Probability is treated as a first-class product.
LEMs produces outcome distributions designed to be evaluated, improved, and converted into expected-value decisions—not simply percentages that sound plausible in a response.
LARGE EVENT MODELS / CONSTRAINED FORECASTS
Make the next
business move
worth more.
LEMs helps teams optimize millions of recurring business decisions—pricing, inventory, routing, underwriting, sales actions, and capital allocation—so meaningful improvements, multiplied across the business, can compound into millions or billions of dollars of revenue and value. It also brings the same discipline to rare, high-impact decisions where a single choice matters.
MODELConstrained forecast distribution
DECISIONOutcome probability × business value
RECORDAuditable inputs and choices
FROM FORECAST TO ACTION
Don’t just predict.
Optimize.
Define the actions that are actually available: price a deal, allocate inventory, approve a credit limit, select a sales motion, or decide when to invest. For every action, assign the business value of each possible outcome. LEMs forecasts the outcome distribution and calculates expected value for every action.
Try the Decision Studio →- 01Define outcomesBound the states that matter and establish the information cutoff.
- 02Attach valueState the profit, loss, savings, or strategic value of every action–outcome pair.
- 03Compare expected valueProbability × value reveals which action earns the most under the available evidence.
PROBABILITY IS THE PRODUCT
Large Event Models (LLMs) cannot learn how to predict accurate probabilities of events. LEMs will with sufficient training data.
General-purpose AI can write a percentage, but ordinary next-token training does not directly train that articulated number to match the event frequency that follows. When the data consists of context and eventual-outcome tuples, its objective rewards emitting the next token—not a counterfactual probability label. That alone does not make a percentage decision-grade: it may not be tied to a complete outcome set, available evidence, a known information cutoff, or a result that can be measured against what happens next.
Train a custom model →Built to adapt rapidly.
LEMs combines proprietary probability architecture with event-specific learning, enabling rapid adaptation to specialized business data, recurring decision patterns, and newly resolved outcomes.
Better probabilities should prove themselves.
Forecast quality can be measured against resolved events, refined with feedback, and connected directly to the value of the actions a business chooses.
ECONOMICS OF BETTER DECISIONS
Better decisions, multiplied
into consequential value.
In large organizations, material improvements to high-volume business actions can be worth millions of dollars. Across global markets, supply chains, pricing systems, credit books, and capital-allocation programs, powerful improvements multiplied across millions of decisions can create hundreds of millions—or billions—of dollars in annual value.
Annual value can be at stake when a large enterprise improves price, discount, conversion, churn, or mix decisions across thousands of transactions.
For banks, insurers, asset managers, and market participants, better underwriting, execution, inventory, and risk decisions can influence nine- and ten-figure portfolios.
At the largest businesses, better supply, procurement, capacity, and investment allocation decisions can move billions of dollars of enterprise value.
Illustrative scale ranges, not promises of results. Real value depends on decision volume, economics, constraints, implementation, and measurement discipline.
Finite by design
Every answer is constrained to a user-defined categorical or numeric outcome set.
Time attached
Evidence records when it happened and when it became knowable, protecting the forecast cutoff.
Yours to inspect
Keep a searchable record of runs, inputs, permitted outcomes, probabilities, and action values.