The economic world model that any product or agent can build on.
Sybilion turns the forces that move a market into confident, defensible decisions, wherever the decision is made. Signal goes in. A decision comes out. You have the workflow you need - either built by you, or us.
Most APIs return a forecast.
Sybilion returns the forces behind it.
A forecast tells you where a number is going. It does not tell you what is moving it, with what lag, or whether to act yet. Sybilion exposes the layer underneath the number (what drives what, how confident the model is, and what is about to move against you). That is the difference between a chart and supporting critical decisions.
drivers
What moves what
Submit a series or its metadata. Get back the ranked external signals (macroeconomic indicators, regional and category dimensions) that move it, with the lag relationships that make them useful before the move, not after.
POST /api/v1/drivers
forecasts
A forecast you can defend
A monthly series in, a point forecast out, with quantile bands, per-driver attribution, and backtest metrics in one call. Every output shows its reasoning, so it survives the room where it gets signed off.
POST /api/v1/forecasts
alerts
What is moving against you
Pass your context. Get back the macroeconomic factors turning relevant to it, before they are obvious. The proactive half of the same model that powers drivers and forecasts.
POST /api/v1/alerts
All three accept optional region and category filters from the catalog. Every claim here maps to an endpoint you can call today. See the full schema in the docs.
One layer. Any system that needs to act on a market.
Sybilion is your decision layer (signal in, decision-relevant output out) which is exactly what lets any system embed it alongside what already exists.
Better inputs to the models you already run
Feed validated drivers and forecast bands straight into your positions and risk models, through the API or webhooks. No product to adopt. No change to the workflow.
Give an agent context a foundation model cannot reach
Which external signals predict the series in front of it, and how confident to be. Your agent executes the task. Sybilion tells it which call to make first. One MCP connector, no plumbing.
Turn a feed into a recommendation
Enrich the data you already sell or surface with the causal context (what drives the number, and when) that moves it from a chart to a decision your users can act on.
Stand up a focused app in an afternoon
The decision logic is the API call. You build the experience around it. The same core functions support a procurement tool, a trading widget, or an agent, without rebuilding the intelligence underneath.
mcp
curl
# Any agent. One connector.
{
"mcpServers": {
"sybilion": { "url": "https://mcp.sybilion.dev/mcp" }
}
}
# Then ask, in plain language:
# "What drives EU polyethylene, and should we commit Q3 volume now?"A model of what drives what. Not a map of who owns what.
Underneath every surface is one thing: a causal and contextual understanding of how economic series move each other, with what lag, and under what conditions. It is, in effect, a living graph of market dynamics. Not a record of who supplies whom (that already exists) but a model of cause, lag, and condition.
That graph is the durable asset. Forecasts, drivers, alerts, and agents are how it is consumed. We stay at this layer rather than owning the workflow you already run, which is what keeps us out of the application trap and lets a finance team, a trading platform, or an AI agent embed us instead of replacing what works.
The graph is the asset. The decision is the outcome. The surfaces are how you consume it.
Built to be embedded.
A small, honest surface area. The same model, reachable however your system already works.
Transparent about what is in the API today: Sybilion supports monthly business time series. Sub-monthly frequencies and instant runtime are on the roadmap, not in the public API yet. We would rather tell you that than have you find out in production.
start building