A composable analytics platform for supply chain. Forecast demand, simulate inventory, optimize networks, segment your catalog — your team builds the analysis on a visual canvas, then shares it as apps every planner can run. No code. No black box.
Your best people already re-forecast before every S&OP meeting, re-segment the catalog, simulate an inventory policy, test a network change against a disruption. But they do it in tools that were never built for it — so the decision gets made late, on numbers nobody fully trusts, with logic nobody can see.
Set the parameters once, run them for a year. Then demand swings, lead times move, and the formulas can't keep up.
Powerful and rigid. A change is a change request. The decision waits on a roadmap written in a vacuum.
Fast, iterative, analytical — and built so the people closest to the problem can do the work themselves.
Your team drags analytics nodes onto a canvas, connects them into a pipeline, and runs it — turning raw history into clean forecasts, inventory simulations, network plans, and answers a planner can act on. Connect your data. Compose the analysis. Run it and read it.
Drag data in. Wire up a forecast. Add segmentation. Layer in inventory logic. Every node is a real supply chain method — forecasters, segmentation, inventory simulation, routing — and every connection is typed so your workflow can't break silently.
Transformations run server-side on a columnar engine; heavier algorithms run in isolated compute. A workflow that's correct on a thousand rows is still correct, and fast, on tens of millions. You watch it run live — each node lights up as it executes.
Anyone on your team can build a workflow on the canvas — then publish it as an app: a clean, guided interface with just the inputs that matter. Planners across teams, sites, and geographies run the same analysis on their own data, without touching the logic underneath.
Every node is a real method — forecasting, segmentation, inventory, network optimization, reconciliation — ready to drag in and run. No notebooks. No model from scratch. No PhD on the payroll. 70+ nodes today, growing every month.
Boron isn't built for a single workflow. It's built for the analysis behind every major supply chain call — demand, inventory, network, and the strategic questions in between.
Profile demand, clean and unconstrain it, then run a tournament of statistical, ML, and intermittent-demand models. The champion selector picks the best model per SKU on backtested accuracy.
Simulate policies against real demand and lead-time uncertainty before you commit. See the trade-off between service level and holding cost explicitly. Roll it out as an app per category or region.
Bring geocoding, distance matrices, routing, and map visualization onto the same canvas as your forecasts. Ask where a facility should sit, what a lane disruption costs, how flows should shift.
Run ABC, XYZ, ABC-XYZ, and lifecycle segmentation in a single pass. See concentration risk, find the SKUs driving the business, flag the ones that should run on autopilot — or be retired.
Build the review as a workflow once, publish it as an app, and run it every cycle on fresh data. Bring real scenarios — growth, disruption, recession — side by side on the KPIs that matter.
Run workflows on a schedule, watch KPIs against thresholds, and surface the exceptions that need a human — a forecast drifting, a stockout risk building, a lane slipping. A control tower from parts you can see.
Plenty of tools will give you a number. The harder question a leader has to answer: can I stand behind it in the room? A black box gives you an answer and asks for faith. Boron gives you an answer and shows its work.
Every transformation, model, simulation, and override is on the canvas. No hidden steps.
Follow any number back to the data it came from. The forecast shows its work.
Your logic, your data, your workflows. Exportable, not locked in.
A supply chain decision has to be the same every cycle, traceable, and made by someone who can defend it. Here's how the options stack up against that bar.
You can hand your data to an AI agent and ask for a forecast today. Ask again tomorrow and you may get a different one. That's the nature of probabilistic tools — and it's exactly wrong for supply chain, where the same inputs must produce the same decision, every cycle, in a way you can audit. Boron is deterministic by design. It does use AI — to suggest the next node, explain a result, and accelerate the build — but the AI advises; your team decides. Boron augments your people. It never asks you to take an unexplainable number on faith.
A capability you bring to your team. Run the advanced analysis that used to need a data science function — and make network, inventory, and scenario calls on analysis built in the open.
The analysis you redo every period becomes an app you run in minutes — freeing your time for judgment, not janitor work.
Geocoding, distance matrices, routing, and network simulation on the same canvas as the forecasts that drive them.
The first tool that matches how you think — compose the method, run it, ship it to the team, get your work out of a file only you understand.
One-off analytics requests leave your queue. The business gets a governed, auditable, version-controlled place to build.
Codify your methods into durable, reusable workflows you deploy to every client — instead of rebuilding the same model each engagement.
Boron is in early access. We're working hand-in-hand with a small number of supply chain teams to build the platform around real planning problems — not a roadmap written in a vacuum.
You get unusual influence over what we build, and direct access to the people building it. We'd rather earn a small number of teams who genuinely rely on Boron than chase logos we can't stand behind.
Explore the co-build program →See Boron on your own data, or just see how it works. No procurement marathon required.