List of Cohere Rerank 3 Customers
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Since 2010, our global team of researchers has been studying Cohere Rerank 3 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 Cohere Rerank 3 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 Cohere Rerank 3 for Generative AI Platforms include: Ramp, a United States based Professional Services organisation with 850 employees and revenues of $100.0 million, Loom, a United States based Professional Services organisation with 240 employees and revenues of $25.0 million, Quo, formerly OpenPhone, a United States based Professional Services organisation with 150 employees and revenues of $17.0 million and many others.
Contact us if you need a completed and verified list of companies using Cohere Rerank 3, 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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Loom | Professional Services | 240 | $25M | United States | Cohere | Cohere Rerank 3 | Generative AI Platforms | 2024 | n/a |
In 2024, Loom deployed Cohere Rerank 3 as part of its Generative AI Platforms implementation to power support automation by syncing its help center into conversational flows. The deployment targeted customer support and self-service use cases, with Cohere Rerank 3 used to surface and prioritize the most relevant help-center content inside conversational interfaces.
The implementation centered on a retrieval and reranking architecture, using Cohere Rerank 3 to order candidate passages returned from help-center content so responses are more relevant and compact. Configuration emphasized ranking thresholds and selective retrieval to limit token consumption and reduce latency overhead, aligning the application behavior with typical retrieval-augmented generation workflows in Generative AI Platforms.
Integrations were focused on embedding the ranked help-center content into conversational flows and support automation, operationally covering customer support and help-center driven self-service channels. The integration model emphasized content sync from the help center into the conversational layer, enabling the system to reference authoritative documentation during interactions without inventing external connectors.
Governance and operational tuning were handled through iterative ranking adjustments and content curation by support and product teams, with ongoing model tuning to refine relevance signals. According to Cohere's customer story, Loom achieved a 51% self-serve rate shortly after deployment, demonstrating the implementation's impact on support automation while the reranking approach aimed to improve answer relevance and manage token and latency costs.
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Quo, formerly OpenPhone | Professional Services | 150 | $17M | United States | Cohere | Cohere Rerank 3 | Generative AI Platforms | 2024 | n/a |
In 2024 Quo, formerly OpenPhone, implemented Cohere Rerank 3 to automate in-app and ticketed customer support. Cohere Rerank 3 is part of Cohere's Generative AI Platforms offering and was selected to improve retrieval and ranking of support content for both self-service and agent-assisted workflows. This deployment targeted customer support functions across Quo's support and product teams within its professional services oriented operations.
Cohere Rerank 3 was applied as the ranking layer in a retrieval-augmented generation style stack, inferred from the case study description of retrieval-based automation and improved ranking for support content. The implementation focused on reranking candidate knowledge snippets, tuning relevance signals, and routing higher-quality responses into conversational assistants and search interfaces. Configuration work emphasized relevance thresholds, ranking model tuning, and content selection controls to elevate answer quality for end users.
Operationally the solution was embedded into in-app help flows and ticketing pipelines to enable automated responses and improved self-serve pathways. The scope explicitly covered in-app support experiences and ticket handling workflows used by customer support teams, creating a unified retrieval surface across support content sources. No specific external ticketing vendors are reported in the source material, the integration signal is limited to in-app and ticketed support channels.
Governance and rollout work centered on content curation and ranking policy adjustments to align automated responses with escalation rules and support workflows. Outcomes called out in Cohere's customer story include achieving greater than 54% self-serve outcomes and a reported 4x ROI. The case documents Quo Cohere Rerank 3 Generative AI Platforms customer support implementation as a retrieval-focused automation approach applied to support operations.
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Ramp | Professional Services | 850 | $100M | United States | Cohere | Cohere Rerank 3 | Generative AI Platforms | 2024 | n/a |
In 2024, Ramp implemented Cohere Rerank 3 within its Generative AI Platforms deployment to automate in-app support flows and surface more relevant help-center content. The implementation focused on retrieval and precision improvements using Cohere Rerank 3 to rank candidate documents and drive self-service answers inside the product support interface.
The implementation configured Cohere Rerank 3 as a reranking layer on top of existing content indexes, aligning query signals with relevance scoring to prioritize the most applicable help articles. Deployment work included model configuration for reranking, content ingestion pipelines for help-center documents, and embedding retrieval workflows to feed candidate passages into the Cohere Rerank 3 model for final scoring.
Integrations centered on the in-app support UI and Ramp’s help-center content repository, operationalizing the model for the customer support function and product-embedded assistance. Ramp reported a rapid two-week deployment in Cohere’s customer story, and the deployment scope emphasized runtime inference in support flows rather than broad back-office automation.
Post-deployment governance emphasized monitoring relevance signals and iterative tuning of ranking thresholds and content coverage to maintain precision. Ramp reported over 60% self-served outcomes, and Cohere Rerank 3 was cited as the component driving the improved retrieval precision and more accurate surfacing of help documents.
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Buyer Intent: Companies Evaluating Cohere Rerank 3
- Ascent Mcc Holdings Canada, a Canada based Professional Services organization with 10 Employees
- Macnica Hong Kong, a Hong Kong based Distribution company with 18 Employees
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