List of Sama GenAI Customers
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Since 2010, our global team of researchers has been studying Sama GenAI 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 Sama GenAI 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 Sama GenAI for Generative AI Platforms include: CreatorIQ, a United States based Professional Services organisation with 325 employees and revenues of $55.0 million, Orbisk, a Netherlands based Professional Services organisation with 68 employees and revenues of $4.0 million, Vulcan, a United States based Banking and Financial Services organisation with 10 employees and revenues of $2.0 million and many others.
Contact us if you need a completed and verified list of companies using Sama GenAI, 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 Sama GenAI 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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CreatorIQ | Professional Services | 325 | $55M | United States | Sama | Sama GenAI | Generative AI Platforms | 2017 | n/a |
In 2017, CreatorIQ implemented Sama GenAI as part of its influencer marketing data labeling pipeline. The deployment uses Sama GenAI within the Generative AI Platforms category to locate, extract, and tag brand mentions that feed marketing and CRM modeling workflows.
The implementation emphasized annotation and validation capabilities, with inferred use of model evaluation and human in the loop workflows to sustain labeling accuracy and throughput. Sama GenAI's model evaluation and human in the loop workflows are assumed to be part of the annotation and validation stack used to maintain accuracy across markets.
Operational coverage focused on influencer marketing and marketing analytics functions, and governance incorporated annotation validation workflows and quality SLAs to enforce labeling standards. According to the provided vendor notes, the solution supported roughly 4x growth in labeled data volume and delivered a consistent greater than 99% quality SLA.
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Orbisk | Professional Services | 68 | $4M | Netherlands | Sama | Sama GenAI | Generative AI Platforms | 2022 | n/a |
In 2022, Orbisk deployed Sama GenAI, a Generative AI Platforms application, to augment its computer-vision food-waste monitoring system and support operational and sustainability workflows. The implementation relied on Sama-provided high-quality image annotation, labeling hundreds of thousands of food images to populate Orbisk's analytic data stores and reporting pipelines.
Sama GenAI's image-model evaluation and human-in-the-loop validation capabilities were used to iteratively improve Orbisk's computer vision model performance and reporting accuracy. Configuration work centered on annotation quality controls, consensus reviewer workflows, and model feedback loops to tighten label-model alignment.
The Sama GenAI output was embedded into Orbisk's analytics stack to feed client-facing dashboards and operational teams serving restaurants and food service clients. The deployment directly supported operations and sustainability business functions by instrumenting monitoring, alerting, and waste reporting with validated image-level annotations.
Governance emphasized dataset labeling standards, reviewer throughput, and regular model validation cycles during rollout to maintain annotation consistency. Reported outcomes tied to the annotation and validation workstreams include client reductions in food waste up to 70% and avoidance of 200,000 kg of waste to date, demonstrating the operational impact of Sama GenAI on Orbisk's monitoring and reporting capabilities.
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Vulcan | Banking and Financial Services | 10 | $2M | United States | Sama | Sama GenAI | Generative AI Platforms | 2019 | n/a |
In 2019 Vulcan implemented Sama GenAI in the Generative AI Platforms category to support wildlife conservation computer vision for research and conservation operations. The deployment focused on large scale image and video annotation for UAV sourced datasets used in wildlife monitoring.
Sama GenAI was applied to image and video annotation pipelines, with inferred use of model evaluation tooling and human in the loop workflows to manage annotation quality and iterative model refinement. Sama delivered annotation at scale, labeling between 600,000 and 1,000,000 images across project datasets. This annotation capability accelerated training data preparation and model training cycles.
Operational coverage centered on Vulcan's research and conservation operations across African survey regions, where UAV sourced imagery drove object detection and species monitoring workflows. Integrations with other enterprise systems are not specified, the implementation is described in terms of dataset ingestion, annotation orchestration and human review workflows. Sama GenAI provided the application layer for managing annotation projects and quality control.
Governance relied on human in the loop annotation governance and model evaluation checkpoints to ensure label consistency and iterative retraining. The implementation was intended to improve detection accuracy for wildlife monitoring and explicitly reported faster model training and reduced turnaround times as operational effects.
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