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List of Artificial Labs Platform Customers

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Logo Customer Industry Empl. Revenue Country Vendor Application Category When SI Insight
Bms Group Insurance 179 $81M United Kingdom Artificial Labs Artificial Labs Platform ML and Data Science Platforms 2023 n/a
In 2023, Bms Group partnered with Artificial Labs and adopted the Artificial Labs Platform to deploy its Contract Builder capability for digital contract creation and broker placing workflows in the London market. This implementation addresses Apps Category . The initial rollout began in February 2024 and focused on the Artificial Labs Platform Contract Builder and embedded data extraction functions to standardize contract drafting and improve placement efficiency for broking operations. The vendor reports pilots delivered up to ~50% reductions in contract creation time, and governance was structured around staged pilots and incremental broker workflow adoption within placement teams to validate the contract building and data extraction use cases.
Chaucer Group Insurance 500 $336M United Kingdom Artificial Labs Artificial Labs Platform ML and Data Science Platforms 2020 n/a
In 2020, Chaucer Group partnered with Artificial Labs to deploy the Artificial Labs Platform for underwriting in the UK London market. The engagement concentrated on digitising quote, bind and issue processes for high-volume specialty products and embedding machine-learning-enabled algorithmic underwriting to support faster decisioning. The implementation explicitly targeted Chaucer Group's underwriting function to improve responsiveness and speed to market. The Artificial Labs Platform was configured to operationalise algorithmic underwriting models alongside digital workflow orchestration for the quote, bind and issue lifecycles. Functional modules implemented included automated decisioning and rules-driven policy issuance workflows, with controls for exception routing to underwriters and preservation of manual underwriting authority. The deployment focused on scalable digital underwriting workflows consistent with underwriting category capabilities. Operational rollout was executed in the UK London market and applied to underwriting teams managing specialty lines, with governance framed around model management, decisioning controls and workflow change approvals. Process changes redefined handoffs between automated decisioning and manual review to maintain oversight during algorithmic underwriting. The platform was intended to improve underwriting responsiveness and speed to market while enabling more scalable digital underwriting workflows.
Chaucer Plc Insurance 500 $139M United Kingdom Artificial Labs Artificial Labs Platform ML and Data Science Platforms 2020 n/a
In 2020 Chaucer Plc partnered with Artificial Labs to deploy the Artificial Labs Platform as an Artificial Intelligence underwriting system for high-volume specialty insurance products. The collaboration was scoped to digitise Chaucer’s quote, bind and issue workflows, combining Chaucer’s underwriting expertise with machine learning driven risk selection and pricing models delivered through the Artificial Labs Platform. The implementation centered on a quote-to-issue application architecture, with a configurable quoting engine, automated binding and policy issuance workflows, and embedded machine learning scoring for risk selection and pricing. Chaucer configured the Artificial Labs Platform to support underwriter decisioning and broker-facing quote responsiveness, aligning UI workflows to underwriting practice and enabling underwriters to triage simple risks through automation while retaining manual controls for complex risks. Operational coverage targeted Chaucer’s specialty underwriting lines and broker distribution channels, with the platform designed to accommodate the complexities of specialist business lines the firm writes. Artificial Labs worked closely with Chaucer to validate data models and business rules, and Artificial’s access to Capita’s start up development arm, Capita Scaling Partner, was used to support rapid commercial scaling and platform adoption. Governance changes emphasized process leanification and operational scalability, shifting routine quote and issuance tasks onto the Artificial Labs Platform and freeing underwriters to spend more time with brokers and clients on complex placements. The deployment aimed to enhance underwriting responsiveness and speed to market while instituting data-driven pricing and selection workflows across Chaucer’s underwriting function.
Insurance 400 $300M United States Artificial Labs Artificial Labs Platform ML and Data Science Platforms 2023 n/a
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Buyer Intent: Companies Evaluating Artificial Labs Platform

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FAQ - APPS RUN THE WORLD Artificial Labs Platform Coverage

Artificial Labs Platform is a ML and Data Science Platforms solution from Artificial Labs.

Companies worldwide use Artificial Labs Platform, from small firms to large enterprises across 21+ industries.

Organizations such as Chaucer Group, Lockton, Chaucer Plc and Bms Group are recorded users of Artificial Labs Platform for ML and Data Science Platforms.

Companies using Artificial Labs Platform are most concentrated in Insurance, with adoption spanning over 21 industries.

Companies using Artificial Labs Platform are most concentrated in United Kingdom and United States, with adoption tracked across 195 countries worldwide. This global distribution highlights the popularity of Artificial Labs Platform across Americas, EMEA, and APAC.

Companies using Artificial Labs Platform range from small businesses with 0-100 employees - 0%, to mid-sized firms with 101-1,000 employees - 100%, large organizations with 1,001-10,000 employees - 0%, and global enterprises with 10,000+ employees - 0%.

Customers of Artificial Labs Platform include firms across all revenue levels — from $0-100M, to $101M-$1B, $1B-$10B, and $10B+ global corporations.

Contact APPS RUN THE WORLD to access the full verified Artificial Labs Platform customer database with detailed Firmographics such as industry, geography, revenue, and employee breakdowns as well as key decision makers in charge of ML and Data Science Platforms.