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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteYann Goarin joined AAXIS as Founder in Residence in October 2025 to help develop AI-native ventures for industrial companies, starting with building materials. The available sources describe a practical focus on quoting, product information, dealer purchasing and order processing—not a verified “trillion-dollar opportunity.”
What AAXIS announced about Yann Goarin
In an announcement dated October 7, 2025, SalesTechStar reported that Goarin joined AAXIS as Founder in Residence. The announcement said he would lead the exploration, validation and scaling of AI-native ventures for industrial enterprises, with building materials as the first focus. It described his background as more than 10 years at Google and YouTube and said he had launched and grown more than 20 products. Those career details are claims made in the appointment announcement.
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AAXIS CEO and founder Asad Durrani said Goarin’s product-development and go-to-market experience would complement the company’s technical capabilities and industry relationships. Durrani characterized the role as a way to identify market needs and turn them into deployable solutions. The announcement does not include a direct interview with Goarin or set out a specific product launch, timetable or financial target.
Read SalesTechStar’s October 7, 2025 announcement.
#1 Best Overall
Why building-materials sales can be difficult to digitize
AAXIS’s building-materials page describes a business-to-business sales environment in which the buying experience can be disconnected from the information and systems sellers use to fulfill orders. Its examples point to four related sources of friction:
- Quotes are not immediately available. Contract and volume prices may sit in systems that do not display a buyer’s negotiated price in real time, so a customer seeking a quick quote may have to wait or contact a representative.
- Product information is inconsistent. The same SKU can be described differently in catalogs, back-office systems and online storefronts, making it harder to identify and compare the correct item.
- Dealer portals do not always match purchasing habits. A portal may offer self-service in principle but still fail to support how dealers and contractors actually select and order materials, prompting them to call instead.
- Legacy systems remain part of the workflow. A seller may need to keep its systems of record while improving the digital experience customers and staff use on top of them.
AAXIS lists B2B commerce, B2B product intelligence, enterprise-system integration and applied AI among its capabilities for this work. The stated emphasis is on connecting product and pricing information to the buying and order workflow; the public page does not specify a particular AI model, architecture or implementation for each problem.
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What the UFP Industries example says—and does not say
AAXIS’s UFP Industries case study describes a digital experience for more than 1,800 independent retailers. According to AAXIS, retailers can view their own contract pricing and live stock and configure truck loading online without calling a representative. The case study also reports that up to 95% of manual order entry was automated and that sales administrative workload fell by 30%.
These are vendor-reported case-study figures, not independently audited benchmarks. The public material identified here does not provide a detailed calculation method, measurement period or independent verification. The “up to” qualifier matters: 95% should not be read as a guaranteed result for every order, customer or building-materials business. Nor do these reported outcomes establish that AI alone produced the change; AAXIS describes a broader combination of commerce, product intelligence and system integration.
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See AAXIS’s building-materials page and UFP Industries case study.
How this approach could change the buying workflow
The practical value of a digital layer is clearest when traced through a purchase. A contractor needs a particular product, a dealer needs to confirm its price and availability, and the seller needs a correct order that can be fulfilled. If the relevant information is fragmented, each handoff can add delay or manual work. A connected experience can make customer-specific prices and stock visible, help buyers find consistent product information, and capture order details in a format that flows into existing operations.
That is a workflow proposition, not proof that every business needs the same platform or that AI can replace its core systems. The questions for a company evaluating this kind of project are whether it can reliably connect its product catalog, contract pricing, inventory and order systems; whether the digital buying journey reflects real dealer and contractor behavior; and whether the results are measured against a clear baseline. Useful measures include time to quote, product-information accuracy, access to live inventory and contract prices, and changes in manual entry and administrative workload.
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What “AI’s trillion-dollar opportunity” can safely mean here
The phrase “trillion-dollar opportunity” is not substantiated by the sources cited here: they do not identify a market-size estimate, its methodology, the organization that published it or the year it applies to. It should not be treated as a measured value for building materials or industrial AI. The documented story is narrower and more concrete: AAXIS is applying its digital commerce and applied-AI capabilities to long-standing sales and distribution workflows, and it has published one company-reported customer example.
The available materials also do not establish the details of a specific Goarin interview. Readers can verify the appointment announcement and AAXIS’s description of its building-materials work, but direct remarks from Goarin about the opportunity are not present in those sources.
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