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Wood Wide lets your app or AI agent reliably forecast, explain, and reason over structured and time-series data. Designed for environments where data and context shift and adaptive modeling is required, Wood Wide delivers accurate, inspectable, and reliable numeric outputs.

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What is numeric reasoning

Numeric reasoning refers to the ability to model, analyze, and reason over quantitative data such as tables, time-series, metrics, and logs while respecting structure, constraints, and domain context. This includes tasks such as forecasting, anomaly detection, ranking, attribution, and what-if analysis. Unlike natural language reasoning, numeric reasoning must be precise, stable, and verifiable. Small errors compound quickly and can lead to incorrect decisions.

Large language models are optimized for language, not numbers, and are unreliable for quantitative reasoning. Traditional machine learning systems require extensive feature engineering, model training, and ongoing maintenance, making them complex and brittle to use in adaptive workflows.

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What Wood Wide provides

Wood Wide exposes API endpoints that take in structured or time-series data along with lightweight context such as schemas, units, and constraints, and return numeric reasoning outputs. These outputs include forecasts, explanations, anomaly signals, rankings, and scenario analyses. Results are deterministic, inspectable, and designed to be used directly inside applications, services, or AI agents.

Wood Wide abstracts away feature engineering, model selection, training, and retraining. You do not need to manage models or pipelines. You call the API and receive results you can trust.

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How it fits in an AI stack

Wood Wide is a flexible reasoning layer that sits between your data sources and your application or agent logic.

It integrates cleanly with existing data stores, analytics pipelines, and AI systems. Wood Wide is complementary to LLMs, retrieval systems, and orchestration frameworks. Developers typically call Wood Wide from backend services or agent workflows when numeric reasoning is required as part of a larger decision or interaction loop.

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Who it is for

Wood Wide is built for:
  • Developers building AI agents, copilots, or automated workflows
  • Product and platform teams embedding analytics into production systems
  • Companies that need flexible, reliable forecasting and explanations at scale
  • Teams looking to eliminate bespoke ML pipelines for numeric tasks
No machine learning expertise is required to get started.

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Next steps

To begin, follow the Quickstart to connect a dataset and make your first reasoning call.