Two things happened at the same time.
A2go was built to bring them together, in a way that creates ongoing agility and resilience in a supply chain now and into the future.
Agentic AI became genuinely capable
Not a chat window, but systems that can reason across data, rules, constraints and tradeoffs, and come back with an action a planner can defend.
Supply chains stopped behaving
Tariffs, conflict, weather, freight, demand that no longer resembles last year’s. Nobody is waiting this one out.
Founded
Decision pillars of purpose-built supply chain agents
Operating centers — United States and Brazil
Weeks to first domain live
Three vantage points on the same problem.
A2go’s executive team came at supply chain from three directions and kept arriving at the same conclusion: the technology was finally ready, and the way it was being applied was going to fail most companies.
Mike Romeri
Chief Executive Officer & Founder
Mike spent his career inside supply chain operations, and he kept finding the same gap in every one of them. The insight side kept improving — better forecasts, better analytics, better dashboards, reports arriving sooner every year. The decision side did not move. A disruption would surface on a Monday and still take days to resolve, and the resolution came out of a conference call, a planner’s instinct and a spreadsheet rebuilt by hand. Afterward, nobody could reconstruct why that call was made, what got traded away, or whether it was the right one.
The shortage was never insight. It was everything that happens after it.
Speed up the reporting into a decision process that is still manual, still partly guesswork and still not auditable, and all you have bought is an earlier view of a problem you cannot act on any faster.
- 40 years in supply chain · Harvard MBA
- 20+ years Partner at PRTM (now PwC)
- Contributed to SCOR as a practitioner at PRTM
- Co-founded OPS Rules with MIT’s David Simchi-Levi — acquired by Accenture, 2016
Cesar Oliveira
Chief Operating Officer
Cesar has spent years deploying ERP and enterprise platforms across manufacturing, distribution, food and industrial operations. Systems of record execute and track reliably; they were never built to reason across one another. And no company arrives at its stack by design — it accumulates, one platform at a time, plus whole environments inherited through acquisition. What companies need is not another system. It is AI that connects not just the siloed data but the decisions that span it: the tradeoffs, the guardrails, the priorities that determine which order gets protected.
Coordination is the product, and every pain point has to be tied to a business outcome before anyone builds anything.
He designed the A2go AI Assessment around exactly that — start from the decision that is costing money, name the outcome it should move, then coordinate across forecasting, planning, inventory and allocation to move it.
- 20+ years ERP transformation
- MBA, University of Colorado Boulder
- Led 25+ enterprise ERP go-lives: SAP, Oracle, Epicor
- Architect of ADIP, built natively on Databricks
- Leads the 32-person Brazil team
Stephen Hutson
Chief Technology Officer & Co-Founder
Stephen saw the commoditization coming before most of the market did. The models themselves were going to become a utility — every supply chain organization would eventually have access to the same ones, at roughly the same cost, with roughly the same capability. Whatever advantage existed was never going to live there. It was going to live in what surrounds the model: the data a specific decision requires, drawn from wherever it already sits, and what accumulates around that decision afterward.
The model is the commodity. What you build around it is not.
Routine decisions can run automatically, inside limits you set. The ones that need a person arrive as explainable recommendations with the projected outcome attached, in front of a knowledge worker while there is still time to act. Every decision is logged and auditable — and becomes part of the record of how your company actually decides, which is what makes the next recommendation better than the last. That part cannot be commoditized, because no vendor has it.
- 9 years Principal, PwC-PRTM
- Contributed to SCOR while at PRTM
- Nominated Distinguished Engineer at IBM
- Ran Watson AI and Commerce Analytics Services for APAC
- Designed A2go’s data orchestration layer
- Architected the composable agent framework
Guilherme Yoshimura
Head of Data Science
- 12+ years in IT, a decade of it in data science and advanced analytics
- MSc Data Science, University of São Paulo; academic residency, University of Alabama
- Data-driven transformation work with Nestlé, iFood, Thoughtworks, Honda and Itaú Unibanco
- Leads agent design and deployment across the four pillars
Guilherme owns A2go’s data science strategy — how the agents are built, evaluated and put into production. His background spans manufacturing, financial services and high-volume digital platforms, which is the range the agent library has to hold up across. He builds and leads the teams that turn machine learning into decisions a planner will actually approve.
Matt Calamusa
US Director of AI Sales
- 16+ years in enterprise technology sales and GTM leadership
- Graduate thesis-in-residence, The Walt Disney Company Global Corporate Alliances
- Senior commercial roles at Syren Cloud, Nutanix and Arista Networks
- Enterprise deals across healthcare, financial services, aviation and manufacturing
Matt leads A2go’s US revenue strategy and the executive conversations that start it — where a company’s highest-cost decision sits, and what it would take to move it. He has spent his career translating technical capability into deployments with a number attached, across cloud, data and security. He is a player-coach: he builds the team and carries a bag.
Brazil is where a large part of the platform gets built.
Not an offshore delivery function. A2go’s Brazil operation is a 32-person engineering and delivery center that designs agents, hardens them against real operations and takes them into production alongside customers.
Where the hardest environments are
Food, protein and agriculture at national scale — multi-plant, multi-channel, high-SKU, with biological supply variability and commodity price exposure that most planning systems were never built for.
Deep SAP and multi-ERP practice
Complex SAP estates, plus the mix of platforms that accumulates through acquisition. Reconciling across them without a standardization program first is routine work, not a special case.
Pricing and demand planning at speed
Regional demand, pricing and allocation decisions that move weekly rather than monthly. This is where the forecast and OTIF pillars were pressure-tested.
The agent library was not built in a lab against clean data. It was built against operations that break the assumptions in most planning software, which is why it holds up in a multi-ERP estate assembled through three acquisitions.
Agribusiness and agri-processing Food, protein and perishables
Inside ADIP
The three layers these three arguments produced, and how they fit together.
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Point of viewThe Coordination Gap
The thesis in full: why the cost sits between your systems, not inside them.
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ReferenceWorked decisions
A decision closed end to end, with the alternatives priced.
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The platform has three layers because the team has three arguments.
Each layer answers one of them. Take any one away and the argument above it stops holding.
A data foundation
That reads from where your systems already are.
Coordinated supply chain agents
That reason across them.
A judgment layer
That holds the part no vendor can supply.
Why we built ADIP the way we did.
Three routes are on offer today. Two ask you to change how you work. The other starts with how you already work.
Buy a prebuilt platform and conform to it
Its data model, its workflows, your teams re-engineered around it — all before any of it reaches the numbers.
Fund a long consulting engagement
Measured in years, against an outcome nobody can name at the start.
Take what you already run and make it work better
Your pain points set the sequence, not a vendor’s roadmap — and the first one is live in 8–12 weeks.
ADIP orchestrates only the data a decision actually needs — from the ERP, the planning tool, the warehouse system, the supplier feed, wherever it sits today — and puts interoperable agents on top of it that coordinate across forecasting, planning, inventory and order promising. The recommendation reaches the person who owns the call with the tradeoffs and the projected outcome attached, while the order can still be re-promised, the shipment re-routed, the buy adjusted.
Built from the pain points in.
Our team came out of supply chain, with depth in industrial manufacturing, agriculture, and food and protein processing — multi-plant, multi-ERP, high-SKU, thin-margin environments where a scheduling decision has a number attached to it by end of week.
That is why the agents are organized around pain points rather than industry-standard workflow diagrams. We built for the four pillars of supply chain and the pain points within them.
Forecasting & Planning
Operational Planning
Supply & Inventory Optimization
OTIF Optimization
Adopting AI shouldn’t put the business on hold.
Every company with a physical supply chain is going to build AI capability. That part is settled. What is still open is whether building it has to mean a consulting engagement measured in years, a data program measured in millions, and a return that arrives after the people who approved it have moved on.
You have the same volatility, but less room to absorb it
For a $20B enterprise, that path is survivable. For a $500M manufacturer it is not — and that company has the same volatility, the same fragmented systems from the last three acquisitions, and considerably less room to absorb a bad quarter.
Not the compromise version
The incremental path is not the compromise version for companies with smaller budgets. It is the better path at any size, and it happens to be the only one available at some of them.
Who we serve
The profile, the complexity and the qualifying signals.
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Deep diveWhat deployment looks like
Rapid assessment, first domain live, then expansion at a pace you set.
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AssessmentAI readiness survey
Ten minutes, and it tells you where your decisions stall before we ever talk.
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Your stack records. The agents reason. Your people decide.
A2go is supply chain decision intelligence — one governed layer over the systems you already run.