Our Values

Structured Access to Forecasting Models

Expose time-series foundation models through a standardized interface for agents and LLM systems.

Temporal Reasoning as a Tool

Allow LLMs to call forecasting, simulation, and scenario tools directly within workflows.

Production-Ready Deployment

Secure HTTP endpoints with authentication, built for enterprise environments.

How it works

Agents send structured requests over MCP.
Nolano handles data parsing, model selection, reasoning, and response formatting.

LLM / Agent

Model Context Protocol

Nolano Forecast Models

Structured Outputs

Designed for Real-World Forecasting

Continuous Context

Agents ingest evolving signals like demand shifts, pricing changes, or macro trends.

Tool Awareness

Forecasts can trigger action, from inventory updates to alerts.

Closed-Loop Decisions

Predictions feed directly into systems, enabling automated workflows.

Composable by Design

Modular interfaces let agents plug into different data sources, tools, and workflows without rebuilding pipelines.

Start using Model Context Protocol

Use in Playground

Connect data and run contextual forecasts instantly.

Integrate with your stack

Deploy MCP into your applications with APIs and SDKs.

Start using Model Context Protocol

Build modular AI agents for real-world prediction tasks fully customizable and deployable in minutes.

Build modular AI agents for real-world prediction tasks fully customizable and deployable in minutes.

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