Evara eliminates the repetitive, time-consuming, and error-prone difficulties that undermine effective forecast management and decision-making. Using actuals, Evara automates forecast management through efficient probabilistic modeling and data inference for decision-making under uncertainty—from investment forecasting and reserve planning to enterprise resource allocation, goal validation, and early ROI prediction for new product lines, and more.

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A better approach to forecast management

Evara’s is a tool, a sophisticated probability engine. It bridges actuals with a user’s expertise, assumptions, and intuition. Built on deep mathematics and decades of research, it serves as a link between human and machine. To minimize current workflow disruption, we prioritize direct integration withExcel, with an estimated 0.5—1.5 billion users [1,2] and the vast majority of professionals using a spreadsheet at least once a day [3].

Evara is like “intelligent what-if analysis or scenario planning”—but it’s more than just generating a few hypothetical scenarios. And if you’re familiar with Monte Carlo simulations, a better analogy might be “automatically, adaptive Monte Carlo simulations from actuals.”

Put simply, Evara helps forecast which outcomes are more or less likely and how choices influence risk and goals. As new actuals become available, the engine automatically handles the forecast management process. In the context of the Excel, this happens across the entire workbook, regardless of the number of cells, rows, columns, sheets, and their complex dependencies. This means earlier interventions, timely goal-driven strategic changes, and the ability to easily flag critical mismatches between assumptions and reality, prompting human intervention when needed.

The technology is grounded in Bayesian inference, where assumptions, beliefs, and expectations systematically adapts to actuals. The means that the user can (and should) make explicit what they think is really possible for every assumption and educated guesstimate (such as a number or cell in Excel). This includes considering—aided by Evara’s UI and APIs—the chance or “likelihood weight” given to those possibilities. Typically, these numbers would come from aggregated data and/or reflect experience, expertise, or what we refer to as “gut feeling.”

Ultimately, this gives us two things:

Assumptions and uncertainties are made clear; easy to discuss; record and track; and stakeholder consensus is needed only once.
1. Additionally, this leads to robust “ground truth” baseline that includes all relevant and approved uncertainty. This means better stakeholder alignment and compliance.
Given new actuals, there is one, and only one, optimal forecast update—a core functionality of Evara’s automation. We explore this concept deeper in ‣

Strategic applications of Bayesian inference

1. Early Validation of Strategic Plans

Test whether an investment thesis or operational turnaround is gaining traction before conventional KPIs would normally reveal it. Detect soft signals in early data and calibrate expectations quickly.

2. New Product or Market Launches

In markets with limited or ambiguous early data, Evara continuously updates market size, demand elasticity, or ramp curves—allowing for earlier adjustments and less exposure to strategic overreach.

3. Actuarial Planning and Insurance Pricing

In settings with limited claims history or tail-heavy risk distributions, Evara supports confidence-weighted premium design and reserve estimation. You can watch a detailed walkthrough below:

https://youtu.be/KgSg9SIcDpA