List of Kognic Platform Customers
Goteborg, 4117 56,
Sweden
Since 2010, our global team of researchers has been studying Kognic Platform 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 Kognic Platform for ML and Data Science 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 Kognic Platform for ML and Data Science Platforms include: Continental, a Germany based Automotive organisation with 96426 employees and revenues of $46.60 billion, Zenseact Group, a Sweden based Professional Services organisation with 600 employees and revenues of $31.0 million, Kodiak Ai, a United States based Transportation organisation with 306 employees and revenues of $15.0 million and many others.
Contact us if you need a completed and verified list of companies using Kognic Platform, 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.
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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Continental | Automotive | 96426 | $46.6B | Germany | Kognic | Kognic Platform | ML and Data Science Platforms | 2023 | n/a |
In 2023, Continental implemented the Kognic Platform, an ML and Data Science Platforms solution, deployed by perception R&D in Germany and across Europe to accelerate 3D ground-truth generation from LiDAR point-clouds for ADAS and autonomy development. The Kognic Platform entered production after a late 2023 proof of concept and was adopted by Continental's perception engineering teams for operational labeling workflows.
The deployment emphasized point-cloud annotation workflows, dataset lifecycle management, and QA orchestration to produce 3D labels and ground-truth assets for perception model training. Implementation used the Kognic Platform annotation tooling and automation to handle complex 3D segmentation and labeling patterns common to LiDAR-based perception pipelines, while managing dataset versioning and delivery artifacts.
Operational coverage targeted Continental's perception R&D organization in Germany and Europe, and the rollout enabled a hybrid workforce model combining internal labelers with Kognic-managed contributors. Workflows were configured to support task assignment, review cycles, and delivery staging aligned to customer demos and ADAS validation timelines.
Governance changes formalized QA checkpoints and delivery processes to streamline demos and iterative validation, and process updates supported hybrid internal plus Kognic workforce scaling. Outcomes explicitly reported from the deployment included improved labeling throughput, enabled hybrid workforce scaling, and streamlined QA and delivery for customer demos.
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Kodiak Ai | Transportation | 306 | $15M | United States | Kognic | Kognic Platform | ML and Data Science Platforms | 2024 | n/a |
In 2024, Kodiak Ai deployed the Kognic Platform to scale high-quality, time-series multi-sensor annotation pipelines for autonomous trucking development in the United States. Kodiak Ai implemented Kognic Platform as an ML and Data Science Platforms solution to centralize annotation throughput and support iterative model training. The engagement prioritized integrating Kodiak's pre-labeling outputs with Kognic's tooling to streamline label handoff.
Implementation work focused on configuring time-series annotation workflows, automated delivery orchestration, and label validation capabilities typical of ML and Data Science Platforms. Kognic Platform was configured to ingest pre-labeled sequences, apply platform-native quality checks and reliability scoring, and produce annotation batches formatted for downstream machine learning consumption. These modules were used to reduce manual labeling overhead and to standardize label schemas for model training pipelines.
Operational coverage centered on Kodiak Ai's ML engineering and labeling operations, aligning annotation delivery with model iteration cycles used in autonomous trucking development across US sites. Governance changes included formalizing handoff processes between Kodiak's pre-labeling stage and Kognic Platform annotation queues, and instituting validation checkpoints to improve label reliability for ML training. The engagement explicitly aimed to automate annotation delivery, improve label reliability for ML training, and accelerate model iteration cycles.
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Zenseact Group | Professional Services | 600 | $31M | Sweden | Kognic | Kognic Platform | ML and Data Science Platforms | 2018 | n/a |
In 2018, Zenseact Group implemented the Kognic Platform to scale ADAS and perception annotation workflows and to automate delivery pipelines for model training in Gothenburg, Sweden. The deployment positioned Kognic Platform within the ML and Data Science Platforms category to support dataset shaping, annotation operations, and model training handoffs for perception engineering teams.
The implementation focused on annotation workflow orchestration and quality control capabilities, with Kognic Platform configured to manage dataset labeling queues, annotation review cycles, and dataset shaping workflows. Configuration work included automated API endpoints and pipeline orchestration components to push validated datasets into downstream model training processes and to close feedback loops between annotators and model evaluation.
Operational coverage centered on perception and ADAS engineering groups in Gothenburg, with flows instrumented for annotation teams and dataset managers. Integrations were delivered as automated APIs and pipeline connectors to the teams primary model training infrastructure, enabling programmatic dataset delivery and repeatable ingestion for training runs without naming specific training systems.
Governance and process changes emphasized tighter QC workflows and standardized annotation handoffs, with a long term partnership model that produced automated APIs and centralized workflow controls. The engagement produced measurable productivity gains for dataset shaping and annotation operations, as reported by Zenseact Group, and institutionalized API-led dataset delivery patterns for ongoing model training cycles.
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