PARIS, FRANCE, le 1er mars 2023 — SESAMm, leader du traitement automatique des langues, un domaine de pointe de l'intelligence artificielle, a annoncé aujourd'hui la clôture d'une levée de fonds en série B2 de 35 millions d'euros (37 millions de dollars US). L’objectif : accélérer sa croissance et son développement international.

Cette opération permettra à SESAMm de poursuivre son expansion notamment aux Etats-Unis et en Asie, et de soutenir ses développements technologiques en intelligence artificielle pour l’analyse ESG (critères Environnementaux, Sociaux et de Gouvernance des entreprises) et de sentiment. En complément, cette levée de capitaux permettra de recruter des talents clés en ESG, développement informatique et intelligence artificielle, ventes et marketing.

Ce nouveau tour de table a été mené conjointement par Elaia, une société de venture capital spécialisée dans la deep tech, et Opera Tech Ventures, fonds de capital-risque de BNP Paribas (BNPP). Cette levée de fonds bénéficie également de la participation du gestionnaire d'actifs Unigestion, de la banque Raiffeisen Bank International (RBI) à travers son entité de venture capital Elevator Ventures, d’AFG Partners, de CEGEE Capital ainsi que des investisseurs historiques de SESAMm, incluant Carlyle (CG) et New Alpha Asset Management, qui ont participé à la précédente série B1. Cette opération porte le total des fonds levés par SESAMm à 50 millions d'euros.

"Nous sommes ravis de soutenir les ambitions de SESAMm, qui exploite une technologie de pointe pour créer des données d’analyse et de suivi de tendances ESG. Nous sommes très impressionnés par la qualité et l’expertise de son équipe de direction, opérant d’ores et déjà  sur tous les continents et pour des clients institutionnels de premier rang au niveau mondial.

La vision d’apporter ces indicateurs ESG technologiques à l’industrie financière et aux grandes entreprises représente un réel changement de paradigme et nous sommes très heureux de faire partie de cette aventure aux côtés de l’équipe de SESAMm", a déclaré Pauline Roux, Partner chez Elaia.

"Dans un contexte où il est de plus en plus critique d’accompagner la prise de décision grâce à des sources de données pertinentes, nous avons trouvé le produit de SESAMm très efficace pour aider à identifier, filtrer et évaluer des informations clés, tant sur de petites sociétés privées que sur des plus grandes entreprises. Nous partageons pleinement la vision de son équipe et sommes très fiers de soutenir SESAMm dans son développement", a déclaré Thibaut Schlaeppi, Managing Director d'Opera Tech Ventures.

SESAMm est un fournisseur de données leader dans l’utilisation du traitement automatique des langues, et qui compte parmi ses clients les plus grandes sociétés de private equity, banques et gestionnaires d’actifs, ainsi que des entreprises de tous secteurs. Grâce à son data lake de plus de 20 milliards de documents, en croissance de 20 % chaque année, SESAMm fournit des données et technologies pour générer des analyses innovantes. Les cas d'utilisation sont multiples et incluent la détection de controverses, les scores réputationnels, les indicateurs ESG et ODD (Objectifs de Développement Durable), le due diligence d'investissement ou encore le suivi automatisé de fournisseurs.

"Depuis que nous avons commencé à travailler avec SESAMm en tant qu’investisseur et client il y a plus de deux ans, nous avons été impressionnés à la fois par la croissance de l'entreprise et par ses indicateurs de pointe qui ont appuyé nos processus de recherche de sociétés, de due diligence et de création de valeur pour les sociétés de notre portefeuille", a déclaré Matt Anderson, Chief Digital Officer de Carlyle. "Nous sommes ravis de renforcer notre partenariat avec SESAMm en participant à ce nouveau tour de table."

Le CEO de SESAMm Sylvain Forté, son COO Pierre Rinaldi et son CTO Florian Aubry ont cofondé SESAMm en 2014. Avec leur équipe de près de 100 experts de la donnée, ils travaillent sur de grandes quantités d'informations textuelles issues du web, des sites d'actualités aux rapports d’ONG en passant par les médias sociaux, pour les transformer en puissantes informations pertinentes et rapidement exploitables.

Sylvain Forté, CEO et cofondateur de SESAMm, a partagé: "Nous sommes heureux et reconnaissants d’avoir finalisé cette levée de fonds de 35 millions d'euros pour poursuivre notre croissance et nous étendre sur de nouveaux marchés internationaux tels que Singapour. Lever ce montant important dans des conditions de marché tendues est une validation supplémentaire de la pertinence des deux tendances clés qui sont au cœur de SESAMm : l'intelligence artificielle et l’ESG. Nos outils permettent ainsi à toutes les entreprises de prendre de meilleures décisions et de combler les manques de données, notamment dans l’ESG, sur les entreprises publiques et privées."

À propos de SESAMm

SESAMm est une société d'intelligence artificielle de premier plan, au service des sociétés d'investissement et des entreprises du monde entier. SESAMm analyse plus de 20 milliards de documents en temps réel afin notamment de détecter automatiquement les controverses sur les investissements, les clients et les fournisseurs, calculer des scores ESG et d'impact positif, améliorer le due diligence et le sourcing en private equity et analyser le sentiment sur les actifs financiers.

À propos d'Elaia

Elaia est une société de venture capital spécialisée dans le financement des entreprises technologiques en phase d'amorçage. La société se focalise sur les startups des secteurs du logiciel, du web et des médias numériques et a démontré sa capacité à soutenir des entreprises devenant performantes et rentables. L'équipe d'Elaia est composée d'investisseurs et d'entrepreneurs expérimentés qui apportent des conseils et un soutien précieux aux startups de leur portefeuille. En s'attachant à aider les entreprises à croître et se développer, Elaia est un partenaire de choix pour tout entrepreneur qui souhaite lancer une entreprise technologique.

À propos d'Opera Tech Ventures de BNP Paribas

Opera Tech Ventures est la branche VC de BNP Paribas, lancée en 2018 avec l'objectif d'investir dans des startups qui transforment l'industrie financière. Le fonds est géré par BNP Paribas Asset Management France, au sein de sa division Private Assets, dédiée à la gestion d'actifs privés. Avec une dimension globale, Opera Tech Ventures soutient les entrepreneurs qui construisent des entreprises ambitieuses, de la série A à la série C, avec des investissements allant de 3 à 15 millions d'euros.

À propos de Carlyle

Carlyle (NASDAQ : CG) est une société d'investissement mondiale possédant une expertise sectorielle approfondie et qui déploie des capitaux privés dans trois secteurs d'activité : Global Private Equity, Global Credit et Global Investment Solutions. Avec 373 milliards de dollars d'actifs sous gestion au 31 décembre 2022, l'objectif de Carlyle est d'investir judicieusement et de créer de la valeur au nom de ses investisseurs, des sociétés de son portefeuille et des communautés dans lesquelles nous vivons et investissons. Carlyle emploie plus de 2 100 personnes dans 29 bureaux répartis sur cinq continents. Pour plus d'informations, consultez le site www.carlyle.com. Suivez Carlyle sur Twitter @OneCarlyle.

Contact média

Danny Calogero
dcalogero@fullyvested.com
+44 7891 563 294

At SESAMm, we have always been at the forefront of utilizing artificial intelligence (AI) for Environmental, Social, and Governance (ESG) risk analysis. Our journey began in 2014, leveraging natural language processing (NLP) to analyze vast amounts of data to identify company risks - from public equities to expanding into private assets. Our technology stack, deeply rooted in AI-first principles, has evolved significantly over the years, incorporating deep learning and large language models since their inception.

2023 marked a significant leap in generative AI, breaking data quality and precision barriers. Our ability to attain human-level precision in ESG control detection across dozens of languages is a testament to our commitment to continual technological advancement. We plan to further integrate generative AI into our processes, enhancing data quality and expanding our coverage, especially for smaller firms and infrastructure projects that often go unnoticed in the market.

Enhancing User Interaction with AI: The Future of Client Engagement

A significant focus for SESAMm in 2024 is transforming how clients interact with our SaaS platform. Last year, we introduced a prototype of our generative AI chatbot, which marked the beginning of a new era in user interaction. Our goal is to utilize generative AI not just for data improvement but to enhance the overall user experience. This includes making data more accessible and addressing challenges like the lack of unique identifiers in the private space.

Accelerating Innovation and Development with AI

Our team of 60 engineers and researchers is the driving force behind our rapid development and innovation. The integration of large language models has enabled us to deliver features more quickly and efficiently. With a robust infrastructure and an agile AI team, we're pushing the boundaries of what's achievable with AI, outpacing traditional human analyst capabilities. This agility in innovation is crucial for our continued leadership in the market.

The Future of AI Technologies in SESAMm's Roadmap

We keep a close eye on the latest AI advancements, from new open-source libraries to cutting-edge commercial models. Our team is deeply engaged in fine-tuning models for specific applications, especially in tracking ESG events and uncovering insights about smaller, lesser-known companies. This approach reflects our culture – an AI-first company specializing in the financial and ESG space, always eager to incorporate more technology and innovate.

Cementing Leadership in AI-Powered ESG Analysis

Our vision extends beyond 2024, aiming to be the premier global player in ESG controversy and risk analysis. With regulations evolving globally, we anticipate a significant expansion in our market. Our focus remains on delivering unparalleled reputational insights, especially in the investment world, but also rapidly expanding into supply chain analysis and client monitoring. We're not just keeping pace with the market but setting the standard for AI-powered ESG analysis.

The Expanding Global ESG Landscape: Opportunities and Challenges

As regulations intensify worldwide, including in polarized markets like the US and Asia, we see a growing demand for ESG analysis. This global shift presents both opportunities and challenges. Our strategy includes further integrating our solutions into various ecosystems, such as ESG reporting tools, portfolio management systems, and CRMs. Through strategic partnerships, we aim to position SESAMm as an integral part of the global ESG analysis framework.

Walking the Talk: SESAMm's Commitment to Sustainability

In 2024, SESAMm is not just about leading in technology; we're deeply committed to practicing what we preach regarding sustainability. We have a robust ESG Manifesto and a series of actions aligned with environmental, social, and governance principles. Recognizing our relatively limited carbon footprint as a tech company, we focus on making impactful choices and fostering a culture of awareness and change within our team.

We've implemented programs for environmental awareness, like our Climate Risk training, conducted by certified employees. Our efforts in governance are highlighted by the appointment of our first independent board member, Stephane Beson, signifying our dedication to having diverse external perspectives guiding our company.

Regarding environmental footprint, we prioritize partnering with providers that use clean energy in their data centers. This conscious decision-making extends to selecting partners who can offset their carbon emissions, reflecting our commitment to sustainability.

Remote Work and Sustainability: A Dual Focus

Our approach to remote work has always been progressive. We see it as not just a productivity enhancer but also as a key sustainability strategy. Given our global presence, with teams in France, New York, Tunisia, and London, remote work is essential. It brings our team closer, transcends cultural barriers, and reduces our carbon footprint by significantly cutting down on travel. This strategy aligns with our dedication to work-life balance, recognizing the importance of flexibility for our employees.

Targeting the Right Market with Tailored Services

2024 is a year of strategic focus for SESAMm, especially in terms of our target market. We're seeing a growing trend among banks to aggregate ESG controversy data. Our unique capability to provide comprehensive coverage, encompassing mixed assets portfolios, positions us as a key player for these institutions.

We continue strengthening our presence in private equity and expanding our reach into the banking sector, private debt, and infrastructure funds. Our dedicated corporate business practice is another area of expansion, helping European companies monitor ESG controversies. We also focus on sustainability and ESG teams, procurement teams, and third-party risk teams, ensuring a broad yet targeted market approach.

Upholding Data Security and Privacy

Data security and privacy are paramount, especially given the increasing sophistication of cyber threats. Our co-founder and CTO brings invaluable expertise in cybersecurity. We conduct annual audits, have robust systems to monitor and preempt attacks, and continuously train our teams to be vigilant. While we don't deal with personal data, we focus on protecting critical systems for our clients, ensuring that we maintain the highest standards in data security.

Building a Team for the Future

Looking ahead, our confidence in 2024 stems from our team's exceptional capabilities. We've implemented agile processes and onboarded talented individuals across all levels. Our team's passion and dedication are key drivers in adapting to market changes and delivering high-quality services. It's not just about where SESAMm is now but how our team will continue to excel and innovate in the future.

A Vision of Innovation and Responsibility

As we move into 2024, SESAMm stands at the forefront of AI-powered ESG analysis, not just through technological innovation but also through a steadfast commitment to sustainability and security. Our focus on the right markets, combined with a forward-thinking approach to remote work and data protection, positions us to meet the evolving needs of our clients.

Join us at SESAMm as we navigate the future of ESG analysis, leveraging our expertise to foster a sustainable, secure, and innovative business environment. Explore SESAMm's cutting-edge solutions and be part of a future where technology meets responsibility.

SESAMm’s AI Technology Reveals ESG Insights

Discover unparalleled insights into ESG controversies, risks, and opportunities across industries. Learn more about how SESAMm can help you analyze millions of private and public companies using AI-powered text analysis tools.

In the digital age, data proliferates at an astonishing rate. From news articles to social media posts, the information explosion presents unique challenges in processing and understanding content accurately. One significant challenge is distinguishing between entities with similar or identical names in different contexts. named entity disambiguation (NED) is a sophisticated technology within natural language processing (NLP) aimed at tackling this issue. This technology ensures that when you search for "Orange," the results accurately reflect whether you meant the color, the fruit, or the multinational corporation. This article explores the concept of NED, underscores its importance, and elaborates on how SESAMm employs this technology to stand out from other companies in the artificial intelligence (AI) landscape.

What Is Named Entity Disambiguation?

Named Entities: Defining the Basics

In data science and text processing, a named entity is defined as any real-world object that can be denoted with a proper name. This includes people like "Elon Musk," companies like "Google," and landmarks like "Mount Everest." These entities are distinct because they refer to unique individuals, organizations, or locations, unlike common nouns such as "manager" or "river," which are non-specific and can refer to many different entities globally.

Named Entities: Defining the Basics

Named Entity Disambiguation, also known as entity linking, involves identifying which specific entity is referred to in an unstructured text when there are multiple candidates with similar names. This process utilizes a blend of machine learning, knowledge graphs, and other sophisticated NLP algorithms to analyze the text and determine which entity type is relevant in the given context. This determination is important because it affects the interpretation and subsequent processing of the information.

The Importance of Named Entity Disambiguation

The role of NED in text analysis and information processing cannot be overstated, particularly when dealing with large and complex datasets. It enables:

• Refined text analytics: For tasks like sentiment analysis, precise entity recognition ensures that emotions or sentiments are accurately associated with the right entities. This is crucial for businesses to understand public perceptions of their products or services accurately.
• Efficient construction of knowledge graphs: Knowledge graphs that organize and link real-world information rely heavily on NED to accurately populate and update their data. This accuracy is essential for applications like digital assistants, which use these graphs to provide informed responses to user inquiries.

The Importance of Named Entity Disambiguation

NED is a complex process that involves multiple steps and methodologies to accurately identify and link named entities in a given text to their correct real-world counterparts.

1. Identifying Named Entities

Before disambiguation can occur, named entities must first be identified within a text. This is typically done using named entity recognition (NER), a preliminary step that involves scanning text data to locate and classify entities into predefined categories such as person names, organizations, locations, dates, and other specific information.

Techniques Used in NER

• Rule-based systems: These utilize patterns and linguistic rules, such as capitalization or context indicators (e.g., titles like Mr. or corporate designators like Inc.), to identify entities.
• Statistical methods: Techniques like Hidden Markov Models (HMMs) or Conditional Random Fields (CRFs) learn from large datasets of annotated text to recognize entities based on probabilistic models.
• Deep learning approaches: More recently, models based on neural networks, particularly those using architectures like LSTM (Long Short-Term Memory) or transformers, have become prevalent. These models benefit from large amounts of training data and have shown superior ability to capture context for more accurate entity recognition.

2. Categorizing Named Entities

Once entities are identified, they need to be categorized accurately. This involves classifying each entity according to its type, which helps in narrowing down the possible meanings in the subsequent disambiguation step.

Methods for categorization

• Fine-grained classification: Beyond basic categories, entities can be classified into more specific classes, such as distinguishing between types of organizations (e.g., non-profit vs. corporate) or public figures (e.g., politician vs. artist).
• Contextual classification: It involves analyzing the surrounding text to understand an entity's role and relevance, using both the immediate context and broader discourse.

3. Disambiguating Named Entities

The core of NED lies in its ability to distinguish between entities that share the same name. This step is critical because it determines the accuracy of information extraction, search engines, knowledge graph construction, and other NLP applications.

Core Techniques in Disambiguation

• Rule-based disambiguation: Applies heuristic rules based on linguistic cues and patterns, such as geographical proximity or typical associations (e.g., Apple might be linked to "technology" if the context involves words like "iPhone" or "MacBook").
• Machine learning models: Supervised learning models are trained on datasets where each entity is annotated with its correct reference. These models learn to predict the correct entity based on features extracted from the context.
• Unsupervised and semi-supervised methods: These involve clustering similar entities and using algorithms to predict the most likely meaning based on the densities of clusters and the contextual similarity.
• Knowledge-based approaches: Utilize large external databases or knowledge graphs that contain information about entities and their relationships. By querying these resources, NED systems can pull contextual information and metadata to resolve ambiguities. For example, linking to a specific Wikipedia page can clarify whether "Jordan" refers to the country, the river, or the basketball player, based on the context.

4. Linking Entities to External Databases or Knowledge Graphs

The final step in NED is often linking the disambiguated entity to a unique identifier in an external database or a node in a knowledge graph. This linkage not only confirms the entity’s identity but also enriches the text with semantic information that can be used for further processing and analysis.

Linkage methods

• URI Assignment: Each entity is assigned a unique resource identifier (URI) that points to a specific location in a database or a knowledge graph.
• Semantic tagging: Entities are tagged with semantic labels that provide additional metadata, enhancing the richness of the data for subsequent analytical tasks.

The combination of these techniques ensures that NED systems can operate with high accuracy and efficiency, making them indispensable in the field of NLP. By understanding and implementing these processes, SESAMm enhances its analytical capabilities, offering precise and context-aware solutions that stand out in the competitive AI landscape.

SESAMm's Innovative Approach to NED

SESAMm has carved a niche in the NLP field by incorporating advanced, proprietary technologies that refine and enhance the NED process:

• Cutting-edge algorithms: SESAMm develops and deploys state-of-the-art machine learning approaches and deep learning algorithms designed to increase the precision and reliability of entity disambiguation.
• Scalable data processing: SESAMm's platforms are engineered to handle extensive data volumes, making them well-suited for large-scale industrial applications that require robust data analysis capabilities.
• Customizable APIs: SESAMm offers adaptable APIs that clients can tailor to fit specific project requirements, whether for financial analysis, marketing research, or other specialized areas.
• Seamless knowledge graph integration: By integrating its NED processes with dynamic knowledge graphs, SESAMm enhances its semantic analysis capabilities, enabling deeper insights and more accurate data interpretations.

Conclusion

Named Entity Disambiguation is a fundamental component of modern NLP applications, essential for interpreting the enormous volumes of data generated daily. By accurately identifying and categorizing named entities, NED not only deepens the understanding of text but also improves the efficiency of information processing. SESAMm's approach to NED sets it apart in the AI analytics field, pushing the boundaries of what's possible with smart, context-aware technology solutions. To learn more about SESAMm’s innovative technology and how it is used to identify ESG controversies, request a demo.

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