Continuous, explainable.
Pattern recognition and anomaly detection across accounting + usage data. Tuned on the actual shape of SaaS waste — not generic finance signals. Every finding ships with the data behind it.
✦ The problem
Manual review can't keep up.
The mid-size stack generates thousands of data points a month — invoices, seat changes, usage windows, contract events. No human can hold it in working memory; no spreadsheet was built for it.
- Manual reviews take weeks and are outdated by the time the report reaches decision-makers.
- Reviewers focus on the largest line items while smaller, systemic waste compounds across dozens of tools.
- The interesting patterns only emerge cross-tool — and the cross-tool view requires a dataset no human is curating. Cross-tool patterns include duplicate payments that hide across departments.
- Reactive: cost issues are discovered after they hit the budget, not before.
We're not selling a black box. Every finding lists the rule or model that produced it, the underlying invoices and activity windows, and the assumptions behind the modeled dollar value. You can audit any recommendation before acting on it — that's the contract. This is the engine that powers SaaS cost optimization across the platform.
✦ How Efficyon does it
Baseline. Detect. Recommend.
Three layers, running continuously. The first scan completes in roughly two weeks; from then on, it's monthly cadence.
Baseline your stack
The first two weeks: ingest historical spend, usage, contracts, and org structure. Learn what 'normal' looks like for your company — including seasonality, growth, and departmental variations.
Detect & predict
Continuous comparison of current data against the baseline. Anomalies, pattern matches, and forecasted issues surface in real time. Cross-validation reduces false positives.
Recommend & track
Each finding lands as a prioritized recommendation: modeled dollar value, confidence score, and the implementation step. Outcomes feed back; next month's analysis gets sharper.
✦ What it surfaces
Sample anomaly feed from a typical scan.
Illustrative — categories repeat across stacks; the specific events vary.
Sample / illustrative · explainable findings · audit any one before acting
✦ Get started
Let the engine watch the gap.
Connect one system. The first baseline runs in two weeks; the monthly findings start landing in your dashboard automatically.