Dinakar Munagala
Analyst · Rosenblatt Securities
Thank you, Lana, and good afternoon, everyone. We came off a breakout growth year in 2025, and we expect 2026 to continue that trend. Q1 strengthened our commercial foundation through several new contracts and partnerships. First, we expanded our NeoTensr contract, bringing the total potential value to $70 million. We signed a strategic partnership agreement with Winmate, a publicly traded leader in ruggedized computing with the intent to close approximately $15 million in business in the first year. We deepened our joint engagement with Nokia across Asia Pacific. Together, we stood up a joint AI innovation lab advancing hybrid AI rack scale development. The engagement also includes a strategic partnership with Datacomm, one of Southeast Asia's leading cloud service providers. Finally, we announced Blaize AI Services and will bring our first application service to market. Q1 revenue came in at approximately $2.7 million. This reflects a global memory shortage that limited server availability from one of our trusted suppliers and delayed orders. Customer demand remained intact throughout the quarter. We expect to secure the inventory needed to deliver over $11 million to a single customer in the second quarter of this year and we are reaffirming our full year 2026 revenue guidance of $130 million. At GITEX AI 2026, in April, one of the largest AI showcases in Asia, we announced Blaize AI Services, which we expect to turn AI infrastructure into production-ready APIs that cloud service providers, data center operators and system integrators can deploy, monetize and resell. Today, we are going to announce the next step in execution, the upcoming launch of our face recognition AI service, the first in a series of application-level services running on the Blaize Hybrid AI platform. Why this matters? AI services will complement our hardware sales with recurring application layer revenue per query. It's higher margin, it's stickier, and it scales with our partners' growth, not just with their CapEx cycle. Face recognition is the first proof point, additional high-demand services, including intelligent document processing will follow. We have signed a contract with NeoTensr that is expected to generate up to $50 million in revenue in the first year. This builds on more than $20 million in revenue that we recognized in Q4 of 2025, bringing the total potential value to approximately $70 million. The development uses a co-branded AI server built on Blaize Quad card. Each server handles 200-plus simultaneous camera streams with advanced AI analytics while running LLM and VLM inference on the same infrastructure. This is what our hybrid AI architecture was built for, real-time perception at the sensor layer, advanced reasoning on the same rack, no round trip to a distant cloud. The rollout is expected to span multiple cities across Asia Pacific in multiple phases. Each phase is expected to drive higher-margin revenue as the AI services layer takes hold. Earlier this month, we entered into a strategic agreement with Winmate. Together, we will integrate Blaize AI into ruggedized systems, drones, handhelds, vehicle-mounted units and embedded devices for mission-critical operations, border security, maritime, essential infrastructure and field health care. Beyond the contracts I just described, we are advancing a series of rack-scale hybrid AI engagements anchored by our joint partnership with Nokia. This work reaches cloud service providers and infrastructure partners. These opportunities are multisite, multiphase with hundreds to thousands of edge nodes per program. They span smart city, sovereign data center and large-scale ruggedized field use cases. The architecture is hybrid GSP plus GPU at rack-scale, orchestrated by Blaize AI Services stack. The pattern is consistent. Customers want sovereign control of their data. They want efficiency. They want application-level AI services they can resell. Hybrid AI delivers all 3. Stepping back, the AI infrastructure conversation is shifting fast. A year ago, the industry was focused on one thing, massive centralized GPU clusters for training. Today, the conversation moved decisively towards sovereign language model, inference at the edge, in-country at unit economics that actually work at scale. That shift is what Blaize was built for. Three pillars: number one, sovereign AI infrastructure. Governments and large enterprises across Asia, Middle East and Europe demand compute that stays within their borders under their control. Hybrid rack-scale enables this without hyperscaler economics. Number two, smaller LLM-based AI services. Most enterprise AI workloads do not need a frontier model. They need a tightly tuned domain-specific model on infrastructure they can afford. Our hybrid architecture runs vision and language workloads on the same rack, opening the service revenue our partners can monetize for query. Number three, programmable energy-efficient compute. This is where the Blaize GSP advantage compounds. Performance per watt, deterministic latency, a software stack that serves vision, LLM and VLM workloads on the same hardware. Hybrid rack-scale is the unit of deployment for the next phase of AI. We are building toward it, and our partners are buying in. On May 6, we closed a $35 million equity offering, supported by a group of large institutional investors. This capital strengthens our balance sheet. The proceeds will support our commercial deal commitments, continued AI services development, rack-scale hybrid platform advancement and next-generation platform development. Blaize is a company executing against one of the most significant opportunities in AI history. Rack-scale hybrid AI, sovereign infrastructure, the strategic path for recurring AI services revenue and partnerships that put Blaize at the center of the AI inference build-out. Contracts are expanding, partnerships are deepening across an increasingly diverse base of AI use cases. And finally, engagements are advancing in the field. So with that, I'll turn it over to our CFO, Harminder Sehmi.