London-based Ares Intelligence has raised approximately £2.5m from venture capital investors Creandum and Lakestar, alongside angel investors connected to Google DeepMind, to expand its platform for identifying opportunities to deploy artificial intelligence across businesses.
The investment follows the company’s establishment earlier this year by Matt Falconer and Alfie Ranstead, who previously worked together on automation business Instruct. Ares is developing technology that interviews employees about their working practices and identifies processes where AI could reduce repetitive work or improve efficiency.
The funding was reported at approximately £2.48m. Ares has not disclosed its valuation, the allocation made by each investor, or the ownership structure following the transaction.
Its platform conducts confidential, voice-based interviews in more than 70 languages, allowing employees across departments and international locations to describe their routine activities, operational difficulties, and existing use of technology.
The system analyses those conversations to identify manual tasks, inefficient handovers, duplicated work, and opportunities for automation. It then produces a prioritised implementation plan, ranking potential interventions according to their expected value, feasibility, and urgency.
The approach reflects a recurring difficulty encountered by organisations adopting AI. A business may have access to suitable software but lack a detailed understanding of which operational processes would benefit most from its deployment.
In larger organisations, that knowledge is often distributed among employees rather than held centrally by management. Staff working directly with customers, documents, administrative systems, or internal workflows may understand problems that are not visible through formal reporting structures.
Falconer previously worked at IBM and Automation Anywhere, gaining experience in enterprise automation. Ranstead has a software engineering background. Their earlier venture, Instruct, addressed automation requirements and helped establish the commercial relationships that informed the development of Ares.
By gathering information from employees, the company intends to give management teams a more detailed picture of how work is performed before they commit resources to individual AI applications.
Ares says it has secured enterprise customers in Europe and the United States during its first seven months of operation. Its published customer examples include London communications agency Words+Pixels, where the platform identified eight potential interventions.
The Words+Pixels assessment estimated that approximately £57,000 in annual staff capacity could be recovered by addressing inefficient manual work. That figure represents an identified opportunity rather than a verified reduction in operating expenditure.
The customer example also illustrates the distinction between identifying automation opportunities and implementing them. Recommended changes may still require software integration, revised processes, training, and ongoing supervision before their anticipated benefits can be realised.
Employee interviews introduce their own information management requirements. Discussions about internal workflows can involve commercially sensitive processes, staff responsibilities, and existing technology arrangements.
Ares offers configurable anonymisation, options for removing identifying information, and data hosting and retention settings intended to accommodate organisations with differing security and privacy requirements.
Its model also differs from a conventional consulting engagement built around workshops with selected managers. The company is seeking to collect information from a broader employee population and automate the initial assessment of operational requirements.
The investment will support development of the platform and expansion of its commercial activities. Ares enters that phase with backing from two established European venture firms and early customer relationships built around workforce-based assessments of AI opportunities.
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