Orby has now joined Databricks’ Built On Partner Program and is leveraging Databricks Mosaic AI to pretrain, build, deploy and monitor its innovative Large Action Model, ActIO, a deep learning model able to interpret actions and perform complex tasks based on user inputs.

“As the demand for data intelligence increases, Orby’s AI innovations are a real game changer in enabling enterprise automations that require truly cognizant reasoning,” said Naveen Rao, VP of Generative AI at Databricks.

“Orby’s unique LAM approach gives organizations the ability to complete tasks with increasing complexity and variability, easily automating complex tasks that, until now, just hasn’t been possible or practical,” Rao concluded.

LARGE ACTION MODEL TAKING CENTER STAGE

Unlike conventional Large Language Model (LLMs) approaches, which focus on interpreting language and generating responses, Orby’s unique Large Action Model (LAM), observes actions to automate tasks and make decisions.

Orby’s LAM simply observes a user at work, learns what can be automated, and creates the actions to implement it. Users then approve the process and can modify the actions at any time, this allows continuous improvement as Orby learns more.

Making generative AI truly useful for the enterprise requires incredible amounts of variable inputs to enable rapid contextual reasoning. Today’s open source and proprietary LLMs are trained on massive amounts of data, but of only one modality: language. Other multimodal models may allow for variable inputs but lack the complex planning and visual grounding capabilities necessary to translate these inputs into enterprise-ready actions that reason, adjust, continuously learn and improve. Large Action Models are uniquely suited to empower enterprise efficiency, but first must be trained on massive amounts of data across multiple modalities.

“Databricks Mosaic AI makes it possible to build a multimodal training pipeline at a scale that is essential for delivering unrivaled performance, accuracy and stability,” said Will Lu, Co-Founder and CTO of Orby.

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The autoSCORE application was developed in Norway by Holberg EEG using a deep-learning model and trained on the world’s largest dataset of more than 30,000 expertly labeled EEG recordings. The development and validation of the model have been carefully designed to avoid typical errors and sources of bias, and the output is focused on aspects key to the interpretation of clinical EEGs [1].

“The development of the world’s first AI model capable of reading clinical EEGs at the level of the best experts in the world is a significant accomplishment,” says Harald Aurlien, co-founder of Holberg EEG and a consultant in clinical neurophysiology at Haukeland University Hospital. “The partnership and distribution agreement with Natus, one of the world’s largest EEG companies, will help bring this innovative solution to customers around the world.”

The 2023 study “Automated Interpretation of Clinical Electroencephalograms Using Artificial Intelligence,” published in JAMA, revealed autoSCORE consistently performed with accuracy, sensitivity, and specificity near or above 90% [1]. It also showed the autoSCORE application to be on par with leading human experts while outperforming similar AI models on the market.

“The autoSCORE application has the potential to improve clinical outcomes and value of care for the millions suffering from epilepsy,” states Natus Neuro Chief Executive Officer Chris Landon. “This newly launched solution represents the first of what we expect will be a series of innovative AI applications at Natus designed to deliver efficiency and quality improvements across healthcare.”

Visual EEG review is considered the gold standard for determining abnormalities, but it demands clinical resources and years of specialized training. The AI-enabled autoSCORE application is designed to allow neurocare teams to more efficiently and consistently perform EEG data analysis.

Unlike traditional spike and seizure detectors, the autoSCORE application comprehensively assesses EEG data for multiple clinically relevant abnormalities and provides study-level assessments to inform care teams whether an EEG study is normal or abnormal.

The autoSCORE application has received U.S. Food and Drug Administration clearance for use with routine EEG studies and is currently available in the United States, exclusively with Natus NeuroWorks EEG Software Version 10.

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Table of Contents
1. What is Unified Data Fabric?
2. The Need for UDF in Modern Enterprises
3. Unified Data Fabric for Seamless Data Access and Management
4. What is Unified Data Fabric?
5. The Need for UDF in Modern Enterprises
6. Implementing a Unified Data Fabric: Best Practices
7. Real-World Applications of Unified Data Fabric
8. The Future of Data Management
9. Parting Thoughts

In the context of the increasing prominence of decisions based on big data, companies are perpetually looking for the best approaches to effectively utilize their data resources truly. Introduce the idea of Unified Data Fabric (UDF), a new and exciting proposition that provides a unified view of data and the surrounding ecosystem. In this blog, we will uncover what UDF is, its advantages and thinking why it is set out to transform the way companies work with data.

1. What is Unified Data Fabric?

A Unified Data Fabric or Datalayer can be described as a highest form of data topology where different types of data are consolidated. It is an abstract view of the data accessible across all environment – on-premises, in the Cloud, on the Edge. Therefore, organizations are able to better leverage data and not micromanage the issues of integration and compatibility by abstracting over the underlying complexity through UDF.

2. The Need for UDF in Modern Enterprises

Today, elite business organizations are more involved in managing massive data from multiple fronts ranging from social media platforms to IoT, transactions, and others. Recent data management architectures have had difficulties in capturing and managing such data in terms of volume, variety, and velocity. Here’s where UDF steps in:

1. Seamless Integration: UDF complements the original set up by removing the barriers that create organizational and structural data separation.
2. Scalability: This makes it easy for UDF to expand with data as organizations carry out their activities without performance hitches.
3. Agility: It also enables an organization reposition itself rapidly when it comes to the data environment of an organization, hence it becomes easier to integrate new data sources or other analytical tools.

3. Unified Data Fabric for Seamless Data Access and Management

In the context of algorithmization of management and analytics-based decision making, more often than not, companies and enterprises are in a constant search for ways to maximize the value of their data. Introduce the idea of a Unified Data Fabric (UDF) – a relatively new idea that could help in achieving consistent and integrated data processing across various platforms. Let’s dive a bit deeper on understanding what is UDF, what it can bring to businesses, and why it will redefine data processing.

4. What is Unified Data Fabric?

A Unified Data Fabric is a complex data structure that unifies different kinds of data from multiple sources and types. This framework paints a single picture of data that can be effectively accrued and managed across operational environments, whether seated in the network perimeters, cloud, or at the edges. Thus, UDF sufficiently simplifies the problem of working with big data so that organizations can concentrate on how they can benefit from it rather than worrying about how they can integrate and reconcile their data with that of other organizations.

5. The Need for UDF in Modern Enterprises

Contemporary organizations encounter a deluge of data from various sources Social Media, IoT, transaction systems, etc. This data can overwhelm the traditional data management systems because of its volume and the fact that it varies in type and it moves very fast. Here’s where UDF steps in:Here’s where UDF steps in:

1. Seamless Integration: By interfacing with specific tools to obtain data, all the relevant information does not exist in various isolated compartments in the organization.
2. Scalability: UDF increases performance and resource capabilities in parallel with data growth to allow business to operate at maximum capacity without performance constraints.
3. Agility: It also implies that when there are changes in the data environments, organizations adopt them easily enabling changes on the sources of data or analytic tools.

6. Implementing a Unified Data Fabric: Best Practices

1. Assess Your Data Landscape: Evaluate all the present data types, storage methods as well as the management methods being used. This will assist in defining where UDF will be of the most use and where it will add the most value.
2. Choose the Right Technology: Choose tools that opened with compliance with the principles of the UDF and their capabilities to address the scopes and requirements of your data environment.
3. Focus on Interoperability: Make sure that your UDF solution can easily connect with applications already in use and new ones that will come into use in the future so as to not be bound to a particular vendor.
4. Prioritize Security and Compliance: Additionally, invest in strong security features and that your implemented UDF solution must be capable of conforming with data protection laws.

7. Real-World Applications of Unified Data Fabric

Industry pioneers in several sectors have already implemented UDF to streamline their data operations. A few instances are described below:

• Healthcare: Co-relate patient records, research data, and operational metrics to provide more personalized care with superior outcomes using UDF.
• Finance: Financial institutions leverage UDF for aggregating and analyzing transaction data, market trends, and customer information to have better fraud detection and risk management.
• Retail: This is how, through UDF, retailers can get data integration from online and offline channels for managing inventory and delivering highly personalized shopping experiences.

8. The Future of Data Management

A UDF is one aspect that is slowly, yet with increased rapidity, establishing a very important role in securing unrivaled potential, innovative capabilities, competitiveness in business, and seamless data access and management for organizations deepening their digital transformations.

9. Parting Thoughts

UDF is likely to be more significant as organizations proceed with the integration of advanced technology. The usefulness of being able to present and manipulate data as easily as possible will be a major force behind getting data back into dynamic uses whereby businesses can adapt to change and remain competitive in the market.

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By joining NVIDIA Inception, Mendel will receive access to NVIDIA’s industry-leading technology, including the latest NVIDIA NIM inference microservices to accelerate Mendel’s Hypercube AI solution, and technical expertise in artificial intelligence, deep learning and data science. The resources from the program will help Mendel bring sophisticated, reliable and explainable AI solutions to the healthcare sector that are not only powerful but also responsible and tailored to meet the high stakes of medical decision-making.

“We are incredibly excited to share that Mendel AI has joined NVIDIA Inception, marking a significant milestone in our ongoing efforts to redefine the possibilities of AI in healthcare,” said Karim Galil, CEO of Mendel AI. “By integrating NVIDIA’s cutting-edge AI tools, we aim to enhance our platform’s capabilities, specifically in processing and understanding complex, unstructured medical data at an unprecedented scale. With the program’s resources and together with other forward-thinking companies, we are committed to pushing the boundaries of what AI can achieve in medical research and patient care, aiming to ensure that our technology continues to lead the way in efficiency and effectiveness.”

Mendel’s innovative approach to creating clinician-like AI was included in a recent NVIDIA blog post, highlighting Mendel’s applications across clinical research, real-world evidence generation and cohort selection.

NVIDIA Inception helps startups during critical stages of product development, prototyping and deployment. Every Inception member gets a custom set of ongoing benefits, such as NVIDIA Training credits, preferred pricing on NVIDIA hardware and software, and technological assistance, which provides startups with the fundamental tools to help them grow.

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Parallel Bio, a biotech company using the immune system to cure disease, today announced that Ari Gesher has joined as the first head of technology. He will oversee technical infrastructure and the development of advanced AI and robotics infrastructure to rapidly scale its immune organoid platform for drug discovery.

Gesher joins from Insight M (formerly known as Kairos Aerospace), most recently as chief technology officer, where he spent more than seven years developing and growing its wide-area aerial methane detection technology used to manage industrial emissions of the greenhouse gas. Before that, he spent 10 years at Palantir in various engineering and outreach roles, joining in 2006 as one of the first software engineers at the big data software company.

“Ari knows how to solve the tough problems of industrializing and scaling technologies in completely new fields and turning them into world-class platforms,” said Robert DiFazio, co-founder and CEO of Parallel Bio. “His proven leadership and technical prowess will serve us well as we pioneer a completely new way of discovering drugs proven to work in humans from the start.”

Gesher will work with DiFazio and Juliana Hilliard, co-founder and chief scientific officer, on advancing its immune system platform with a focus on software development and the application of AI and robotics to automate lab processes and organoid creation, analyze and report on studies and experiments, and build predictive disease models from biological data.

Parallel Bio’s first platform for scalable and repeatable biology uses arrays of lymph-node organoids to replicate the immune systems of diverse human populations. Organoids are 3D, self-assembling models of human biology, or so-called “mini organs.” They mimic the structure and function of parts of the human body and their response to disease or treatment as if the organoids were individual patients.

“This is a rare opportunity to combine my passion for developing deep technology with cutting-edge applications of human biology to treat human disease better, cheaper, and faster than ever before,” Gesher said. “Working on the frontiers of industrial automation and computational biology, the founding team is well positioned to upend the industry’s reliance on animal models and flip the 95% drug failure rate on its head.”

Parallel Bio recently released its first commercial application called Clinical Trial in a Dish, which studies the efficacy and safety of new immunotherapies using human models at the earliest stages of drug discovery. Five pharmaceutical companies, including a Fortune 500 firm, have begun testing 20 drug candidates with the alternative to animal tests.

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Key Features of Image-Pro AI:

• Superior outline accuracy: Even the most complicated edges can be traced with high quality. AI Models work similar to the human brain, finding objects undeterred by dim, complex edges.
• User-friendly operation: AI Deep Learning Segmentation requires minimal input to operate efficiently. Simple controls, limited options, and fast prediction results.
• Secure & confidential: No uploading or downloading required. Save time and avoid sharing your data on unsecure company servers. All operations are on-device.
• Protocol-ready: Integrate Deep Learning Segmentation accuracy into an automated workflow by saving an Analysis Protocol with a custom or pre-trained model.

Tailored for both life sciences and materials research and quality inspection, Image-Pro® AI offers robust solutions that cater to specific application needs. Trusted globally, Image-Pro® AI represents a significant leap forward in scientific image analysis, providing a comprehensive, user-friendly tool for researchers and scientists.

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Frazier continues to focus on the strategic expansion of its dedicated Center of Excellence (CoE), providing Fortune 500 talent to our portfolio companies in the areas of Human Capital, Technology & Innovation, Data Analytics & Artificial Intelligence, M&A & Corporate Development, Finance, Operations, Legal, Commercial Excellence, and ESG. Frazier Managing Partner Ben Magnano said, “As the firm continues to invest in the breadth and depth of this team, we are targeting expansion in areas where we see accelerated value creation opportunities as part of our differentiated strategy.”

“Frazier is fortunate to have such a talented, collaborative group of professionals who enjoy supporting our management teams and helping them drive transformation,” said Andy Caine, CoE COO & Partner. “We are thrilled to welcome Sanji, Steve, and Lindsey to our team, each of whom brings a wealth of experience in their areas of expertise.”

About Sanji Fernando: Sanji joined Frazier’s CoE Data & AI team after serving as SVP of AI Products and Platforms at Optum, where he spearheaded the development of next generation AI to transform healthcare. He launched United AI Studio, a unified platform enabling rapid experimentation and deployment of advanced ML across UHC and Optum. The United AI Studio provides access to cutting-edge capabilities like Generative AI while ensuring rigorous monitoring of model performance and evaluating models for bias. Previously, Sanji pioneered the application of deep learning for risk adjustment, automating the identification of hierarchical condition codes from medical charts. This innovation drove efficiencies and positioned UnitedHealth as an AI leader in healthcare administration. Sanji also founded Optum Labs’ Center for Applied Data Science (CADS), delivering innovative AI and Machine Learning solutions to complex healthcare challenges. With his proven track record of leveraging AI to create enterprise value, Sanji was key in positioning UnitedHealth to the forefront of healthcare’s digital transformation. His leadership and technical acumen make him a strategic driver of transformative innovation for the organization. Sanji obtained his BS in Computer Science from Trinity College in Hartford.

About Steve Blackhart: Steve Blackhart is an M&A professional with extensive transaction experience on both the buy-side and sell-side. He joins Frazier from Black Eagle Advisors, an M&A advisory firm he founded in 2015. There, Steve executed transactions across a wide breadth of industries, including healthcare, business services, and consumer products. He also provided strategic guidance in the formation of corporate development functions within newly acquisitive organizations and served as the head of M&A for a number of companies. Most recently, Steve led corporate development for MiX Telematics (NYSE: MIXT), directing the Company’s acquisition of Trimble’s Field Services Management division and its merger with Powerfleet (NASDAQ: PWFL). Steve began his career in the investment banking division of Bank of America Merrill Lynch before becoming a private equity professional at FFL Partners. At FFL, Steve was involved in the firm’s investments in healthcare, consumer products, and financial services businesses. Steve holds a BS in Business Administration from the University of Southern California and an MBA from Harvard Business School.

About Lindsey Murphy: Lindsey has over 12 years of experience in sales operations, and in her role at Frazier, she will support its portfolio companies with sales operations, including improving sales processes, tools, and pipeline management. Her responsibilities include utilizing expertise in data analysis, CRM administration, and sales forecasting to improve sales efficiency and revenue growth. Prior to Frazier, she was the Director of Sales Operations at Parata, a Frazier portfolio company. During her time at Parata, Lindsey provided strategy, design, and execution of sales processes for the commercial organization leading to increasing revenue from $75 million to $275 million. Lindsey received her BS in Business Administration from the University of North Carolina, Wilmington.

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“Billions of dollars have been spent on creating programs to help people whose needs fall outside of traditional healthcare, yet these needs persist,” says Gray. “The answer is not more money or more programs. The answer is better information.”

View the op-ed by Erine Gray below.

Learning from Lyft to Fix the Social Safety Net

By Erine Gray

In 2012, on a busy city street, I watched a man exit an ordinary-looking vehicle driven by someone else. I asked him if he knew the driver and he responded that he did not. Rather, he was an early angel investor in something called Lyft, which used a mobile app to connect drivers with people who needed rides. His Lyft had just dropped him off.

After we parted, I thought the concept made sense – connecting a person who needs a ride with a driver who needs a fare – but I thought the logistics were insurmountable to overcome.

Quickly, I was proved wrong…

To read the rest of the op-ed, click here.

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Graph database and analytics leader Neo4jⓇ today announced at Snowflake’s annual user conference, Snowflake Data Cloud Summit 2024, a partnership with Snowflake to bring its fully integrated native graph data science solution within Snowflake AI Data Cloud. The integration enables users to instantly execute more than 65 graph algorithms, eliminates the need to move data out of their Snowflake environment, and empowers them to leverage advanced graph capabilities using the SQL programming languages, environment, and tooling that they already know. 

The offering removes complexity, management hurdles, and learning curves for customers seeking graph-enabled insights crucial for AI/ML, predictive analytics, and GenAI applications. The solution features the industry’s most extensive library of graph algorithms to identify anomalies and detect fraud, optimize supply chain routes, unify data records, improve customer service, power recommendation engines, and hundreds of other use cases. Anyone who uses Snowflake SQL can get more projects into production faster, accelerate time-to-value, and generate more accurate business insights for better decision-making.

Neo4j graph data science is an analytics and machine learning (ML) solution that identifies and analyzes hidden relationships across billions of data points to improve predictions and discover new insights. Neo4j’s library of graph algorithms and ML modeling enables customers to answer questions like what’s important, what’s unusual, and what’s next. Customers can also build knowledge graphs, which capture relationships between entities, ground LLMs in facts, and enable LLMs to reason, infer, and retrieve relevant information more accurately and effectively. Neo4j graph data science customers include Boston Scientific, Novo Nordisk, OrbitMI,and Zenapse, among many others.

“By 2025, graph technologies will be used in 80% of data and analytics innovations — up from 10% in 2021 — facilitating rapid decision-making across the enterprise,” predicts Gartner® in its Emerging Tech Impact Radar: Data and Analytics November 20, 2023 report. Gartner also notes, “Data and analytics leaders must leverage the power of large language models (LLMs) with the robustness of knowledge graphs for fault-tolerant AI applications,” in the November 2023 report AI Design Patterns for Knowledge Graphs and Generative AI. 

Neo4j with Snowflake: new offering capabilities and benefits

Enterprises can harness and scale their secure, governed data natively in Snowflake and augment it with Neo4j’s graph analytics and reasoning capabilities for more efficient and timely decision-making, saving customers time and resources.

1. Instant algorithms. Joint customers can use SQL to build knowledge graphs and run more than 65 Neo4j graph algorithms out of the box, including easy-to-use machine learning tools. Neo4j’s library is available as a native service within Snowflake. Graph algorithms are available as SQL functions, enabling users to easily enhance ML pipelines with influencer scores, community identifiers, page rank, outliers, and other graph features for greater ML accuracy.
   
2. Zero ETL (Extract, Transform, Load). Customers can access and run Neo4j’s extensive library of graph algorithms entirely within their Snowflake environment without the need to go through procurement and security sign-off to move their data to another SaaS provider. The ability to use their data as-is without having to go through the painful exercise of extracting, transforming, and loading it into another database and provider. Zero ETL simplifies security and data workflows and eliminates the overhead of data preparation.
   
3. Familiar languages and tooling. Customers benefit from native graph capabilities as part of a toolset and environment with which they already know. Data scientists and developers can use Snowflake SQL in their workflows to streamline development, accelerate time-to-insight, and easily derive greater value from their data. Neo4j works with the latest Snowpark Container Services (SPCS) that Snowflake announced today.
   
4. GenAI enabled. Joint customers can create knowledge graphs and generate vectors that take advantage of structured, unstructured, and relationship data. These features are part of a complete GenAI stack within Snowflake that includes both vector search and Snowflake Arctic LLM models. The result organizes and represents the data in ways that make it easier to understand and retrieve insights in GenAI applications and make these insights more accurate, transparent, and explainable.
   
5. Fully serverless and flexible. Customers pay only for what they need. Users create ephemeral graph data science environments seamlessly from Snowflake SQL, enabling them to pay only for Snowflake resources utilized during the algorithms’ runtime using Snowflake credits. These temporary environments are designed to match user tasks to specific needs for more efficient resource allocation and lower cost. Graph analysis results also integrate seamlessly within Snowflake, facilitating interaction with other data warehouse tables.

Supporting quotes

Greg Steck, VP Consumer Analytics, Texas Capital Bank

“At Texas Capital Bank, we’re built to help businesses and their leaders succeed. We use Snowflake and Neo4j for critical customer 360 and fraud use cases where relationships matter. We are excited about the potential of this new partnership. The ability to use Neo4j graph data science capabilities within Snowflake will accelerate our data applications and further enhance our ability to bring our customers long-term success.”

Jeff Hollan, Head of Applications and Developer Platform, Snowflake

“Integrating Neo4j’s proven graph data science capabilities with the Snowflake AI Data Cloud marks a monumental opportunity for our joint customers to optimize their operations. Together, we’re equipping organizations with the tools to extract deeper insights, drive innovation at an unprecedented pace, and set a new standard for intelligent decision-making.”

Sudhir Hasbe, Chief Product Officer, Neo4j

“Neo4j’s leading graph analytics combined with Snowflake’s unmatched scalability and performance redefines how customers extract insights from connected data while meeting users in the SQL interfaces where they are today. Our native Snowflake integration empowers users to effortlessly harness the full potential of AI/ML, predictive analytics, and Generative AI for unparalleled insights and decision-making agility.”

The new capabilities are available for preview and early access, with general availability later this year on Snowflake Marketplace. For more information, read our blog post or contact us for a preview of Neo4j on Snowflake AI Data Cloud.

To learn more about how organizations are building next gen-apps on Snowflake, click here.

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

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Modern data pipeline platform provider Matillion today announced at Snowflake Data Cloud Summit 2024 that it is bringing no-code Generative AI (GenAI) to Snowflake users with new GenAI capabilities and integrations withSnowflake Cortex AI, Snowflake ML Functions, and support for Snowpark Container Services.

Matillion’s graphical components make it easy and near instant to incorporate GenAI into data workflows, regardless of technical expertise. To do this, Matillion abstracts SQL functions that run in Cortex AI’s fully managed infrastructure for LLMs.

The newly launched GenAI components enable powerful out-of-the-box use cases, including generating product descriptions, extracting key information from customer reviews, analyzing sentiment, summarizing lengthy reports, and translating content for global audiences.

Ciaran Dynes, Matillion Chief Product Officer said: “As organizations move beyond the hype of GenAI to the realities of implementation, Matillion makes it fast and easy, using only existing data engineering skills, whilst being completely integrated with Snowflake. We believe that 90% of AI engineering is still going to be about preparing the data. Data engineers are key.”

“We are excited to announce that Matillion now supports all of the major Snowflake AI capabilities, in a no-code, easy-to-use platform providing a complete and powerful AI solution that integrates with existing data workflows,” Dynes continues.

Baris Gultekin, Head of AI at Snowflake said: “At Snowflake, we’re on a mission to bring the potential of AI to every business user, no matter their technical proficiencies. Partners like Matillion are making that mission easier to achieve through innovations and advancements that provide a pushdown AI approach, and no-code pipeline platform to accelerate the pace of AI solution development. There’s a tremendous opportunity to build on Cortex AI, and it’s exciting to see companies like Matillion taking advantage of it.”

Matillion also announced support for Snowpark Container Services components, which enable users to run any open-source LLM used for a data processing job directly inside their Snowflake accounts, ensuring no single record leaves Snowflake and that all datasets have the highest level of security and governance.

“Alongside bringing GenAI and LLMs to a wider market with easy no-code functionality, we’re also ensuring security and sovereignty of regulated, sensitive data with our Snowpark Container Services components,” added Dynes. “Matillion brings AI capabilities directly into data pipelines, and with our Snowpark Container Services integration, we provide Snowflake users with the flexibility to adapt and evolve as customers’ needs change, potentially leveraging smaller, dedicated models for improved cost, accuracy, and performance.”

Matillion’s Snowpark Container Services integration allows customers to securely run virtually any open-source AI model directly within their Snowflake account, ensuring data privacy and enabling customization based on specific use cases.

The addition of Snowflake AI components is the latest in a series of launches from Matillion following the announcement of its AI vision in late 2023. In March, Matillion was first to market with its platform to unify pushdown ELT and pushdown GenAI to allow data engineers to build Analytics and AI pipelines faster.

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