INDUSTRY — DISTRIBUTION & WHOLESALE
Distribution businesses in the $400M–$2B range run on a handful of recurring decisions: who gets constrained product, which DC holds which inventory, and what promise dates the business can actually keep. The A2go Decision Intelligence Platform (ADIP) puts AI agents on those decisions. Your team keeps the final call.
WHERE THE MARGIN LEAKS
A supplier shorts a PO and this week's commitments now exceed what is on hand. Someone decides which customers get filled, which orders slip, and which contracts carry penalty clauses that make the wrong choice expensive. Today that decision happens in a spreadsheet, under time pressure, with tier rules applied from memory.
THE DECISION: who gets filled this week?
Total inventory looks healthy. By distribution center it is not: one building sits on months of a SKU that another building keeps stocking out of. The cash tied up in that mismatch is real, and the transfer-or-hold decision gets revisited too rarely because assembling the DC-level picture takes days.
THE DECISION: what moves, what holds, what marks down?
Customer service commits dates against an availability snapshot that was true last night. By the time the order ships, the same stock has been allocated twice. The result is expedite fees, misses against tiered service commitments, and penalty exposure that surfaces at invoice time.
THE DECISION: what date do we commit on this order?
HOW ADIP COVERS THEM
Monitors committed quantity against available supply. When commitments exceed supply, it drafts an allocation across your customer tiers using the rules you set, and shows the service and penalty consequences of each option before anyone commits.
Watches DC-level position against forecast and flags where working capital is pooling in the wrong building. It proposes specific transfers, holds, or markdowns, quantified in cash terms, on a cadence you choose.
Checks live availability at the moment of commitment, not last night's snapshot. It flags orders promised against stock that is already spoken for and recommends dates that will hold.
Every agent output is a proposal, not an action. Your planners review, adjust, and approve. See how agents act and the judgment layer for how approval works.
WHAT IT MOVES
In SCOR DS terms, this is Order and Fulfill work, with Plan setting the guardrails upstream.
One A2go customer, selling through 17 sales channels to more than 150,000 customers, cut forecast preparation from 28 hours to under one hour and now simulates pricing across 60,000+ SKUs in minutes rather than days. The before-state was analysts pulling data to Excel and circulating stale, static recommendations. Read the published story on Databricks' site, or see more results.
FIT
A $400M–$2B distributor typically runs one ERP and at least one WMS, sometimes several after acquisitions, and does not carry a bench of data engineers. ADIP works from the data those systems already produce. In the same published customer story, 25+ market-intelligence users run scenarios daily without data-engineering support. That is the operating model: your planners and commercial team working the decisions directly, not filing tickets to a data team.
If you own the P&L, start with the CFO view. If you own operations, start with the COO and supply chain view. For how agentic decision intelligence differs from traditional analytics and generative AI tools, see the comparison.
Tell us where allocation, inventory placement, or order promising hurts most, and we will map that decision end to end: who makes it today, on what data, and what an agent-assisted version looks like.
Map your highest-pain decision