What Makes Sigma Squared Different?

Most analytics platforms find patterns. We find causes. That distinction is the difference between knowing what happened and what to do next.

The Methodology

One methodology. Any domain.

Most intelligence platforms are built for a single use case. So when your problem doesn't fit the template, the model fails.

Sigma Squared is built differently. The same causal methodology that predicts frontline retention predicts customer churn, and loan performance. We don't retool. We reapply.

One framework. Validated across domains.

Built from your data
— not industry benchmarks.

Our Strengths

Explainable
by design

Most tools give you a score. We give you the reason.

Our models are deterministic and causal — not probabilistic black boxes. Every recommendation can be interrogated, defended, and explained to anyone who needs to ask why: your leadership, your legal team, your community.

If you can't explain it, don't act on it.

Deterministic Algorithms

Transparent Logic

Designed for Auditability

Explainable by design

Most tools give you a score. We give you the reason.

If you can't explain it, don't act on it.

Deterministic Algorithms

Transparent Logic

Designed for Auditability

Built for real-world complexity

Most platforms require clean, structured data before they'll give you anything useful. Real organizations don't have that.
We work with fragmented records, inconsistent inputs, and environments where data governance is still evolving.

That's not a limitation we work around — it's a condition we were built for.

Frontline Workforces

Highly-Regulated Sectors

Real-World Data

Human-led decision making

We don't replace judgment. We give it something to stand on.

Our intelligence surfaces in the tools your teams already use — with context specific enough to act on and reasoning transparent enough to defend. Every recommendation keeps a human in the loop, by design.

Your people make the call. We make sure they're making it with the right information.

Human-In-the-Loop Always

Contextual Guidance

Designed to Inform