The data arrived on time. The decision didn’t.
Your ERP, planning system and warehouse each hold part of the answer. Putting them together still takes meetings, spreadsheets and days you don’t have. ADIP — the A2go Decision Intelligence Platform — coordinates the decision across all of them, on the systems you already run.
Clear to Build. A work order releases Friday. One BOM line out of 214 is short, and a $438,000 shipment is at risk. Equivalent stock exists under another customer’s part number — if the ontology can prove it. Open the worked decision → Opens in a new tab, so this page stays where it is. Worked example — the customers, SKUs and penalties are synthetic; the mechanics are the product’s.
Databricks published how a coordinated A2go agent system took a regional demand and pricing forecast cycle from 28 hours to under one hour, running on the customer’s own lakehouse with no migration. The wider coordinated agent system at this large global protein processor increased profitability by $200 million in Year 1. Read the A2go–Databricks story →
Your data, made AI-ready where it already lives.
A2go reads from the systems you already run and reconciles the context required for a decision inside your environment.
Snowflake, Azure Synapse, AWS Redshift, BigQuery, Cloudera, Delta, Iceberg, a data fabric, or a mix accumulated across acquisitions — ADIP draws from it through standard governed interfaces. Your existing investment stays in place and gets more valuable: static stores become live, agent-ready feeds instead of extracts somebody refreshes on a schedule. It also doesn’t wait for a finished master-data program — it starts delivering in weeks.
A supplier confirms a nine-day slip on a resin lot.
ERP · open POMES · the runSupplier feedEach system holds one piece. Neither has raised anything.
Coordinated, not centralized.
Specialized agents reason together across that context and recommend an action with the reasoning, constraints and business impact attached.
Each agent is purpose-built for one decision and tied to a measurable financial metric, and they orchestrate continuously on the same governed data — demand reaching planning, planning reaching inventory and order promising, without waiting for a meeting. When inventory and service pull against each other, the Judgment Layer decides how the conflict is weighed.
What triggered it and when · the alternatives that were considered · the constraint that applied · the expected financial impact of each option · a ranking, and the tradeoff behind it. Approval-ready, not another alert.
Three options costed against the schedule that will actually run.
Ranked against your own allocation rules, not a generic default.
A person approves. Then it writes back.
Routine decisions inside limits you define can run automatically. Everything else arrives in front of the person who owns the call, with the projected outcome attached, while the order can still be re-promised, the shipment re-routed, the buy adjusted.
Your ERP remains the ERP. ADIP does not become a second place where the truth lives.
Every deployment defines three things before an agent runs: which decisions may be automated, which must be approved, and what the agent may never do regardless of confidence. Those limits are configuration, not a policy document. Every decision is logged with who approved it, when and why, and the log is exportable for audit.
Approve
Edit or reject writes back, with the reason it changed.
The Judgment Layer.
Every recommendation, approval, override, action and outcome contributes to your company’s decision memory, held in your environment and owned by you.
Your best planner knows which customer you protect when two orders compete, which supplier’s lead time to distrust in August, and when the rule gets broken. None of that is in your ERP. It is in people.
Your Judgment Layer carries your company’s own decision logic — the approvals, the rejections and the reasoning behind them. It is never shared across customers and never used to train a model any other customer touches. Without it, AI is automation. With it, expertise compounds instead of retiring.
The approved action was written back to:
And the reasoning was kept. This account is now protected against this supplier in this season, so the next call starts from what your team already decided.
Stored in your environment · used only to improve your decisions · never shared across customers
Numbers from live deployments, not a projection.
Regional demand and pricing forecast cycle at a large global protein processor — 65 plants, 17 sales channels, 150,000+ customers.
Published by DatabricksProfitability increase in Year 1 at the same processor, from the coordinated agent system across demand, production, S&OP and pricing — 60,000+ SKUs simulated in minutes.
A2go–Databricks story →Master production scheduling at a ~$500M industrial manufacturer and distributor. 24 spreadsheets replaced by one coordinated run.
Customer deploymentSee how each was measured Find the operation that looks like yours
Why agentic AI.
Three things are sold as AI. Only one of them makes a decision, and that difference is the whole argument.
“We already have AI.”
Probably true. Almost certainly not this. Three different things are sold under one word, and only one of them closes a decision.
It predicts
Statistical forecasting, demand sensing, anomaly detection. Often already inside your ERP or planning tool. Produces a number.
It explains
Chat assistants and productivity AI. Summarize the report, draft the email, answer a question about the data. Produces language.
It decides and acts
Reasons across data, rules, constraints and tradeoffs from several systems at once, and returns a ranked, costed recommendation ready to approve.
The full comparison, with what each one cannot do See a decision closed end to end
Mike Romeri
CEO & Founder
- 40 years in supply chain
- 20+ years Partner, PRTM (now PwC)
- Co-founded OPS Rules with MIT’s David Simchi-Levi — acquired by Accenture
Cesar Oliveira
Chief Operating Officer
- 20+ years ERP transformation
- 25+ enterprise go-lives: SAP, Oracle, Epicor
- Architect of ADIP
Stephen Hutson
CTO & Co-Founder
- 9 years Principal, PwC-PRTM
- Nominated Distinguished Engineer, IBM
- Ran Watson AI & Commerce Analytics, APAC
A2go
Founded 2017
- Built natively on Databricks
- Governed through Unity Catalog
- Delivery team in the US and Brazil
Bring the decision that costs you the most.
Not a platform evaluation. One 30-minute session on where a single decision stalls across your systems, what it is worth, and what a first deployment against it would look like. You leave with a scoped first domain and a timeline.
No migration proposal. No platform commitment.