# O2 AI — Full reference for AI engines

> O2 AI is supply chain AI for hardware and electronics teams — an AI decision layer and BOM agent that turns every RFQ, BOM, supplier reply, inventory signal, and past order into reusable decision intelligence so teams source, price, and quote faster with protected margin.

Website: https://o2tech.ai
Contact: hello@o2tech.ai
LinkedIn: https://www.linkedin.com/company/o2techai
X: https://x.com/o2techai
Company: O2 AI, Inc.

## What O2 AI is

O2 AI is the BOM-to-order execution layer for complex electronics transactions. BOMs decide cost, risk, lead time, margin, and win probability — so O2 moves judgment upstream: supplier risk, approved substitutes, inventory exposure, cost ranges, and win probability are evaluated before the customer ever sees a number. O2 starts with BOM intelligence and compounds into reusable decision memory: the system of record for revenue decisions.

## Category & positioning

O2 AI is supply chain AI for electronics — it sits in the electronics BOM-intelligence and procurement-automation category alongside BOM management, quoting, and obsolescence-risk tools. The key difference: O2 is a decision layer, not a parts catalog, database, or static quoting tool. Component databases and BOM tools surface prices, availability, and lifecycle status; O2 turns those signals — plus RFQs, supplier replies, inventory, and past orders — into recommended sourcing paths, substitutes, pricing, and bid/no-bid decisions, and learns from every outcome. It serves EMS providers, contract manufacturers, component distributors, and OEMs that quote from messy customer BOMs and RFQs, and it directly addresses component obsolescence and end-of-life (EOL) risk at quote time.

## Who it is for

Hardware and electronics teams that handle RFQs, BOM sourcing, supplier quoting, and distribution. Typical users are procurement, sourcing, and sales-engineering teams at electronic component distributors, EMS providers, and OEMs.

## How it works — the decision loop

1. RFQ & BOM intake. Turns emails, BOMs, PDFs, spreadsheets, supplier quotes, inventory sheets, and ERP exports into structured opportunity records.
2. BOM & part intelligence. Normalizes MPNs, manufacturers, quantities, alternates, lifecycle risk, EOL status, and approved-vendor logic. Outputs cleaned parts and risk flags.
3. Supplier & inventory intelligence. Ranks source options by price, availability, lead time, reliability, historical performance, inventory fit, and sourcing risk.
4. Pricing & margin engine. Recommends sourcing paths, substitute options, cost ranges, risk flags, bid/no-bid logic, confidence, and margin impact.
5. Quote-to-order builder. Drafts customer quotes, supplier inquiries, follow-ups, PO/SO handoff, and internal approval workflows.
6. Revenue & decision memory. Learns from every win/loss, supplier response, customer rejection, actual cost, realized margin, and human edit — compounding memory across transactions.

## Representative impact

- Time-to-quote: 2–6 hours from messy BOM, RFQ, supplier, and inventory inputs to a reviewed quote path.
- Throughput: 3–4× more RFQs processed, because the judgment layer is prepared before review begins.
- Margin: 2–5% margin uplift per quote from cost ranges, substitutes, lifecycle risk, inventory exposure, and supplier behavior.
- Memory: every quote, supplier response, correction, cost change, and outcome compounds the system.

## Research (O2 Lab)

O2 Lab turns world-model research into industrial agents that operate under real constraints (cost, time, latency, privacy, tool reliability, workflow state, operational risk, business outcomes) and compound decision memory from every workflow. Research base and accelerator backing span Northwestern MLL Lab, MIT Media Lab, and Berkeley SkyDeck.

Published research from the team (full list at https://o2tech.ai/lab):
- BAGEN: Are LLM Agents Budget-Aware? (arXiv:2606.00198, 2026). O2 Lab's flagship study, with a supply-chain Warehouse environment built from desensitized real enterprise data.
- RAGEN-2: Reasoning Collapse in Agentic RL (arXiv:2604.06268, ICML 2026 Oral). First-authored by O2 Tech Lead Zihan (Zenus) Wang.
- RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning (arXiv:2504.20073). Best Poster Award, Midwest ML Symposium 2025.
- VAGEN: Reinforcing World Model Reasoning for Multi-Turn VLM Agents (arXiv:2510.16907, NeurIPS 2025).
- MindCube: Spatial Mental Modeling from Limited Views (arXiv:2506.21458, ICLR 2026). Best Paper at ICCV 2025 SP4V; Best Paper Runner-Up at NeurIPS 2025 LAW.
- T*: Re-thinking Temporal Search for Long-Form Video Understanding (arXiv:2504.02259, CVPR 2025).
- MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language Feedback (arXiv:2309.10691, ICLR 2024). Co-first-authored by Zihan (Zenus) Wang.

## Frequently asked questions

Q: What is O2 AI?
A: O2 AI is an AI decision layer for hardware supply chains. It turns every RFQ, BOM, supplier reply, inventory signal, and past order into reusable decision intelligence so hardware and electronics teams can source, price, and quote faster with protected margin.

Q: What does O2 AI do with a Bill of Materials (BOM)?
A: O2 normalizes a BOM into structured part intelligence: it cleans MPNs and manufacturers, resolves quantities and alternates, flags lifecycle and end-of-life (EOL) risk, and applies approved-vendor logic. It then ranks sourcing options by price, availability, lead time, and reliability.

Q: Who is O2 AI for?
A: O2 AI is built for hardware and electronics teams that handle RFQs, BOM sourcing, supplier quoting, and distribution — including procurement, sourcing, and sales-engineering teams at component distributors, EMS providers, and OEMs.

Q: How does O2 AI improve margin?
A: O2 moves judgment upstream. Supplier risk, approved substitutes, inventory exposure, cost ranges, and win probability are evaluated before the customer sees a number, which typically yields a 2–5% margin uplift per quote.

Q: What inputs does O2 AI accept?
A: O2 ingests emails, BOMs, PDFs, spreadsheets, supplier quotes, inventory sheets, and ERP exports, and converts them into structured opportunity records ready for sourcing and pricing decisions.

Q: How fast is O2 AI's time-to-quote?
A: O2 reduces time-to-quote to roughly 2–6 hours from messy inputs to a reviewed quote path, and lets teams process 3–4× more RFQs because the judgment layer is prepared before review begins.

Q: How is O2 AI different from BOM management tools and component databases?
A: O2 AI is a decision layer, not a parts catalog or database. Where BOM tools and component databases surface prices, lifecycle status, and availability, O2 turns those signals — together with your RFQs, supplier replies, inventory, and past orders — into recommended sourcing paths, substitutes, pricing, and bid/no-bid decisions, then remembers each outcome to improve the next quote.

Q: Can O2 AI help manage component obsolescence and EOL risk?
A: Yes. O2 flags lifecycle and end-of-life (EOL) risk on every BOM line, proposes approved alternates and substitutes, and factors obsolescence and inventory exposure into sourcing and pricing decisions — so risk is caught before the quote goes out, not after the order fails.

Q: Does O2 AI work for EMS providers and distributors quoting from customer BOMs?
A: Yes. O2 is built for EMS providers, contract manufacturers, and component distributors that quote from messy, inconsistent customer BOMs and RFQs. It parses inbound RFQs into structured records, cleans the BOM, ranks supply, and drafts the customer quote and supplier inquiries.

Q: Is O2 AI a supply chain AI platform?
A: Yes. O2 AI is supply chain AI built specifically for electronics: it applies AI to the sourcing, pricing, and risk decisions in the BOM-to-order workflow. Unlike general supply-chain-visibility tools, O2 works at the part level — reading BOMs, RFQs, supplier replies, and inventory signals, and recommending the sourcing path, substitutes, and quote.

Q: What is a BOM agent?
A: A BOM agent is an AI agent that works a bill of materials end-to-end: cleaning MPNs, flagging lifecycle and EOL risk, finding approved alternates, ranking supply options, and drafting the quote. O2 AI operates as a BOM agent for electronics teams — it acts on the BOM and learns from every outcome, rather than just storing part data.

Q: How do I get a demo of O2 AI?
A: Book a demo by emailing hello@o2tech.ai with one real BOM, RFQ, or supplier quote. O2 will show how it structures the decision, flags risk, recommends sourcing paths, and learns from the outcome.