Pioneering startups unleash enterprise value with Amazon SageMaker Incubator

By Dylan Sarachek, Sr. Worldwide Analytics Partner Development Specialist – AWS
by Vitor Freitas, Sr. Worldwide Data and AI Partner Solutions Architect – AWS
by Gitika Vijh, Sr. Worldwide Data and AI Partner Solutions Architect – AWS

Startups face significant hurdles delivering AI solutions for customers, due to the complexity of managing scalable infrastructure and establishing go-to-market strategies. Technical integration challenges, coupled with the need to meet strict enterprise requirements for security, compliance, and reliability, often create lengthy development cycles that delay market entry. The Amazon SageMaker Startup Incubator addresses these challenges head-on by providing Amazon Web Services (AWS) startup partners with comprehensive technical enablement, ready-to-use reference architectures, and accelerated go-to-market support as they use the next generation of Amazon SageMaker.

Amazon SageMaker is the center for all your data, analytics, and AI, bringing together widely adopted AWS machine learning (ML) and analytics capabilities with unified access to all your data. Through the integrated solutions built with partners as part of the SageMaker startup incubator program, customers can now combine innovative startup technologies with proven infrastructure from AWS to accelerate their data and AI initiatives, from data preparation to model deployment and beyond.

This post highlights the success of five lighthouse startup partners nominated for the program. We invite you to read on to learn about the innovative solutions our startup partners have built, demonstrating the real-world impact of these collaborations.tank

Transformative success stories in the Amazon SageMaker Startup Partner Incubator

Since the general availability of the next generation of Amazon SageMaker, our incubator partners have rapidly embraced its advanced capabilities to create comprehensive solutions for customers. These startups are using the SageMaker Unified Studio environment, including the query editor, notebooks, and SageMaker Catalog for seamless data sharing. They’re using the lakehouse architecture of Amazon SageMaker with its Iceberg API integration to build powerful end-to-end solutions that address complex enterprise needs.

Tonic AI: Synthetic data solutions for software and AI development

Enterprises use the Tonic.ai solution to transform sensitive data into secure, high-fidelity synthetic datasets for AI and analytics workloads.

Through a new notebook-based workflow, AWS customers can now use Tonic Textual on data stored in their lakehouse architecture of Amazon SageMaker to transform high-risk, unstructured data into safe, high-quality assets. This de-identified data is essential for a wide range of generative AI use cases, including fine-tuning large language models (LLMs), building knowledge bases for Retrieval Augmented Generation (RAG) workflows, creating evaluation datasets, and testing generative AI applications. To see the full workflow in action, readers can find a detailed, step-by-step guide on the Tonic.ai blog.

“The SageMaker next generation incubator program put our mission on the fast track: bringing Tonic Textual’s advanced technology directly to enterprises building on SageMaker. It has been pivotal in helping them replace sensitive information with high-quality synthetic data, dramatically shortening the path from raw data to impactful, production-ready AI models.” — Tomer Benami, VP of Business Development

Activeloop: Unlocking AI Data Analysis

Activeloop, a pioneering AI infrastructure company, specializes in developing solutions that optimize AI data management and processing workflows. Their flagship product, Deep Lake, serves as a database specifically designed for AI applications, enabling efficient handling of multimodal data, including text, images, signals, and literature, by delivering impressive speeds in Apache Spark workloads of up to 3.9 times faster, compared to open source alternatives.

Enhanced by their integration with the next generation of Amazon SageMaker and its lakehouse architecture approach, Activeloop combines Deep Lake’s ultra-fast, multimodal AI data retrieval capabilities with the enterprise-grade scalability and versatility of SageMaker. This integration enables direct access to complex AI data from object storage, streamlining analytics and artificial intelligence and machine learning (AI/ML) model development. By unifying multimodal data with Deep Lake’s advanced indexing capabilities, organizations can seamlessly connect textual, numerical, and visual information, accelerating insights and innovation across various domains, from healthcare to drug discovery, transforming their data assets into competitive advantages while maintaining efficient, scalable, and cost-effective AI operations. Read more about the solution on the Activeloop blog.

“Through the incubator, we’ve gained invaluable technical enablement and marketing support, allowing us to scale our Deep Lake service capabilities to meet enterprise demand.” — Davit Buniatyan, CEO

Weaviate: For AI engineers who think big

Weaviate is a cloud-centered open source vector database that simplifies development and deployment of AI applications. It offers vector search, keyword, and hybrid search capabilities through a scalable, flexible platform with pluggable architecture to connect with ML models. Production deployments benefit from built-in multi-tenancy, replication, role-based access control (RBAC) authorization, zero-downtime backups, advanced filtering, vector compression, and out-of-the-box RAG capabilities. Weaviate is available as a hosted service, self-managed instance, or as fully managed Weaviate Cloud. By handling the infrastructure and operational details, Weaviate Cloud frees developers to focus on innovation while providing enterprise-ready solutions through various hosting options including serverless cloud, enterprise cloud, and bring your own cloud.

Weaviate has developed a comprehensive SageMaker Unified Studio notebook solution that bridges the gap between traditional data storage and AI-powered applications by using existing lakehouse data. This solution seamlessly transforms your structured or unstructured data stored in your lakehouse into a vectorized format within Weaviate’s vector database, enabling sophisticated semantic search capabilities and RAG queries for enhanced data utilization. Read about the solution on Weaviate blog.

“The SageMaker Incubator provided us the guidance and support we needed to bridge the gap between open source innovation and enterprise readiness, making it easier for our customers to deploy Weaviate at scale.” — Tony Le, Sr. Director of Partners

Snowplow: Turn customer behavior into competitive advantage

Snowplow provides a customer data infrastructure that organizations can use to collect and stream clickstream data in real time. Organizations can capture complete, high-fidelity event data across every digital touchpoint—governed, modeled, and ready for downstream analytics and AI-driven use cases.

Through the SageMaker Startup Partner Incubator program, Snowplow has built a solution to collect your data in real time and ingest it into your lakehouse on AWS. Once ingested, teams can use this lakehouse data to drive analytics use cases. You can run extract, transform, and load (ETL) capabilities available within SageMaker Unified Studio or use the SageMaker integration with Amazon Quick Sight to generate dashboards for enterprise reporting. You can further power ML use cases like recommendations and next best action. The joint solution provides a seamless path from a trusted behavioral data foundation to real-time AI-driven optimization at scale. Learn more about our partnership and joint solution at Snowplow for Amazon SageMaker.

“The SageMaker Incubator program has accelerated our ability to deliver real-time, AI-ready clickstream data directly into customers’ SageMaker environments. With Snowplow’s solution deployable in the customer’s tenant, and Snowplow Signals enabling easy real-time feature computation and serving, teams can securely build and operationalize ML and agentic models with rich behavioral data without sacrificing control or compliance.” — Yali Sasson, Snowplow Co-founder and CTO

SuperAnnotate: Streamlining AI development

SuperAnnotate helps efficiently curate, label, and validate multimodal data for AI training and evaluation. Its robust annotation environment and connected data pipelines enable enterprises to accelerate time-to-production by up to 72%, powered by a seamless human and AI feedback loop that ensures consistent, high-quality data.

SuperAnnotate’s integration with Amazon SageMaker streamlines the entire machine learning workflow from raw data to AI deployment. Annotated datasets from SuperAnnotate are stored in standardized formats and are directly accessible in Amazon Simple Storage Service (Amazon S3) within the SageMaker Unified Studio environment. This gives teams simple, consistent access to labeled data in SageMaker Unified Studio to use in any stage of model development – from training, fine-tuning, and evaluation – without complex data handling. Together, SuperAnnotate and SageMaker create human-in-the-loop active learning cycles to continuously retrain and improve your models.

“The SageMaker Incubator gave us exactly what we needed to move fast – deep technical support and tight collaboration, helping us deliver a truly integrated solution at scale. It’s been an incredible experience building alongside AWS to deliver a unified workflow that brings greater value and speed to our mutual customers.” – Vahan Petrosyan, Cofounder and CEO

Conclusion

In this post, we showed how innovative startups such as Tonic.ai, Activeloop, Weaviate, Snowplow, and SuperAnnotate are harnessing Amazon SageMaker to deliver enterprise-grade data and AI solutions. Partners in the program benefit from the robust capabilities of Amazon SageMaker. The unified environment enables comprehensive data management across data lakes and warehouses and provides AI-assisted development support and built-in governance and security controls. Partners can use seamless data sharing and ETL capabilities so they can focus on innovation rather than infrastructure management.

For startups that want to scale their data and AI solutions for enterprises, the Amazon SageMaker Startup Partner Incubator provides a proven path to success. The program offers comprehensive support, including technical enablement, marketing assistance, and accelerated go-to-market strategies. Through this support, partners can rapidly develop and deploy enterprise-grade solutions that solve real business challenges while maintaining the highest standards of security and compliance.

Ready to transform your startup’s potential into enterprise value? Join the Amazon SageMaker Startup Partner Incubator today by visiting our application page at Amazon SageMaker Startup Partner Incubator.