Harmony for your value chain.

Swarm is an AI-native planning platform that connects demand forecasting, scenario analysis, and buy planning in one place—so your supply chain team makes faster, better-informed decisions with full visibility into the why.

Preseason and in-season statistical forecasting on your data
What-if scenarios with governed accept/reject workflow
Connect to your warehouse—first forecast in days, not quarters

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Forecast accuracy

91.4%

WMAPE · trailing 12 weeks

Adrenaline GTS 24 +8.7%

Ghost 16 Weatherized +3.2%

Glycerin 21 -1.4%

Why Swarm

Planning software that shows its work

Enterprise planning tools promise AI-powered insights, then hide the model behind a black box and charge seven figures before you see a forecast. Swarm takes the opposite approach.

Others: 12–18 month implementations. Dedicated SI team. Seven-figure contracts before you see a result.

Weeks to first forecast

Connect your data warehouse, map your fields with AI-assisted schema detection, and run your first automated forecast in the same sprint. Built for mid-market and growth-stage supply chains that need results before the next buying window.

Others: Fully autonomous execution with limited human oversight, or rigid approval hierarchies.

Governed scenario workflow

Test any what-if scenario against your baseline—price changes, demand shifts, supply constraints—then accept or reject results with full governance. Changes only reach your buy plan after review. Audit trail of who changed what and when.

Others: Separate tools for preseason planning, in-season forecasting, and buy planning that don't talk to each other.

One platform, end to end

Preseason attribute-driven forecasts, in-season statistical models, scenario testing, and buy plan generation all live in the same system—connected to the same data, governed by the same workflow.

Platform

From raw signal to buy plan

One platform that covers preseason planning, in-season response, and scenario testing—at granularities from top-line class down to SKU × color × size × store × week.

Preseason forecasting

Attribute-driven models for 6–18 month horizons. Handles new product introductions and assortment changes where time-series history doesn't exist yet. Calculates plan revenue, cost, and margin.

In-season demand

Automated statistical forecasts on a schedule or on demand, powered by containerized models on AWS. Dashboard tracks sales vs. forecast with date filtering and WAPE accuracy metrics.

Scenario planning

Run unlimited what-if scenarios against a private copy of your data. Apply price, demand, and supply drivers, then compare impact at the total, item, and period level. Accept the best result into your working plan.

Lead time forecasting

Predict supplier lead times from historical PO data using dedicated forecasting models. Helps inform reorder timing and buffer planning.

Buy plan

Move accepted preseason forecasts into a buy plan table. Share plans with suppliers via secure invite links with email verification.

Governance & audit

Row-level tenant isolation, role-based access, and a scenario audit trail that records every action with user and timestamp. Know who accepted, rejected, or modified a scenario and when.

Intelligence layer

AI that works alongside your team,
not instead of it

Swarm's forecasting engine runs containerized statistical models on AWS, triggered on a schedule or on demand. An AI assistant helps you query your data, and the platform is designed to add specialized agents over time—for market intelligence, pricing signals, and supply monitoring.

Available today

Forecasting Engine

Automated statistical forecasting on ECS Fargate

Data Assistant

Natural-language queries against your data

Scenario Engine

What-if analysis with driver-based overrides

On the roadmap

Market Intel

Competitor moves, tariffs, sector news

Material Pricing

Raw material costs and supplier quotes

Geospatial

Weather, shipping lanes, port delays

How it works

From connection to buy plan

Swarm is designed to deliver your first forecast in days, not quarters.

Connect your data

Point Swarm at your existing data warehouse, cloud storage, or file server. Connect to Postgres, Snowflake, BigQuery, Redshift, MySQL, Databricks, Oracle, SQL Server, SAP HANA, Teradata, Azure Synapse, Amazon S3, or SFTP. Upload Parquet and CSV files. Swarm ingests and normalizes your data so forecasting runs against a clean, versioned copy.

Map and validate

Select your tables, map fields to Swarm's schema, and designate your system of record. The platform analyzes your data structure and runs a statistical health check before any forecast.

Forecast and test

Run automated forecasts on a schedule or on demand. Test scenarios, compare to baseline, review accuracy metrics, and iterate until the numbers make sense for your business.

Accept and execute

When the numbers are right, accept the forecast into your working plan and move it to a buy plan. Share plans with suppliers through secure invite links.

Data integration

Connect your sources.
Swarm handles the rest.

Swarm ingests your data into a managed data lake built on open standards. Automated schedules keep everything current, and your data is never locked into a proprietary format.

PostgreSQL Snowflake Google BigQuery Amazon Redshift MySQL Databricks Oracle SQL Server SAP HANA Teradata Azure Synapse Amazon S3 SFTP

Supported data types

Historical sales time series for demand forecasting

ERP data: product hierarchy, vendor, cost, and margin history

Supplier lead time records from purchase orders

Store and location data for assortment and allocation

Pricing history for simulation and optimization

Promotion calendars with spend and expected lift

Automated ingestion

Set up a connection once and Swarm keeps your data current on a schedule. When your source schema changes, ingestion adapts—no pipeline rebuilds required.

Your data is stored in open formats with no proprietary lock-in. Designate one source as your system of record per data type.