Supply chain decision intelligence · above the stack
Your stack records.
The agents reason.
A2go acts.
The A2go Decision Intelligence Platform (ADIP) works above the systems manufacturers and distributors already run — multiple ERPs, WMS, OMS, planning tools, and the spreadsheets in between. It reads what they record, returns the decision with the reasoning attached, and writes the approved action back.
- ADIP runs above your stack. No data migration and no rip-and-replace. What you do not have to do first
- Supply chain specific AI agents. Purpose-built by decision, organized in four pillars, coordinated across them. Supply Chain AI Agents
- Proven in production. Master production scheduling from 18 hours to 15 minutes. See the results
- Governed before it acts. Approval-ready recommendations, human approval, full audit trail. How it runs
- The Judgment Layer. Decision memory that compounds in your own tenant and is never shared. Where the compounding lands
A component slip breaks next week's build at two plants. Three ways to hold the schedule and the promise.
↑ this is the hand-off that used to take three days and four meetings
Illustrative. Every recommendation carries the trigger, the alternatives, the tradeoffs, and the expected impact.
Reads left to right: the systems you already run record; the four agent pillars reason together into one decision package — ranked options, tradeoffs, expected impact; your planner approves; the approved action writes back into the stack. The dashed line is the write-back returning home.
Supply chain specific, purpose-built
Every agent exists because a supply chain decision was costing someone money.
A2go's agents were not adapted from general-purpose building blocks and pointed at supply chain afterward. Each one starts from a decision manufacturers and distributors actually lose money on — a schedule that will not hold, a promise date at risk, inventory sitting in the wrong place — and each is tied to a measurable financial metric. They fall into four pillars, and they work as one system across them.
Forecasting & Planning
Demand sensing, supplier and distributor forecasting, and full-horizon forecast and planning — so the plan reflects what is actually happening.
Explore the agents →Operational Planning
S&OP and master production scheduling, purchase order excellence, and safety stock — the calls that decide whether next week's build holds.
Explore the agents →Supply & Inventory Optimization
Classification, slow-moving inventory, multi-echelon optimization, and supplier reliability — where working capital quietly accumulates.
Explore the agents →OTIF Optimization
Capable-to-promise, promise jeopardy, and unexpected customer orders — so you see the service problem before your customer calls about it.
Explore the agents →Proven in production
Real, measured results.
Not pilots. Before-and-after results from coordinated agent systems running on infrastructure the customer already owned.
~$500M industrial manufacturer & distributor
18 hours → 15 minutes
master production scheduling
MPS was the entry point, then it expanded agent by agent. The same foundation now carries additional decision domains, with no change to the architecture that carried the first one. Outcomes come from the coordinated agent system, not any single agent.
Governance
Governed before it acts, auditable after.
Every recommendation arrives approval-ready and nothing writes back until a person says so. Judgment stays with your planners — in audited environments that is a requirement, not a compromise.
Human-in-the-loop
Approve, edit, or reject. Agents operate inside the limits your team sets, and a call is promoted to automatic only when you decide it has earned it.
Full audit trail
Action, approver, reasoning, timestamp — logged for every decision, so you can show a year later why the system did what it did.
Lineage and permissions
Governed controls run end to end through Unity Catalog on Databricks, from source system to write-back into your ERP, WMS, and OMS.
Who this is for
Four people usually vet this. Here is what each one gets.
CEO
You funded the AI agenda. You need it in the P&L, not on a roadmap.
The first decision domain goes live in weeks, on the systems you already own. Value shows up while a transformation program would still be in design.
CFO
You are carrying the working capital and you cannot trace how it got there.
Every decision is logged with the action, the approver, and the reasoning — so working capital movements can be traced back to who decided what, and why. No second capital ask to get the value.
COO
Procurement, production, and fulfillment each have a view. None of them agree.
One coordinated view across plants, sites, and systems — including MES and OT signals — so a disruption becomes one decision instead of four meetings.
VP Supply Chain
Your planners spend the week assembling data instead of deciding.
Recommendations arrive approval-ready with tradeoffs shown. Override authority stays with the planner, and what they know stays with your team when they retire.
Fit
Who this is built for — and who it is not.
A2go fits companies that move physical goods: manufacturers and distributors, typically $50M to $10B+ in revenue, usually with multiple ERPs, more than one site, and planning decisions that carry real money.
If you run a single clean system with little planning complexity, or you are looking for something to replace your ERP, we are not the right call. A2go goes deep on supply chain decisions, above your systems of record rather than instead of them.
Start here
See A2go on a decision that costs you money.
Bring one decision your team is losing time or margin on. We will map it end to end with you and show the path to first impact in 90 to 120 days, on the systems you already run.