Wood Wide AI provides an API‑first platform that converts structured tables, logs, and time‑series data into a reusable numeric representation enriched with schema, units, and domain rules. This enables reliable forecasting, anomaly detection, segmentation, and what‑if analysis without building separate machine‑learning pipelines, allowing product and LLM‑driven agents to obtain accurate, adaptive numeric insights through a single API.
Funding
Funding not disclosed
Founders
Product
Problem
Companies that rely on structured tables, logs, and time‑series data often build separate machine‑learning pipelines for each dataset, requiring constant model fitting, retraining, and manual rule implementation. This fragmented approach leads to high maintenance costs, inconsistent predictions, and difficulty integrating numeric insights into LLM‑driven agents or product workflows.
Solution
Wood Wide AI offers an API‑first platform that creates a unified, context‑aware numeric representation of structured and time‑series data. By encoding schema, units, and domain rules into a reusable embedding, the service enables reliable forecasting, anomaly detection, segmentation, and what‑if analysis without developing bespoke models for each new dataset. The platform delivers adaptive, production‑ready outputs that can be called directly by applications or large‑language‑model orchestrators, ensuring consistent and interpretable numeric reasoning as data and business conditions evolve. Integration is achieved through a single API, allowing teams to build once and apply the same numeric intelligence across multiple products and agent workflows.
Target Audience
Primary customers are platform engineering teams, data science groups, and product developers who need to embed reliable numeric analytics into SaaS products, internal tools, or AI agents, especially in industries where decision accuracy is critical.
Features
- API‑first interface that transforms tables, logs, and metrics into reusable numeric embeddings conditioned on schema, units, and domain rules
- Built‑in capabilities for forecasting, anomaly detection, segmentation, and counterfactual (what‑if) analysis without separate model training
- Adaptive reasoning layer that maintains accuracy under distribution shift and changing business contexts, reducing the need for frequent retraining
- Agent‑ready endpoints designed for seamless invocation by LLM orchestrators and autonomous workflows
- Production‑grade outputs that are testable, monitorable, and interpretable for high‑stakes decision making
- Documentation and free trial environment to accelerate integration and evaluation