving Net 30 billing for a key account.
SigmaGo TelemetryDeviation Factor & Precedent Velocity
Organizational Failure RiskDangerously hardens into unmonitored shadow policy if rationale is not systematically governed.
Institutional Telemetry

Measuring What Conventional Dashboards Ignore

Conventional analytics measure activity: how many tickets were closed, how many dollars were billed. SigmaGo measures judgment: how policies hold up under stress.

Risk & Scale

Impact Factor

Calculates the multi-dimensional blast radius of a decision across headcount, capital expenditure, customer exposure, and contractual risk.

Policy Boundaries

Deviation Factor

Measures how far an exceptional approval bends or violates established standard operating procedures, preventing silent shadow precedents.

Outcome Telemetry

Confidence Factor

Scores the historical reliability of similar past decisions by auditing whether past hypotheses held true over 6, 12, and 24-month horizons.

Governance Velocity

Policy Health

Continuously tracks the gap between written policies and live frontline decisions, alerting committees when Policy Drift warrants intentional revision.

Contextual Timeline

Decision History

A chronological, searchable cortex of why past choices were made, who authorized them, and what trade-offs were accepted.

Adaptive Learning

Exception Intelligence

Aggregates recurring operational exceptions across business units to recommend formal structural improvements before friction mounts.

Alpha Pilot Program

Explore a Guided SigmaGo Pilot in Your Organization

We are collaborating with forward-thinking leaders, CHROs, and engineering directors to map decision memory, evaluate policy health, and eliminate decision debt.