List of SuperAnnotate Customers
San Francisco, 94105, CA,
United States
Since 2010, our global team of researchers has been studying SuperAnnotate customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased SuperAnnotate for Generative AI Platforms from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using SuperAnnotate for Generative AI Platforms include: databricks, a United States based Professional Services organisation with 5500 employees and revenues of $1.60 billion, Hinge Health, a United States based Healthcare organisation with 500 employees and revenues of $426.0 million, TwelveLabs, a United States based Professional Services organisation with 73 employees and revenues of $8.0 million and many others.
Contact us if you need a completed and verified list of companies using SuperAnnotate, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
The SuperAnnotate customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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databricks | Professional Services | 5500 | $1.6B | United States | SuperAnnotate | SuperAnnotate | Generative AI Platforms | 2024 | n/a |
In 2024, databricks implemented SuperAnnotate, deploying SuperAnnotate in the Generative AI Platforms category to standardize and scale model assessment workflows. The U.S.-based engagement focused on building a customized model-evaluation pipeline for consistent, repeatable assessments of generative models.
The implementation centered on a model-evaluation pipeline that included retrieval augmented generation evaluation and an LLM as a judge configuration to automate judgment and scoring logic. Functional capabilities delivered by SuperAnnotate included evaluation orchestration, automated judge invocation for synthesized scoring, and centralized aggregation of evaluation signals to support standardized decisioning.
The solution was integrated into databricks model assessment workflows and executed within the scoped U.S. engagement, aligning evaluation processes across model teams. Operational coverage emphasized standardized evaluation processes and automation to scale assessment throughput while retaining traceability for model review.
Governance work established a consistent evaluation framework and automated workflow steps to reduce manual effort in scoring and adjudication. According to the SuperAnnotate case study, the engagement yielded up to a 10x reduction in evaluation cost and approximately a 3x increase in evaluation throughput.
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Hinge Health | Healthcare | 500 | $426M | United States | SuperAnnotate | SuperAnnotate | Generative AI Platforms | 2024 | n/a |
In 2024, Hinge Health implemented SuperAnnotate as part of its Generative AI Platforms stack to support clinical musculoskeletal motion technology. The engagement was based in the United States and focused on high quality human pose and motion annotation services and DataOps to improve training datasets for MSK motion models.
SuperAnnotate delivered human pose and motion annotation capabilities along with DataOps services, encompassing annotation workflows, QA orchestration, and dataset handling to prepare temporal and frame level labels for model training. The SuperAnnotate implementation emphasized annotation quality controls and pipeline automation to accelerate dataset iteration for clinical model development.
Annotation outputs were fed into Hinge Health model training and data science workflows, supporting clinical engineering and research teams responsible for musculoskeletal motion analytics. Operational coverage was centered on clinical ML workflows for MSK applications and a United States based project team coordinated annotation and delivery cycles.
Governance relied on DataOps driven quality gates and accelerated QA cycles, which the vendor reported dramatically sped QA and delivery. Outcomes reported by the engagement included an increase in annotation accuracy from approximately 80% to approximately 96% and approximately 50% annotation cost savings.
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TwelveLabs | Professional Services | 73 | $8M | United States | SuperAnnotate | SuperAnnotate | Generative AI Platforms | 2024 | n/a |
In 2024 TwelveLabs implemented SuperAnnotate, deploying managed multimodal video annotation and end-to-end data workflow capabilities within the Generative AI Platforms category to accelerate Marengo video-understanding model development. The U.S.-based project was scoped around annotation throughput and pipeline readiness for supervised and fine-tuning workflows, aligning SuperAnnotate with TwelveLabs model development priorities.
SuperAnnotate provided managed multimodal video annotation, dataset management, quality control workflows, and data export mechanisms to prepare training and validation sets for fine-tuning. The implementation emphasized annotation schema configuration for video frames and temporal segments, iterative labeling cycles, and automated dataset packaging to feed into TwelveLabs fine-tuning pipelines.
Operationally the engagement targeted TwelveLabs ML engineering and data science workflows for the Marengo model, integrating annotated assets into the model fine-tuning process. Governance used a managed services approach with centralized workflow orchestration and versioned datasets to support iterative model development, and the U.S.-based deployment reported a reduction in time-to-market by approximately 50 percent and up to 2x faster model iteration as stated in the project case study.
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