Sansan, Inc.

Thank you for joining our earnings presentation. I am Hashimoto, the CFO of Sanson, Inc. Today, I will present our Q2 and H1 results for the fiscal year ending May 31st, 2026, FY 2025, and our full year forecasts. CEO Tirada will discuss our growth strategies later. This is about the H1 fiscal year 2025 results. I will explain three highlights for H1 fiscal year 2025. First, net sales increased by 26.5% year over year, maintaining robust growth. Consolidated ARR reached 45.9 billion yen as of the end of November 2025. Second, adjusted operating profit increased substantially, marking a record high for the first half, despite ongoing investments. Third, we continued high sales growth in Bill 1, the accounting AX solution. Quarterly net increase in MRR has continually grown since bottoming out in Q3 of the previous fiscal year, and new order value over the three months of Q2 reached an all-time high on a quarterly basis since the service launch. I will move on to the results for the three months of Q2. Net sales grew steadily, up 25.1% year over year. Gross profit margin improved by 2.0 percentage points, year over year, primarily due to enhanced operational efficiency in digitizing Bill 1 data. Adjusted operating profits substantially increased by 294.0% year over year, driven by sales expansion, improved gross profit margin, and a decrease in the S, G, and A ratio. As a result, the adjusted operating margin reached 18.7%, marking a record high level on a quarterly basis. Against this backdrop, ordinary profit and other line item profits also increased substantially. I will now explain the details of adjusted operating profit. net sales remained solid, and the gross profit margin increased by 2.0 percentage points year over year due to an improvement in the ratios of cost of sales. Regarding the breakdown of SG and A expenses, personnel expenses increased by approximately 577 million yen year-over-year, but the ratio of personnel expenses decreased by 3.3 percentage points year-over-year. Additionally, the ratio of advertising expenses decreased by 1.4 percentage points year-over-year. Furthermore, rents and relocation expenses decreased by approximately ¥279 million year-over-year. This was due to the absence of expenses related to the head office relocation incurred in the same period a year ago, and the elimination of double rent payments at both the old and new locations. As a result, the SG and A ratio decreased by 11.3 percentage points year-over-year, leading to a significant increase in adjusted operating profit. Here's the overview by segment. Both the Sansan slash Bill 1 business and the 8 business reported steady net sales growth and a significant increase in adjusted operating profit. This slide shows detailed segment information. Net sales for the Sansan Bill 1 business increased by 25.8% year over year. By solution, Sansan grew 18.8% year over year, maintaining the same level of growth as in Quarter 1. Bill 1 grew 40.1% year-over-year, continuing its high growth rate. Others includes the performance of Contract 1 and the group company, Nine Out Incurs, both of which are growing steadily, of which Contract 1 sales increased by 93.6% year-over-year, and the number of subscriptions reached 569, a 99.0% increase year-over-year. As a result, adjusted operating profit increased significantly, rising 287.4% year-over-year. By solution, Sansan grew 27.5% year-over-year, while Bill 1 recorded a loss of approximately 298 million yen, representing an improvement of approximately 1.3 billion yen year-over-year. Here's an explanation of KPIs for SanSan. The left side of the slide shows recurring sales with a solid 17.4% year-over-year increase. In the center of the slide, the number of SanSan subscriptions increased by 12.1% year-over-year, with a net quarterly increase of 366 subscriptions, accelerating growth from quarter one. Additionally, the monthly recurring sales per paid subscription increased by 4.5% year-over-year, showing stable performance. The last 12 months' average of monthly churn rate, shown on the right side of the slide, increased slightly, primarily due to the higher churn rate in the previous fiscal year's Q4. However, subsequent churn rates since then have remained stable at a low level below 1%, posing no particular concern. For reference, I will explain the new order situation for Q2. The new order value shown here reflects reductions due to cancellations, and is close to the new recurring sales that will be added going forward. This includes orders already reflected in Q2 sales, as well as orders beginning to be recognized in Q3 and beyond. In Q2 of the previous fiscal year, new order value reached a record high quarterly level due to a concentration of orders from mid-sized and large enterprises with higher unit prices. In Q2 of the current fiscal year, new order value decreased 14.2% year-over-year but increased 13.1% quarter-over-quarter and remains at a high level. Here's an explanation of the KPIs for Bill 1. On the left side of the slide, MRR reached approximately 1,069 million yen in November 2025, up 37.3% year over year. While the quarterly net increase has continuously grown at a moderate pace, this quarter's net increase of approximately 83 million yen marks a robust performance, surpassing that level for the first time in a year. In the center of the slide, the number of paid subscriptions increased by 38.9% year-over-year, with a net quarterly increase of 347 subscriptions. This also continues the stable growth of exceeding 300 subscriptions per quarter since Q3 of the previous fiscal year. Meanwhile, monthly recurring sales per paid subscription decreased by 1.3% year-over-year due to an increase in acquiring smaller customers. However, it remains at a high level, so it is not a situation of particular concern. The last 12-month average of monthly churn rate, shown on the right side of the slide, remains low at 0.35%. The new order value for Q2 increased 16.9% year-over-year and 57.8% quarter-over-quarter, reaching a record-high quarterly level since the launch of the business. As a result of strengthening our sales organization and sales approach, not only our invoice receipt service but also our expense reimbursement service has begun to grow steadily, and we view this as further increasing the likelihood of MRR growth from Q3 onward. I will now explain the eight business. Net sales from B2C services remained robust, increasing 11.6% year over year. Net sales from B2B services increased by 27.7% year over year, driven by steady growth in business events and strong growth in recruitment-related services. Furthermore, under business operations focused on profitability, net sales remained robust, resulting in adjusted operating profit increasing significantly by 336.9% year-over-year. I will now discuss the progress of our full-year forecasts. Both net sales and adjusted operating profit through Q2 progressed in line with the full-year forecasts. In particular, adjusted operating profit exceeded our initial expectations for the first half, driven by robust sales growth and progress in cost efficiency. As I have explained, we achieved significant year-over-year profit growth, demonstrating the increasing leverage in our profit generation. We also view this as further solidifying the path toward enhanced profitability outlined in our medium-term financial policy. Meanwhile, from a plan versus actual perspective, the first half results also reflect the impact of certain expenses originally planned for the first half being deferred to the second half. As a result, there has been a change in the profit recognition mix between the first and second halves that we previously communicated. Looking at the full year, our policy remains unchanged. We will continue making the necessary investments to achieve further sales and profit growth in the next fiscal year and beyond. In addition, a portion of the costs not incurred in the first half is expected to be incurred in the second half. Accordingly, there is no change to the full-year earnings forecast at this time. As for our midterm financial policy, performance remains on track, and the figures remain unchanged at this time. That concludes my explanation. Next, Tarada will discuss our growth strategy. I'm Tarada, the CEO. I'll explain our strengths in the generative AI era and our growth strategy. With the rapid adoption of generative AI, we are hearing more skepticism, such as SaaS is dead, and will all SaaS be replaced by AI? At the same time, in actual corporate settings, there are also increasing voices that, despite adopting generative AI, companies are not achieving the results they had expected. Why is that? The reason is very clear. In business, Generative A can only demonstrate its full potential when there is data that is properly structured and normalized and made available for use in close connection with a company's activities. It is the combination of such data and Generative AI that unlocks its true power. Generative AI can easily reference and synthesize publicly available information on the Internet. However, it cannot independently obtain and reference the unique primary information held by a company. For example, information such as who is the counterparty in a sales discussion, what types of invoices have been received for which transactions, and what was agreed in the contract constitutes primary information derived from actual business activities. While generative AI can process and analyze such information, it cannot enter corporate workplaces and obtain it directly. Therefore, in the era of generative AI, we believe that whether a company has a structure that enables the continuous acquisition of primary information will be one of the key criteria that differentiates SaaS competitiveness. From here, I would like to focus on the key reasons why we believe our services will not be replaced by generative AI, but will instead benefit from it as a tailwind for growth. First, let me explain why our services are able to acquire primary information. Fundamentally, from the user's perspective, our services are not simply tools for entering data or creating data for generative AI to use. Rather, they are designed to solve specific practical challenges that arise in companies' day-to-day operations, such as sales, accounting, and legal functions. For example, our services are used to manage customer information for sales activities, to process invoices accurately and efficiently, and to understand contract terms in order to control risk. In this way, they are embedded in daily business operations to improve operational efficiency. As a result, the information users provide to our services constitutes primary information located at the entry point of business processes. With SanSan, this includes information such as who met with whom, which represents the starting point of sales activities and the initial point of contact between a company and its customers, information that underpins the operation of CRM and SFA systems. Bill 1 captures transaction-related information such as amounts and payment terms stated on invoices and receipts, which forms the foundation for all subsequent accounting processes. Contract 1 manages information contained in contracts, which is directly tied to corporate decision-making, and the terms agreed upon have long-term implications for future transactions. This information is unique to each individual company and cannot be obtained by generative AI from publicly available sources. It is, by definition, primary information, because the act of providing primary information through our solutions is deeply embedded in customers' operational workflows. Our solutions are inherently structured to acquire primary information continuously and inevitably. Next, I would like to explain why digitizing primary information itself is difficult using generative AI alone, and to clarify the respective roles of primary information digitization and AI. The majority of the primary information we handle is non-standardized and analog in nature. For example, business cards vary in layout by company and individual, invoices differ in format from one company to another, and contracts are written in free-form text with no fixed structure, including the terms agreed upon. In this way, primary information is inherently unstructured and lacks standardized formats. Generative A, I generally excels at inferring patterns from the large volumes of data, and Texted has learned from in generating optimal responses based on those patterns. At the same time, however, it has a tendency to fill in blanks or missing elements. As a result, when generative AI alone is used for digitization, the generation of incorrect information, so-called hallucinations, is difficult to avoid. Mistakes such as misidentifying field names, numerical errors, or supplementing information that does not exist can be critical in the domains of sales, accounting, and legal operations. In these areas, what is required is not inference, but the digitization of facts themselves. To achieve this level of accuracy, what is needed is A, I trained on primary information created under strict rules, namely, machine learning models that predate generative AI, as well as rule-based logic for deterministic judgment. By leveraging these technologies, patterns can be learned from large volumes of data. and acquired primary information can be structured and normalized. That said, as we have previously explained, it is not possible to achieve near 100% accuracy in digitization without final quality assurance performed by humans. For this reason, an operational model that combines machine learning-based AI with human input is essential. With these premises in mind, I would like to revisit and explain our technology for digitizing analog information. Over many years, we have continuously refined three core elements, AI that evolves through machine learning, standardized digitization rules that support it, and a framework for final quality control performed by humans. Through this process, we have developed pre-generative AI machine learning technologies, such as image processing and natural language processing, as well as proprietary OCR, specialized in reading business cards. These capabilities constitute our competitive advantage, one that remains firmly intact even in today's era of widespread generative AI adoption. Even if, in the future, generative AI becomes more effective in certain aspects of digitization , we believe that companies with a proven track record in delivering highly accurate digitization and rigorous quality control will be best positioned to implement and operate such technologies. In this respect, we see ourselves as having a significant advantage. In fact, we have successfully developed our proprietary generative AI, Viola, which has already had a meaningful impact on reducing digitization costs in recent periods. Going forward, we will continue to advance our data technologies by leveraging the expertise and know-how we have accumulated over many years. From here, I will explain the characteristics and growth strategies of each of our solutions. I will begin with Sansan. When categorizing SaaS offerings along a spectrum from data-oriented to process-oriented, Sansan is positioned as a data- and knowledge-centric SaaS. What Sansan handles is not merely customer contact information, but the context of corporate activities themselves. For example, it digitizes and accumulates primary information that forms the foundation of sales activities such as who met with whom and when, what kind of relationship exists with which company, and which deals progressed based on whose actions. By structuring and normalizing this accumulated information and further enriching it with more than 2.3 million company profiles and other data, We enhance data quality and develop it into foundational data that supports activities across the entire organization. This foundational data is highly compatible with generative AI. As a result, value creation through AI utilization is expanding in areas such as prioritizing sales opportunities and generating proposals based on past experiences. To enable this, we are currently rolling out features such as the SanSan MCP Server and the SanSan AI Agent, with proof-of-concept initiatives already underway at large enterprises. The SanSan MCP Server connects SanSan's business data with external generative AI tools, such as ChatGPT, enabling advanced data utilization. Meanwhile, the Sansan AI agent allows integrated business data to be leveraged through simple instructions, supporting enhanced organizational capabilities in sales and marketing functions. In this way, Sansan is evolving the sales context accumulated within organizations into business data that can be effectively utilized by generative AI. We view these initiatives as important growth drivers in the era of generative AI. Next, I will explain Bill 1. Among our solutions, Bill 1 is positioned as a process-oriented SaaS with a strong focus on improving and automating operational workflows. Its core value does not lie in the secondary use of generated data, but rather in how effectively it can streamline accounting workflows themselves. Against this backdrop, the reason Bill 1 can continue to evolve in the era of generative AI is that it securely captures primary information at the very upstream of the accounting process. In invoice processing, a series of steps, from receiving invoices to creating records, matching line items, approval, and journal entry, are carried out sequentially, and many of these steps are still handled manually today. The reason automation through AI has not progressed as much as expected is clear, and one key factor is the lack of complete and accurate primary data. Bill 1 reliably receives invoices, which constitute primary information, whether in paper or PDF format, and digitizes them with high accuracy. In addition, it has succeeded in digitizing line item details, which had previously been difficult to achieve. With this high-quality primary data in place, we have been able to develop new features, such as AI-powered automatic matching and AI-powered automatic entry, enabling us to access areas that were previously difficult to automate. An important point is that AI-driven automation only becomes viable when both accurate primary information and continuity across business processes are present. By capturing this structure from the entry point, BillOne has begun to evolve into a platform that can automate accounting processes end-to-end, from upstream to downstream, across the entire organization. By combining advanced digitization centered on rule-based logic with machine learning-based AI technologies, such as image recognition and natural language processing, and further integrating generative AI Bill 1 is automating accounting workflows. We view this as representing significant growth potential in the era of generative AI. To conclude, the common element across all of our solutions is that they capture primary information generated at the entry point of core business operations that underpin corporate activities. The digitization, structuring, normalization and utilization of primary information form the essential foundation that enables downstream systems and generative AI to deliver meaningful value. We believe that our ability to consistently acquire, digitize, and leverage primary information represents a strong competitive advantage in the era of generative AI and serves as a core growth platform that we intend to further strengthen going forward. Currently, SanSan, BillOne, and ContractOne are offered as independent solutions. Looking ahead, however, we envision deepening the connectivity among these solutions to a greater extent. Over the longer term, our goal is to move beyond individual functional domains such as sales, accounting, and legal, and to become a business infrastructure that supports everything from the starting point of corporate activities through to decision-making across the organization. That concludes my presentation. Thank you very much for your attention.