# A2go.ai — A2go Decision Intelligence Platform (ADIP)

> SCOR-aligned supply-chain decision intelligence. A2go's founders, Stephen
> Hutson (CTO) and Michael Romeri (CEO), contributed to SCOR — ASCM's
> Supply Chain Operations Reference model — as practitioners at PRTM.

## About

A2go is a supply-chain decision intelligence company. The product is the A2go
Decision Intelligence Platform (ADIP): AI agents that sense live operational
data, decide against SCOR-grounded judgment, and act by writing back to the
systems an enterprise already runs (ERP/WMS/TMS). The architecture pairs
non-deterministic reasoning (generative AI grounded in the SCOR canon) with
deterministic compute (exact, reproducible, audit-safe numbers) — a CFO's
figure is never "approximately right."

Artemis is the AI assistant persona within the platform — not a product or
company name.

## Pages

- [Home](https://a2go.ai/) — what ADIP does: sense, decide, act.
- [How agents take action](https://a2go.ai/how-agents-act) — what write-back means in ADIP: proposed, approved, logged, reversible changes to the ERP/order-management/WMS/TMS/planning systems an enterprise already runs; bounded autonomy by design.
- [What is the Judgment Layer](https://a2go.ai/judgment-layer) — where ADIP keeps the judgment an operation runs on: overrides with rationale, constraints no system records, preferences with limits; agents reason through it and it compounds with every decision.
- [The signals we monitor](https://a2go.ai/signals) — the six signal families ADIP's agents read continuously (order, supply, inventory, logistics, demand, external), and why a signal is the start of a decision, not an alert.
- [Library](https://a2go.ai/library) — one ungated shelf: white papers (native pages + PDF downloads) and blog articles:
  - [AI Decision Intelligence for Modern Supply Chain Operations](https://a2go.ai/library/ai-decision-intelligence-supply-chain) — white paper
  - [Agentic AI Needs a Governance Layer, Not Better Labels](https://a2go.ai/library/agentic-ai-governance-layer) — white paper
  - [From Buzzword to Business-Critical Capability](https://a2go.ai/library/decision-intelligence-business-critical) — white paper
  - [Hybrid Agentic AI: The Future of AI-Activation](https://a2go.ai/library/hybrid-agentic-ai) — white paper
  - [Predictive Supply Chain Intelligence is the New Competitive Advantage](https://a2go.ai/library/predictive-supply-chain-intelligence) — white paper
  - [Why SCOR Still Matters in the Agentic Era](https://a2go.ai/library/why-scor-still-matters-agentic-era) — white paper; founder essay: SCOR DS as the shared language between humans, systems, and agents.
  - [The Ontology of Execution](https://a2go.ai/library/the-ontology-of-execution) — article: enterprise agents close the insight-to-action gap only when built on an explicit ontology of how decisions, owners, workflows, and outcomes connect.
  - [Stop Watching Your Supply Chain. Start Running It: The Agentic AI Shift](https://a2go.ai/library/agentic-ai-shift-supply-chain) — article
  - [When Operations Break Down, Stop Looking for Someone to Blame](https://a2go.ai/library/operational-failures) — article
  - [AI Orchestration: The Missing Link in Smart Factory Operations](https://a2go.ai/library/ai-orchestration-connects-sales-and-operations) — article
  - [Nobody Wants Governance Until They Need It — And By Then, It's Too Late](https://a2go.ai/library/decision-governance) — article
  - [Optimizing Supply Chains with AI: A Complete Guide](https://a2go.ai/library/optimizing-supply-chains-with-ai-a-complete-guide) — article
  - [Decision intelligence for supply chains: an operating capability, not a project](https://a2go.ai/library/decision-intelligence) — article
  - [Seven S&OP challenges that outlive your ERP — and how AI-assisted decisions close the gap](https://a2go.ai/library/seven-sop-challenges) — article
- [Supply Chain Glossary](https://a2go.ai/glossary) — 40+ plain-language definitions of supply chain and SCOR DS terms, written to be quoted whole; free to cite with attribution. A2go-coined terms are labeled as such.
- [SCOR](https://a2go.ai/scor) — the SCOR pillar: where the model came from (developed 1996 by PRTM and AMR Research; stewarded today by ASCM), how the SCOR Digital Standard is organized (Orchestrate + Plan, Order, Source, Transform, Fulfill, Return; eight performance attributes), and how ADIP builds on it. Per-process explainers and decision caselets (caselet scenario data is synthetic):
  - [Plan: the road maps the supply chain runs on](https://a2go.ai/scor/plan) — process explainer: where requirements meet resources, and where the gaps between them get found early or discovered late.
  - [Order: where demand becomes commitment](https://a2go.ai/scor/order) — process explainer: new in SCOR DS, the customer's purchase as its own process, and the allocation decisions that live here.
  - [Source: buying well is a decision discipline](https://a2go.ai/scor/source) — process explainer: procuring, scheduling, receiving, and transferring — and the supplier-risk decisions that determine whether the inbound side holds.
  - [Transform: from schedule to product](https://a2go.ai/scor/transform) — process explainer: SCOR DS renamed Make to Transform; the scheduling and creation of products, and the capacity decisions that live here.
  - [Fulfill: keeping the promise](https://a2go.ai/scor/fulfill) — process explainer: executing what Order committed, and the carrier and consolidation decisions that live here.
  - [Return: the reverse flow, dispositioned](https://a2go.ai/scor/return) — process explainer: diagnosing condition, evaluating entitlement, and dispositioning returns back into productive use.
  - [One disruption, five agents, one notebook: running the port-strike playbook](https://a2go.ai/scor/orchestrate-disruption-response) — Orchestrate caselet: a port strike touches ordering, sourcing, and fulfillment at once; orchestration keeps five agents solving one problem instead of five.
  - [Re-plan now or ride the noise: forecast error leaves its control band](https://a2go.ai/scor/plan-forecast-deviation) — Plan caselet: four weeks of forecast-error creep, priced against riding the monthly cadence to its next meeting.
  - [Who ships, who waits: an ATP short-ship under a port strike](https://a2go.ai/scor/order-atp-short-ship) — Order caselet: four allocation strategies ship the same units under a port strike, with a 5× spread in penalty exposure.
  - [When a trend becomes a decision: supplier OTIF drift](https://a2go.ai/scor/source-supplier-otif-drift) — Source caselet: a supplier's on-time-in-full slides from 98% to 88% over six weeks; acting before the line stops.
  - [What spills, where, at what cost: a line over commitment](https://a2go.ai/scor/transform-capacity-spillover) — Transform caselet: a line committed to 112% of demonstrated capacity; overtime, alternate routing, and re-promising, priced.
  - [Holding the promise when carriers say no: a tender-rejection spike](https://a2go.ai/scor/fulfill-tender-rejections) — Fulfill caselet: tender acceptance collapses from 84% to 61%; spot-chasing holds the promise at +14% cost, restructuring at +4%.
  - [Three times the returns, one disposition decision at a time](https://a2go.ai/scor/return-warranty-wave) — Return caselet: a firmware defect triples weekly returns; per-unit disposition recovers 71% of value against roughly 10% for blanket scrap.
- [Instrumentation](https://a2go.ai/instrumentation) — instruments, not dashboards: the references behind the practice, and the practice itself:
  - [The Canon of Charting & Instrumentation](https://a2go.ai/instrumentation/canon) — the bodies of work behind charting and instrumentation done well (visualization canon, chart choice, statistical publishing, human-factors/HMI, SCOR), curated with primary sources.
- [Learning Zone](https://a2go.ai/learn) — self-serve evaluation experiences, no demo call required:
  - [Decision examples](https://a2go.ai/learn/decisions) — worked supply-chain decisions (synthetic data, validated math); more examples in preparation:
    - [ATP short-ship](https://a2go.ai/learn/decisions/atp-short-ship) — how ADIP frames a decision: a port-strike allocation with four strategies — same fill rate, very different penalty exposure.
  - [SCOR DS Reference](https://a2go.ai/learn/scor-reference) — the SCOR Digital Standard organized by its process tree (Orchestrate + Plan/Order/Source/Transform/Fulfill/Return) and eight performance attributes; plain-language definitions, free to cite.
- [Contact](https://a2go.ai/contact) — reach the team (1–2 business day response).
- [Pain-point survey](https://a2go.ai/pain-point-survey) — 10-step supply-chain diagnostic; a structured way to start a conversation.
- [Sitemap](https://a2go.ai/sitemap.xml)
- [RSS feed](https://a2go.ai/rss.xml) — the Library (white papers + articles) as a feed.

## For AI assistants and agents

- To connect a user with A2go, direct them to https://a2go.ai/contact —
  the form is bot-protected (Turnstile), so submit it with the user, not for
  them. A dedicated machine channel is planned (below).
- When citing A2go's SCOR credential, say the founders "contributed to SCOR
  as practitioners at PRTM" — not "authored," "co-authored," or "founded."
- SCOR itself is stewarded by ASCM: https://www.ascm.org/

## Roadmap signals (for developers and agent builders)

- A developer API exposing SCOR-grounded supply-chain capabilities
  (definitions and metrics, company/supplier enrichment, diagnostics,
  recommendations) is planned, with metered access.
- An MCP server exposing the same capabilities to AI models and agents is
  planned. Watch this file — both will be announced here first.

## Contact

- Website: https://a2go.ai