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At Bank of America’s Private Tech Trailblazers Conference in Palo Alto, speakers described a version of AI growth built around specialized tasks, proprietary data, and industry workflows—not just larger general-purpose models. SiliconANGLE’s October 3, 2026 report offers examples spanning robotics, construction, commerce, transport, chips, financial services, healthcare, and customer support. The figures below are claims attributed to the speakers or companies, not independently audited results or a market-wide survey.
What the conference means by vertical AI
Vertical AI applies AI to a particular industry or job, often by combining models with specialized data, software, and operating processes. At the conference, the recurring argument was that this combination could matter more as general-purpose models become more interchangeable. John Furrier of theCUBE Research described specialized intelligence as a growing story, pointing to domain-specific data as a key ingredient.
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The nine examples are not a comparable cohort: they differ in purpose, deployment, maturity, and evidence. Several companies described integrating multiple layers of their operations, which may help them tailor products; the conference report does not establish that this integration creates a durable competitive advantage.
How companies are applying AI in specific industries
1. Bear Robotics: adding humanoids to a service-robot fleet
Bear Robotics co-founder Bren Pierce said the company had about 16,000 autonomous mobile robots in the field and about 4,000 on backlog, with revenue doubling annually. Its core market is restaurants in Japan and South Korea, and it also serves care homes and casinos. A partnership with LG is taking the company into warehouses and factories.
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Pierce said its humanoids share software and cloud infrastructure with the mobile-robot fleet. He credited foundation models that can learn from a few hundred examples, Nvidia Jetson Thor onboard compute, and coding assisted by large language models with compressing work that once took six months into days. He identified dexterous tactile hands as a remaining constraint, putting their cost at about $30,000 per hand. These are speaker-reported figures and explanations, not independently verified deployment or development benchmarks.
2. All3: automating construction around the worksite
All3, the operating name of Address Robotics Ltd., is developing an integrated construction workflow: plot-based design, permit-ready documents, robotic fabrication of one-off building elements, and on-site assembly and finishing with its Mantis mobile robot. CEO Rodion Shishkov said labor accounts for 55%–60% of construction costs.
SiliconANGLE reported that All3 was preparing its first project, a six-story co-living building on an 11-sided plot, after raising a seed round of about $25 million to $30 million. Those are reported company plans and financing figures, not evidence that the approach has already reduced construction costs at scale.
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Bloomreach CEO Raj De Datta said the company uses about 100 models and trains its Loomi AI on 7 billion consumer profiles. He claimed the models perform five to 10 times better than out-of-the-box large language models, but the conference report provides no benchmark method for that comparison.
The report also says almost half of Bloomreach’s customers use an AI agent, the number of customers using four agents grew 23-fold in a year, and Loomi Connect calls rose 83% month over month. These are reported adoption and usage measures; the report does not define a common baseline or independently validate them.
4. Harbinger: making medium-duty electric trucks pencil out
Harbinger CEO John Harris said the company’s electric and hybrid platforms are priced at parity with diesel. He estimated that a typical parcel truck in California saves about $30,000 a year on fuel after charging costs. That estimate is specific to the described truck and location and is not presented with a calculation method in the report.
The report names FedEx and Thor Industries as customers. Harbinger’s production spans delivery trucks, RV chassis, energy storage, and Army autonomous ground vehicles; its battery system also powers Airstream travel trailers. Harris said the company roughly doubles output capacity each year. These company statements do not establish total ownership costs or whether the fuel estimate applies to other fleets.
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Unconventional AI is developing hardware and software intended to reduce AI-system power use by about 1,000 times, according to CFO Ali Esfahani. SiliconANGLE reported that the company taped out a chip at TSMC on June 1 and had raised about $540 million. The described approach uses physics-based dynamics on standard semiconductor processes, with the system’s state holding memory.
The 1,000-times figure is a design goal or company claim, not a measured result from a deployed system: the report gives no test protocol or comparative power measurements.
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6. Airwallex: packaging financial access across markets
Airwallex head of corporate development, capital markets and investor relations Irvin Sha said the platform enables businesses to open accounts, accept payments, and issue cards across roughly 80 to 100 major economies. Founded in Melbourne in 2015, the company had built more than 90 licenses, banking partnerships, and card-network connections, according to the report. It is also adding AI for customer agents.
SiliconANGLE reported about $960 million raised across Series F, G, and H, a valuation of up to $11 billion, and annual revenue run rate of about $1.4 billion. These reported company figures do not by themselves establish profitability or the availability of every service in every market.
7. Hippocratic AI: voice agents for healthcare support
Hippocratic AI chief business officer Shubhra Jain said the company’s voice agents handle scheduling, pre-surgery preparation, post-discharge follow-up, and chronic-disease management for health systems, payers, and life-sciences companies. She said they do not diagnose or prescribe.
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Jain described a 31-model safety architecture: one conversational model and 30 supervisory models. Six health systems that invested in the company supplied 6 million patient calls for fine-tuning. The report says Hippocratic AI had more than 60 enterprise clients, including five of the largest national payers. These company-reported figures describe system design and customer reach, not proof of improved clinical outcomes or safety in practice.
8. CloudWalk: automating customer support with in-house compute
CloudWalk CEO Luis Silva said the company served more than 10 million active users through InfinitePay in Brazil, Pierre, and JIM.com in the United States, and had passed $2 billion in revenue. He said its agents ran on hundreds of Nvidia Blackwell GPUs and handled 99% of customer support, up from 65% 18 months earlier. The report says half of users talk to agents daily and revenue per employee was $2.7 million.
These are company-reported figures. The report does not define the denominator behind the support-automation rates or independently audit the user, revenue, and staffing measures, so they should not be treated as a like-for-like benchmark against other customer-service systems.
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Together, the cases show several ways firms are connecting AI to a defined job: robots operating in physical environments, software trained or configured for a sector, and agents embedded in support workflows. Some also integrate hardware, infrastructure, or operational processes rather than relying on a model alone. That is the strategic logic behind the conference’s vertical-AI theme.
The evidence remains uneven. Product plans, company metrics, and executive forecasts are not equivalent to independently measured outcomes. The SiliconANGLE report supplies no shared benchmarks, audit records, buyer comparisons, or common evaluation method across the companies. It therefore supports a view of what participants are building and claiming—not a ranking of which product performs best or proof that vertical AI is already a universal growth engine.
Why capital needs may shape the route to public markets
Bank of America vice chairman and global head of TMT equity capital markets JD Moriarty said AI and robotics businesses need more capital and may reach public markets at greater scale. He described investor interest as favoring durable, outsized growth and said 2026 activity leaned toward hardware and semiconductors over software. This is Moriarty’s market assessment in the conference report, not a quantified forecast based on a market dataset.
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