PROSPECTUS SUMMARY

This summary highlights selected information contained elsewhere in this prospectus. This summary does not contain all of the information that you should consider before deciding to invest in our Class A common stock. You should read this entire prospectus carefully, including the sections titled “Risk Factors,” “Special Note Regarding Forward‑Looking Statements,” “Management’s Discussion and Analysis of Financial Condition and Results of Operations,” and “Business,” and our consolidated financial statements and related notes included elsewhere in this prospectus before making an investment decision.

Overview

Syntiant is a semiconductor and software company focused on enabling Physical AI, which we define as on-device sensing and neural inference that allow devices to perceive, process, and respond to real-world inputs. We provide a full-stack, ultra-low-power platform that combines purpose-built neural decision processors (“NDPs”), high-performance sensing, and optimized AI models and tools, enabling customers to deploy intelligent features locally at the point of interaction while using the cloud selectively for training, orchestration, and software upgrades. Our technology has been deployed across a range of devices, from earbuds and wearables to industrial systems and automobiles. As of March 31, 2026, our AI technology had been deployed in tens of millions of devices globally, and our SiSonic sensor products had shipped in more than 25 billion units. We believe this deployed base provides customer validation, commercial access, and a foundation for expansion into next-generation Physical AI applications.

We believe Physical AI represents a natural evolution in how intelligence is deployed, driven by advances in neural network efficiency, purpose-built low-power inference hardware, and the proliferation of natural user interfaces across consumer, automotive, industrial, and medical devices. These advances have made it increasingly practical to run continuous, real-time inference directly on endpoint devices, enabling always-on responsiveness, low-latency interaction, and privacy-preserving local processing within power budgets that were not achievable until recently. While cloud infrastructure remains well-suited for training, orchestration, and software upgrades, its latency, connectivity, and cost characteristics are increasingly misaligned with the performance requirements of many endpoint applications. We believe this shift is expanding the range of AI-enabled applications and increasing demand for purpose-built platforms capable of delivering continuous intelligence directly on-device. A third-party consulting firm estimates that Physical AI will unlock a global market of approximately $490 billion by 2030, and we estimate our total addressable market within Physical AI will reach $22 billion by 2030, representing a compound annual growth rate (“CAGR”) of 27% from 2025 to 2030.

We believe our differentiation is rooted in our ability to deliver ultra-low-power, always-on neural inference on streaming inputs in power-constrained devices, where inference is the primary workload. Our full-stack hardware and software platform helps customers reduce dependence on cloud inference infrastructure, accelerate time to market, and lower power consumption without sacrificing responsiveness. Our established sensor business provides existing customer relationships that we are leveraging to expand across audio, vision, and multi-modal Physical AI use cases.

We market and sell our products through an experienced global sales organization that supports customers from initial design engagement through qualification and volume production. Once a design opportunity is won, our products are embedded in a customer’s product architecture, creating meaningful switching costs and supporting follow-on opportunities across successive product generations. Following the acquisition of the Consumer MEMS Microphone (“CMM”) business from Knowles Corporation in December 2024, we significantly expanded our commercial organization, which supports global tier 1 brands, OEMs, ODMs, module providers, and other ecosystem partners across consumer, automotive, medical, and industrial end markets.

Industry Overview

Significant Growth of Physical AI

AI has become one of the most transformative technologies of the modern era, permeating virtually every sector of the global economy. While many AI applications today run primarily in the cloud, the technology is increasingly moving closer to where it is used, embedded in physical systems that interact directly with the real world. This trend is referred to as Physical AI and spans numerous end markets including automotive, industrial automation, logistics, humanoid and service robotics, aerospace and defense, healthcare, and smart homes.

Physical AI has gained traction in everyday applications, such as earbuds, smart speakers, medical devices, autonomous vehicles, and early industrial robots. According to a third-party consulting firm, the number of connected devices is expected to reach 25 billion by the end of 2026, and many of these devices are candidates for on-device sensing and AI processing. However, we believe

this is just the beginning for Physical AI. We believe the market is poised to expand into increasingly complex applications, including humanoids and advanced industrial robots designed to operate in unstructured and dynamic environments.

Physical AI represents the next evolution of AI, shifting from cloud-based models to embedded, real-time intelligence across devices, environments, and interactions.

Key Drivers of Physical AI Adoption

Physical AI is transforming machines from passive tools to reactive partners that can hear, see, and act with immediate precision. Below are some of the key end-market drivers leading to adoption of Physical AI:

Natural voice and conversational AI interfaces: Voice provides a natural, intuitive, and hands-free way for users to interact with AI assistants and large language models through everyday devices. These interactions increase the importance of Physical AI systems that can accurately capture speech, infer intent, and support responsive on-device processing in real time.

Acoustic scene intelligence: In complex real-world sound environments, Physical AI systems can identify relevant sound, suppress unwanted background noise, and continuously adapt to changing acoustic conditions. We expect this to drive adoption in hearing aids, earbuds, headsets, and other communication systems where specific sound detection and speech intelligibility is critical in settings such as crowded rooms, road noise, and drive-through customer interactions.

Computer vision and spatial awareness: Vision extends Physical AI beyond audio, enabling real-time understanding of objects, environments, and human activity across edge devices. As adoption grows across smart cameras, access control, and consumer electronics, we expect demand to increase for always-on, ultra-low-power vision processing that can deliver contextual awareness without reliance on the cloud.

Continuous health monitoring: Wearables and medical devices are transitioning from simple trackers to “active guardians” by continuously analyzing biometric data. From everyday examples such as sleep patterns and blood sugar analysis to more serious alerts such as heart arrhythmia, Physical AI devices can trigger emergency responses.

Industrial monitoring and predictive maintenance: On high-speed manufacturing lines, Physical AI devices allow cameras and other sensors to act as expert inspectors, helping identify microscopic flaws on assembly lines at high speeds that are impossible for human inspectors to detect or systems that rely on outside processing.

High-speed autonomy: In dynamic environments such as homes, busy streets, industrial warehouses, and manufacturing floors, autonomous machines such as robots, drones, and vehicles must make decisions at high speeds, processing significant amounts of sensor data in milliseconds.

System Drivers of Physical AI

Historically, the majority of AI compute has been addressed by centralized cloud infrastructure, which offers vast compute and storage capacity and ample electricity supply. In recent years as AI has moved from compute-heavy training to inference workloads, the cloud-centric AI delivery model has shown significant limitations for Physical AI users.

The combination of the below factors has fueled a push to move AI inference directly onto physical devices rather than relying solely on remote cloud infrastructure.

Growth of Physical AI Models and Applications

The AI that captures public imagination, such as chatbots, image generation, and other large language models, represents only the current most visible layer of a much larger market potential. Beneath the surface, Physical AI relies on a broad ecosystem of specialized, on-device models that sense, filter, and interpret real-world signals, as depicted below. As these local intelligence

capabilities enable AI systems to perceive their surrounding environment, they enhance the quality, relevance, and responsiveness of higher level conversational and generative AI experiences.

These less-visible tasks are focused on working with on-device sensors such as microphones, image sensors, and inertial sensors to clean incoming signals and provide intelligence for multiple different applications. These Physical AI tasks are characterized by continuous operation, streaming sensor inputs, deterministic response requirements, and strict power constraints, which differentiate them from cloud-centric AI tasks.

Physical AI deployments often combine cloud-based development and orchestration with specialized on-device inference engines. A key attribute of Physical AI is connecting these layers by enabling continuous, low-power AI inference directly on-device, while interoperating with cloud-based tooling for model development and software model upgrades.

For example, in applications such as smart glasses, Physical AI platforms can complement cloud-based LLMs by cleaning audio and video signals and extracting key features from microphone or camera instead of streaming raw signals. This can reduce bandwidth, preserve battery, and improve privacy by keeping raw sensor data local. The cloud LLM layer can then handle heavy reasoning, answer complex questions, and generate responses, while working with clean structured inputs rather than fighting noise. In practice, Physical AI can make cloud AI faster and more accurate.

Illustrative Sensor Processing Chain

Below is a diagram of an illustrative audio processing chain that would be used to enable a conversational LLM to function in an extremely noisy environment, spanning from raw sound capture through increasingly complex AI enabled functions that extract structure, meaning, and intent from real-world signals. In this case, a significant amount of AI inference is being performed on-device, including signal conditioning, event recognition, and speech recognition. Depending on the use case, the signal may either be further processed on-device for complex speech recognition and language understanding or be sent to the cloud.

Historically these on-device functionalities such as signal conditioning and sound detection have been addressed by conventional, non-AI algorithms. These solutions typically followed fixed rules and worked reasonably well in controlled predictable environments but came up short in complex, everyday situations. For instance, traditional signal processing approaches generally achieve limited noise suppression under real-world conditions, particularly when background noise is non-stationary or highly variable. In contrast, AI-based deep neural network approaches can model complex, time-varying noise characteristics and demonstrate substantially greater noise suppression in practical deployments, suppressing non-stationary noise sources. This difference can translate to significant perceptual improvements for human communication in everyday noisy environments.

Challenges of Enabling AI Directly on Physical Devices

Running AI-based neural networks continuously is far more computationally demanding than running conventional algorithms, especially when performed on conventional processors such as MCUs and DSPs. This strain can result in significant performance challenges for physical devices such as:

The evolution of AI-optimized compute in physical devices mirrors the progression of AI compute in cloud-based environments. As AI workloads in the cloud expanded, the industry increasingly adopted more specialized architectures such as GPUs as opposed to general-purpose CPUs to improve the performance and efficiency of AI processing. We believe the physical device market is also poised to transition beyond general purpose processors such as MCUs, DSPs, and application processors to neural processors just as the cloud went to GPUs.

The Syntiant Full-Stack Platform

Building on years of experience with a focus on addressing AI inference challenges in battery-powered devices, we have developed and deployed a full-stack Physical AI compute platform based on three critical pillars:

Depending on the application, customers may adopt our neural processors, AI models, and sensing products independently or in combination. We support customers that deploy only neural processors, only AI software models, and only sensors, as well as customers that deploy full-stack solutions combining two or all three components.

Our Platform Advantage

Our NDPs, sensors, and AI software models form three foundational pillars of a full-stack platform, with each layer purpose-built and designed to work seamlessly together. Our comprehensive approach can shorten our customers’ time to market and reduce the engineering effort required for customers to integrate hardware, software, and models sourced from multiple suppliers or to develop and optimize these components internally.

Our platform also aims to enable more predictable performance and behavior across applications, which we believe accelerates product qualification and commercialization. Because our models are developed in close alignment with our processor

architecture and sensing inputs, customers can deploy production-ready inference workloads without extensive retraining, re-optimization, or hardware specific adaptation. In addition, our sensor portfolio and deployment history help to reduce qualification risk and support faster transitions from prototype to volume manufacturing.

We designed and developed the architecture of our NDPs internally and continue to design and develop new NDPs and SoC products internally. NDPs are implemented in silicon by third-party foundries using intellectual property licensed from third parties, including CPU and GPU cores, interface controllers, and memory libraries, among other functional intellectual property blocks as is customary in the semiconductor industry. We develop most of our AI models internally. Our AI models are trained using our proprietary training data, in-licensed third-party training data, or our customers’ training data, depending on each customer’s specific application requirements. We collaborate with third parties to develop inference model SDKs enabling customers to develop and optimize our AI models for deployment on different hardware platforms. In addition, we collaborate with third parties to provide edge processing system-on-modules AI solutions for voice, vision, gesture and other applications, which typically include a Syntiant NDP, host MCU, one or more Syntiant microphones among other sensors, and an interface for integration with a host device.

Our Technology

Syntiant Core™

The Syntiant Core is the custom-built neural engine for high-performance efficient processing of neural networks within power and size constrained environments. It is the primary building block of the Syntiant NDPs. We purpose-built these processors for continuous neural inference on inputs such as audio, vision, and other sensor data.

Unlike general-purpose MCU- or MPU-centric architectures that rely on frequent data movement between compute and memory, Syntiant Core uses an at-memory compute architecture where neural network inference is performed directly alongside on-chip SRAM, eliminating the costly data movement between storage and compute units. This enables both very high MAC utilization and ultra-low power achieving milliwatt-level power consumption with high throughput.

As a result, our processors are designed to enable always-on operation at power levels suitable for battery-powered and thermally constrained devices. This allows customers to deploy neural inference locally without sacrificing responsiveness, privacy, or device lifetime.

The Syntiant Core makes up the cornerstone of our NDP processors and is paired with an integrated companion DSP and MCU for feature extraction and management while the Syntiant Core performs the majority of the neural processing. The architecture also includes features that we designed to exploit common characteristics of device AI neural workloads. For example, some models may have zero-valued activations which Syntiant Core processors can detect to avoid unnecessary computations, increasing throughput and reducing energy consumption. In addition, inference on time-series streaming inputs may involve intermediate results from prior inference steps that Syntiant Core will automatically reuse, minimizing computation and energy consumption at each time step. These techniques improve efficiency for always-on inference applications.

We have iteratively refined our at-memory neural processor architecture over successive generations of NDP products using feedback from deployed customer models and real-world workloads. Across these generations, we have enhanced concurrency, memory bandwidth, kernel scheduling efficiency, and support for a broader range of neural network layer types and data formats. These improvements are reflected in measured gains in throughput per clock cycle and energy efficiency when compared to general purpose architectures.

Based on internal benchmarking of representative production models, our NDPs can deliver meaningful improvements in effective throughput and energy efficiency relative to certain general-purpose MCU+NPU architectures while operating at sub-milliwatt power levels suitable for continuous use. For instance, in representative workloads with certain specified parameters, Syntiant Core 2 has shown approximately 7.4x higher MAC efficiency and 5.9x higher energy efficiency than an Arm Cortex-M85 + Ethos-U55 baseline in word detection, and 11.8x higher MAC efficiency and 4.9x higher energy efficiency in noise reduction. Our NDPs aim to deliver efficiency advantages versus MCU+NPU architectures, while operating at sub-milliwatt power levels suitable for continuous, always-on use.

Syntiant Core 2 Advantage Over ARM M85 + U55 Configuration

Source: Syntiant Internal Benchmarking Data (Syntiant Core 2 vs Arm M85 + U55), 2026. Based on internal benchmarking of representative production models, including a 260k‑parameter keyword detector, a 590k‑parameter noise‑reduction model to compare the Syntiant Core 2 in the NDP120 operating at 21.5 MHz with 32 MAC in a 40nm process with an Arm Cortex M85 + Ethos U55 MCU+NPU platform implemented on a Renesas RA8P1 1 GHz M85 + 500 MHz U55 256 MAC neural coprocessor in 22nm. The Renesas device is referenced solely as the test vehicle for this representative MCU+NPU configuration. MAC efficiency reflects normalized multiply‑accumulate throughput, and energy efficiency reflects measured power consumption and is not normalized for process‑node differences.

The Syntiant Core architecture is implemented using fully digital standard CMOS logic and does not rely on analog or process-specific circuit elements. As a result, our neural processors can be manufactured in any standard CMOS process technology, which facilitates product development.

SiSonic Sensors

We have designed Syntiant SiSonic MEMS microphones and vibration sensors to combine compact size, high performance, mechanical robustness, and low power operation. Our sensor capabilities are based on patented MEMS structures and packaging technologies that support compact sensor designs with high performance and durability, together with proprietary circuitry and advanced ASIC technology designed to enable low power operation while maintaining performance. We continue to develop AI device specific sensor and system level optimizations intended to reduce power consumption, system size, and improve overall performance.

In 2024, we completed the acquisition of the CMM business from Knowles Corporation, which included the SiSonic product line and associated manufacturing, engineering, and customer relationships. Following the acquisition, shipments of SiSonic sensors have continued as part of Syntiant’s sensor product offerings, supporting ongoing customer programs and maintaining continuity of supply to the market. SiSonic MEMS microphones were among the first commercially deployed MEMS microphones, with initial products introduced in 2003 and used in early slim mobile handset designs. Since that time, more than 25 billion SiSonic sensors have shipped cumulatively across a range of consumer, automotive, and industrial applications.

Syntiant AI Software Models, Modeling Platform, and Inference Software Development Kit

We have designed our AI software models and modeling platform to reduce customer effort and accelerate time to production by delivering advanced device-level AI behaviors for sound, language, vision, and other sensor inputs and outputs. Our approach is focused on enabling levels of inference capability typically associated with server-based AI applications to be deployed directly within small, power-constrained AI devices.

We have developed and validated our models specifically for environments that require compact, predictable solutions optimized for strict power and memory limits. We have designed our AI software models to operate with reduced computational and memory requirements compared to many conventional approaches, which can support always-on real-time inference usage in AI devices. This enables system designers to implement advanced AI functionality locally, rather than relying on continuous connectivity to cloud infrastructure.

Our Syntiant Modeling Platform combines our proprietary data sets, subject matter expertise, and domain-specific and domain-independent optimization techniques, developed over multiple product generations and supported by deployed systems. This platform enables efficient development, optimization, and deployment of compact device AI models using standardized workflows.

Our Syntiant Inference SDK is designed to provide the on-target runtime and integration layer that enables Syntiant’s AI models to run across heterogeneous processing platforms, from DSPs, CPUs, and GPUs to advanced neural accelerators, using a portable runtime with target-specific acceleration backends. It supports tasks such as loading models, configuring inputs from microphones and sensors, interfacing with host processors, and managing inference results. Our portable runtime supports concurrent execution of multiple models and includes both target-specific and target-independent optimizations, such as sparsity and selective execution. This portability enables Syntiant to develop an AI model and deploy it across many different supported accelerators, which supports scaling of Syntiant’s software business.

Our Products

Syntiant Core

Neural Decision Processors (NDPs)

Our NDPs are specialized low-power processors that productize the Syntiant Core neural processing architecture into deployable devices for embedded Physical AI systems. We analogize our NDPs’ position in the Physical AI market to purpose-built GPUs in the data center market, where CPUs and MCUs excel at control-oriented and sequential tasks while GPUs and NDPs are optimized for highly parallel, compute-intensive workloads.

Our processor portfolio includes three primary families of NDPs designed for sensing modalities and Physical AI workloads:

Syntiant Core NDP Products

Note: Future roadmap includes planned next generation (“ng”) and following generation (“nxt”) offerings.

SiSonic Sensors

Our SiSonic MEMS-based microphones and vibration sensors form the front end of our Physical AI platform, capturing real-world audio and mechanical signals to enable always-on sensing and on-device intelligence. With over 25 billion units shipped to date, our sensors are deployed across diverse consumer, automotive, and industrial applications ranging from voice control and voice communication to full-fidelity audio capture.

Our SiSonic MEMS microphone product lineup includes digital and analog microphones and automotive-grade microphones. Our digital and low power sensor capabilities enable tighter integration with downstream neural processing and software, differentiating our offerings from standalone alternatives, and supporting our broader Physical AI platform strategy. Our microphones are sold both on a standalone basis and as part of our optimized Physical AI platform along with our NDPs and AI software models.

Together, our sensor offerings are organized into distinct product families that aim to address the requirements of consumer, automotive, and industrial Physical AI applications.

Our future sensor roadmap includes high-performance, high SNR, low-power, compact, and environmentally rugged microphones designed to support increasingly demanding Physical AI applications. Our sensor business is supported by an operating model that includes in-house design of key MEMS, ASIC, and packaging elements, as well as internally managed assembly, test, and global logistics operations. We believe this full-stack approach, combined with the breadth of our sensor portfolio and deployment history, creates a competitive strength.

Syntiant AI Software Models, Modeling Platform, and Inference Software Development Kit

Our AI model portfolio is organized into four product pillars that we designed for on-device deployment:

Our Syntiant Modeling Platform provides the ability for customers to optimize models for their own applications through a single developer interface for data ingestion and management, labeling, annotation, model training, testing, and deployment. The platform currently supports development for vision and audio models, with support for customer training and optimization of natural language models, expanded physical model types, and multi-modal model development, including vibration, inertial, ultrasonic, radar, and other modalities. System developers can either export generic models for continued development in their own simulation environments, or export platform-optimized models for a specific hardware platform using Syntiant’s Inference SDK.

Our Syntiant Inference SDK is designed to enable customers to seamlessly integrate and deploy our AI models into their end products via a standardized, easy-to-use interface.

Our Market Opportunity

Our technology and product portfolio target several segments within the broader Physical AI ecosystem, including AI processors, MEMS microphones, and AI development tools and software models used for real-world Physical AI endpoints. In

aggregate, we estimate that our total addressable market within Physical AI will grow from approximately $7 billion in 2025 to $22 billion in 2030 at a CAGR of 27%.

Estimated Physical AI Total Addressable Market

Graph created by Syntiant Corp. based on Gartner Research and Next Move Strategy Consulting. Calculations performed by Syntiant Corp. Source: Gartner Forecast: AI Processing Semiconductors, Worldwide, 2024-2030, 1Q26. Forecast: Semiconductors and Electronics, Worldwide, 2024-2030, 1Q26, Rajeev Rajput et al., 26 March 2026 Revenue basis.

Within our total addressable market, we are positioned to address several segments that are experiencing growth as AI capabilities expand into Physical devices.

We have designed our solutions across NDPs, MEMS microphones, and AI software models for Physical AI applications to address a broad range of end markets. These capabilities support scalable deployment of Physical AI across consumer electronics, automotive platforms, industrial and robotic systems, and healthcare and medical devices, where systems must continuously sense, interpret, and respond to real-world environments with minimal reliance on centralized cloud infrastructure.

As AI capabilities continue to expand across consumer electronics, automotive systems, industrial devices, and the healthcare industry, we expect demand for Physical AI processors, sensing technologies, and embedded machine learning models to increase.

Our Competitive Strengths

Our Strategy

Our strategy is to scale our Physical AI platform by leveraging our established sensing footprint as a distribution and integration advantage, enabling us to expand adoption of on-device intelligence within our existing customer base while broadening our reach across additional workloads, applications, and end markets. By engaging early in the customer design cycle and operating adjacent to core sensing components, we enable customers to incrementally add intelligence to products that already incorporate our sensors through existing components, increasing functionality and content per device while minimizing system redesign, qualification risk, and integration complexity.

Leverage Our Sensing Footprint to Drive Physical AI Adoption

We had over 100 customers as of March 31, 2026 and leverage our established sensing footprint and long-standing customer relationships as the entry point for higher-value Physical AI offerings. Our sensors are deeply embedded in customer platforms and positioned early in the design cycle, allowing us to introduce additional on-device intelligence with minimal technical friction and

limited disruption to existing system architectures. Our customer base has expressed significant AI interest, with many of our customers engaged in evaluating our AI products for their use.

Expand Value per Customer Device, Drive Increase in Dollar Content and Enable Margin Expansion

Our layered platform architecture creates a natural monetization progression opportunity within each customer deployment. Sensors establish the initial footprint, neural processors add on-device intelligence at higher average selling prices (“ASPs”), and AI software models layer incremental value with limited additional hardware complexity. We believe this progression can increase revenue per device, shift product mix toward higher-value offerings, and deepen our integration within customer product architectures—creating platform stickiness that supports long-term customer retention. Our integrated solutions are expected to generate gross margins materially higher than sensors alone. Below is an illustration of our dollar content expansion opportunity on a cumulative basis, as sensor customers incorporate additional capabilities from our AI offerings.

For illustrative purposes only. Actual bill of materials (“BOM”) content will differ and vary by, among other things, device category, customer configuration, and deployment timeline.

Capture Greater BOM Share Through Expanding Neural Processor Capabilities

We are expanding the functionality of our neural decision processors and associated software to take on a greater role in device-level intelligence over time. As neural processor functionality expands, we expect customers to increasingly consolidate intelligence that would otherwise be distributed across multiple discrete components, such as MCUs, enabling simpler system designs, lower overall power consumption, and greater system-level efficiency, while increasing our participation in the overall device bill of materials.

Expand Our Platform Across Additional Workloads, Applications, and End Markets

We are expanding our integrated platform of sensors, neural processors, and AI software across audio, vision, motion, and sensor fusion workloads which we believe will broaden our addressable market and increase our relevance across a wider range of intelligent endpoint applications and emerging Physical AI markets. We have seen high interest in markets such as defense, robotics, and autonomous vehicles that we were not addressing two years ago.

Selective Acquisitions to Strengthen Our Platform

We have used acquisitions to enhance our technology capabilities, expand our product portfolio, and deepen our customer relationships. For example, we have completed three acquisitions in the last two years, including the acquisition of the CMM business of Knowles Corporation. We expect to continue evaluating selective acquisition opportunities that can strengthen our platform, broaden our market reach, and support our long-term growth strategy.

Indebtedness and Profitability

Since inception, we have incurred cumulative net losses from operations, including net losses of $60.9 million and $25.7 million for the years ended December 31, 2025 and 2024, respectively, and net losses of $20.9 million and $14.1 million for the three months ended March 31, 2026 and 2025, respectively. As of March 31, 2026, our accumulated deficit was $215.5 million. We have significant debt obligations as of March 31, 2026, including $50.0 million of senior secured term loans outstanding pursuant to a Loan and Security Agreement with Structural Capital maturing in December 2028 and $6.0 million of secured subordinated loans outstanding from Knowles Corporation maturing in March 2029. See “Management’s Discussion and Analysis of Financial Condition and Results of Operations—Liquidity and Capital Resources.”

Recent Developments

Preliminary Estimated Selected Financial Results for the Six Months Ended June 30, 2026

Included below are certain preliminary estimated selected financial results of Syntiant Corp. for the six months ended June 30, 2026 and actual unaudited financial results for the six months ended June 30, 2025. The results for the six months ended June 30, 2026 are preliminary and subject to change, and there is a possibility that our actual results may differ from these preliminary estimates.

The preliminary estimated financial results included in this prospectus have been prepared by, and are the responsibility of, our management. PricewaterhouseCoopers LLP has not audited, reviewed, examined, compiled, nor applied agreed-upon procedures with respect to the preliminary estimated financial results and, accordingly, PricewaterhouseCoopers LLP does not express an opinion or any other form of assurance with respect thereto. The PricewaterhouseCoopers LLP report included in this document relates to the Company’s previously issued financial statements. It does not extend to the preliminary estimated financial results and should not be read to do so. These preliminary estimated financial results should not be viewed as a substitute for financial statements prepared in accordance with U.S. generally accepted accounting principles. Moreover, these preliminary estimated financial results have been prepared solely on the basis of currently available information by, and are the responsibility of, management. It is possible that we or our independent registered public accounting firm may identify items that would require us to make adjustments to the preliminary estimates set forth below as we complete our financial statements and that our actual results may differ from these preliminary estimates. Accordingly, we have provided ranges, rather than specific amounts, for the preliminary estimated financial results described below. Except as required by law, we undertake no obligation to update or supplement the preliminary estimated financial results below until we release our results of operations for the six months ended June 30, 2026, which will not occur until after this offering is completed. These preliminary estimates are not necessarily indicative of results to be expected for the full year ending December 31, 2026 and should be read together with “Risk Factors,” “Special Note Regarding Forward-Looking Statements,” “Management’s Discussion and Analysis of Financial Condition and Results of Operations,” our audited consolidated financial statements included elsewhere in this prospectus and our unaudited interim consolidated financial statements included elsewhere in this prospectus.

(1) Non-GAAP Gross Profit and Non-GAAP Segment Gross Profit (Loss) are non-GAAP financial measures. For definitions of these non-GAAP financial measures and further information about the limitations of the use of non-GAAP financial measures, see the section titled “Management’s Discussion and Analysis of Financial Condition and Results of Operations—Non-GAAP Financial Measures.”

Risk Factors Summary

Our business is subject to a number of risks and uncertainties of which you should be aware before making a decision to invest in our Class A common stock. These risks are more fully described in the section titled “Risk Factors” and elsewhere in this prospectus. These risks include, among others, the following:

Corporate Information

We were incorporated in April 2017 as a Delaware corporation. Our principal executive offices are located at 7555 Irvine Center Drive, Suite 200, Irvine, California 92618, and our telephone number is (949) 774-4887. Our website address is www.syntiant.com. Information contained on, or that can be accessed through, our website does not constitute part of this prospectus, and the inclusion of our website address in this prospectus is an inactive textual reference only.

Implications of Being an Emerging Growth Company

We are an emerging growth company as defined in the Jumpstart Our Business Startups Act of 2012 (the “JOBS Act”). We will remain an emerging growth company until the earliest of: (i) the last day of the fiscal year following the fifth anniversary of the completion of this offering; (ii) the last day of the fiscal year in which we have total annual gross revenue of at least $1.235 billion; (iii) the last day of the fiscal year in which we are deemed to be a “large accelerated filer” as defined in Rule 12b‑2 under the Securities Exchange Act of 1934, as amended (the “Exchange Act”), which would occur if the market value of our common stock held by non‑affiliates exceeded $700.0 million as of the last business day of the second fiscal quarter of such year; or (iv) the date on which we have issued more than $1.0 billion in non‑convertible debt securities during the prior three‑year period. An emerging growth

company may take advantage of specified reduced reporting requirements and is relieved of certain other significant requirements that are otherwise generally applicable to public companies. As an emerging growth company:

In addition, the JOBS Act provides that an emerging growth company can take advantage of an extended transition period for complying with new or revised accounting standards. This provision allows an emerging growth company to delay the adoption of some accounting standards until those standards would otherwise apply to private companies. We have elected to use the extended transition period for any other new or revised accounting standards until the date that we are no longer an emerging growth company or affirmatively and irrevocably opt out of the extended transition period. As a result, our financial statements may not be comparable to companies that comply with new or revised accounting pronouncements as of public company effective dates.

THE OFFERING

In this prospectus, the number of shares of our common stock to be outstanding after this offering is based on shares of our Class A common stock and shares of our Class B common stock outstanding as of March 31, 2026, after giving effect to the Preferred Stock Conversion, the Common Stock Reclassification, and the Class B Stock Exchange (each as defined below), and excludes:

Except as otherwise indicated, all information in this prospectus assumes or gives effect to the following as of March 31, 2026:

SUMMARY CONSOLIDATED FINANCIAL DATA

The following tables set forth our summary consolidated financial data. The summary consolidated statements of operations data for the years ended December 31, 2025 and 2024 have been derived from our audited consolidated financial statements included elsewhere in this prospectus. The summary consolidated statements of operations data for the three months ended March 31, 2026 and 2025 and the summary consolidated balance sheet data as of March 31, 2026 have been derived from our unaudited interim consolidated financial statements included elsewhere in this prospectus. In our opinion, the unaudited interim consolidated financial statements have been prepared on a basis consistent with our audited consolidated financial statements and, in our opinion, contain all adjustments, consisting only of normal and recurring adjustments, necessary for a fair presentation of such interim financial statements. Our historical results are not necessarily indicative of our future results, and our results for the three months ended March 31, 2026 are not necessarily indicative of results that may be expected for the year ending December 31, 2026 or any future period.

You should read the following summary consolidated financial data in conjunction with the section titled “Management’s Discussion and Analysis of Financial Condition and Results of Operations” and our consolidated financial statements and related notes included elsewhere in this prospectus. The summary consolidated financial data in this section are not intended to replace, and are qualified in their entirety by, the consolidated financial statements and related notes.

We define non-GAAP Gross Profit and non-GAAP Segment Gross Profit as our gross profit adjusted to exclude expenses not directly attributable to gross profit, such as amortization of acquired intangible assets, inventory fair value adjustments, and CMM factory transition costs. Inventory fair value adjustments reflect the elimination of the excess of fair value of acquired inventory over its cost basis. CMM factory transition costs consist of incremental facilities and moving expenses specific to the transition of our sensors manufacturing facility in Malaysia from a shared facility with Knowles Corporation to a new location, as required under the terms of the acquisition of the Consumer MEMS Microphone business acquired from Knowles Corporation. CMM factory transition costs ceased during the quarter ended June 30, 2026 and are not expected to recur. We define Adjusted EBITDA as our net loss adjusted to exclude expenses not directly attributable to the performance of our operations, such as income taxes, interest income and expense, depreciation and amortization, inventory fair value adjustments, stock-based compensation, severance costs, impairment of property and equipment, CMM factory transition costs, and transaction costs.

We believe that these non-GAAP financial measures provide useful information to users of our financial statements in understanding and evaluating our results of operations in the same manner as our management team. In particular, we believe that these metrics offer users additional tools to analyze various elements of our operational performance through time by excluding items that vary for reasons unrelated to operating performance. We believe the presentation of non-GAAP Gross Profit, non-GAAP Segment Gross Profit, and Adjusted EBITDA in this prospectus provides investors with additional tools to use in comparing our core business and results of operations over multiple periods with other companies in our industry, many of which present similar non-GAAP financial measures to investors. However, our calculation of these non-GAAP measures may not align with similarly titled measures presented by other companies due to differences in the way that these measures are calculated, limiting comparability across companies.

Non-GAAP Gross Profit, non-GAAP Segment Gross Profit, and Adjusted EBITDA have limitations and should not be considered as the sole measures of our performance and should not be considered in isolation from, or as a substitute for, gross profit and net loss calculated in accordance with GAAP.

The following tables reconcile the most directly comparable GAAP financial measures to each of these non-GAAP financial measures.

Non-GAAP Gross Profit

For the three months ended March 31, 2026 (in thousands):

For the three months ended March 31, 2025 (in thousands):

For the year ended December 31, 2025 (in thousands):

For the year ended December 31, 2024 (in thousands):

Adjusted EBITDA

For the years ended December 31, 2025 and 2024 and three months ended March 31, 2026 and 2025 (in thousands):