List of Cecilia AI Customers
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Since 2010, our global team of researchers has been studying Cecilia AI 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 Cecilia AI 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 Cecilia AI for Generative AI Platforms include: Perkins Coie, a United States based Professional Services organisation with 2500 employees and revenues of $1.16 billion, Watson Farley & Williams, a United Kingdom based Professional Services organisation with 2000 employees and revenues of $316.0 million, Mourant Ozannes, a United Kingdom based Professional Services organisation with 700 employees and revenues of $100.0 million and many others.
Contact us if you need a completed and verified list of companies using Cecilia AI, 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 Cecilia AI 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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Mourant Ozannes | Professional Services | 700 | $100M | United Kingdom | CS DISCO | Cecilia AI | Generative AI Platforms | 2022 | n/a |
In 2022, Mourant Ozannes deployed Cecilia AI as part of a Generative AI Platforms initiative to scale an in-house eDiscovery service across its litigation and consulting teams. The firm expanded a strategic relationship with CS DISCO to embed DISCO’s Cecilia AI review tools, instrumenting AI-powered document review and relevance-ranking capabilities to accelerate matter intake and review triage.
The implementation emphasized reviewer workflow orchestration and automated review queues, using Cecilia AI to centralize document culling, predictive relevance scoring, and reviewer assignment logic. Configuration work focused on case-level review playbooks, evidence tagging conventions, and model tuning consistent with eDiscovery best practices for defensible review.
Operational coverage targeted Mourant’s Channel Islands and United Kingdom practices, with the deployment shifting reviewer effort away from manual line-by-line review toward supervised AI-assisted workflows. The roll out applied across litigation and consulting business functions, producing internal playbooks and governance protocols to codify reviewer responsibilities and escalation paths.
Governance and process changes were formalized through those playbooks and protocols, aligning review standards and defensibility on large matters while enabling repeatable operational procedures. Cecilia AI retained its role as the firm’s Generative AI Platforms component for AI-assisted review and ongoing in-house eDiscovery operations.
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Perkins Coie | Professional Services | 2500 | $1.2B | United States | CS DISCO | Cecilia AI | Generative AI Platforms | 2024 | n/a |
In 2024, Perkins Coie collaborated with CS DISCO as an early user of Cecilia AI, implementing the Cecilia Auto Review generative module to accelerate first-pass document review. This implementation sits squarely within Generative AI Platforms and was used on large-scale matters to augment eDiscovery review workflows.
The deployment focused on automating first-pass review tasks using generative AI capabilities, including document summarization, issue spotting, and assisted relevance prioritization to streamline reviewer triage. Perkins Coie leveraged Cecilia AI to surface candidate responsive documents and to speed initial coding workflows, aligning the application with standard eDiscovery review functional terminology.
Operationally the work was embedded into U.S. eDiscovery workflows across litigation and investigations teams, with use concentrated on high-volume matters where first-pass throughput mattered most. The implementation emphasized reviewer-assisted automation rather than full autonomy, keeping manual review oversight within the firm’s matter teams.
Rollout was conducted as a pilot collaboration with DISCO, using iterative feedback from reviewers and matter teams to refine prompts and review guidance. During pilots Perkins Coie reported notable gains in review speed and accuracy, helping teams reduce review time and improve responsiveness on client matters.
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Watson Farley & Williams | Professional Services | 2000 | $316M | United Kingdom | CS DISCO | Cecilia AI | Generative AI Platforms | 2024 | n/a |
In 2024, Watson Farley & Williams piloted Cecilia AI from CS DISCO to support complex, multi jurisdictional disputes. Watson Farley & Williams implemented Cecilia AI as a Generative AI Platforms solution for legal eDiscovery and dispute document review, exercising DISCO’s Cecilia generative AI suite including Q&A, document summarization, timelines and Auto Review on a matter with more than 3 million documents.
The deployment spanned Europe and Asia and focused on document analysis workflows within the firm’s disputes teams, applying Cecilia AI capabilities to intake, automated review triage, summarization and timeline construction to accelerate disclosure preparation. Governance was organized around a pilot with CS DISCO as strategic eDiscovery partner and an operational rollout targeted at improving review speed and accuracy; the engagement enabled the firm to meet tight disclosure timelines while reducing costs on the referenced matter.
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