Ascentra Labs, a London-based startup founded by former McKinsey consultants, has successfully raised $2 million in a seed funding round aimed at transforming the consulting industry through artificial intelligence (AI). This funding, led by Berlin-based venture capital firm NAP, marks a significant step towards addressing the long-standing resistance of the $250 billion consulting sector to adopt modern technological solutions, particularly AI. The investment also saw participation from notable angel investors, including Alan Chang, CEO of Fuse and former Chief Revenue Officer at Revolut, and Fredrik Hjelm, CEO of European e-scooter company Voi.
The consulting industry has been notably slow to embrace AI, especially when compared to sectors like law and accounting, which have seen substantial investments in AI-driven startups. Ascentra Labs aims to change this narrative by focusing on a specific pain point within the consulting workflow: the labor-intensive process of survey data analysis, particularly in private equity due diligence. This niche area is characterized by repetitive tasks that often require extensive hours spent in Excel, even at the most prestigious consulting firms.
Paritosh Devbhandari, co-founder and CEO of Ascentra, brings a wealth of experience from his tenure at McKinsey & Company, where he worked on the private equity team. His firsthand knowledge of the challenges consultants face while analyzing encoded survey responses has informed the development of Ascentra’s platform. The platform is designed to ingest raw survey data files and produce fully formatted Excel workbooks complete with traceable formulas—essentially automating a task that junior associates typically spend countless hours completing manually.
The disparity in AI adoption between legal and consulting sectors raises an important question: why has venture capital not flooded the consulting space despite its size and the manual nature of its workflows? Devbhandari provides insight into this issue, noting that while many startups have attempted to penetrate the market, the barriers to entry are significant. Professional services firms are notoriously cautious about technology adoption, often requiring extensive security credentials and customer references before even considering pilot opportunities. This slow-moving nature of consulting firms has stymied many startups, with Devbhandari estimating that 90% of them fail to navigate these hurdles.
Moreover, the technical challenges associated with consulting workflows are distinct from those in legal work. Consulting encompasses a variety of data modalities, including PowerPoint presentations, Excel spreadsheets, and Word documents, with information presented in tabular, graphical, or textual formats. This complexity makes it difficult for broad AI solutions to gain traction, as they often struggle to address the unique requirements of consulting tasks.
Ascentra’s strategy is built on extreme specificity. Rather than attempting to automate the entire spectrum of consulting work, the company focuses exclusively on survey analysis within private equity due diligence. This targeted approach allows Ascentra to leverage the repeatability of private equity workflows, where similar analyses recur across different deals. By concentrating on this niche, Ascentra positions itself against a less formidable competitive landscape, as even the largest consulting firms have yet to develop dedicated internal tools for this specific workflow.
The company claims that three of the world’s top five consulting firms are currently using its platform, with early adopters reporting time savings of 60% to 80% on active due diligence projects. However, due to the private nature of the consulting industry, Ascentra cannot publicly disclose the names of these clients. Devbhandari acknowledges the challenge of operating in such a secretive environment but emphasizes the importance of building trust and credibility with clients.
One of the critical challenges for any AI company operating in quantitative workflows is ensuring accuracy. In the context of consulting, where a single error in a financial model can have significant repercussions, the need for precision is paramount. Devbhandari highlights this as Ascentra’s central design challenge, noting that consultants require a very high degree of fidelity in their analyses. Even if an AI solution is 95% accurate, consultants may revert to Excel because they trust it and want to eliminate any margin for error.
To address this concern, Ascentra employs a hybrid technical approach. While the platform utilizes GPT-based models from OpenAI to interpret and ingest incoming data, the actual analysis relies on deterministic Python scripts that produce consistent and verifiable outputs. This combination ensures that the steps following data ingestion are deterministic, eliminating the risk of AI hallucinations—an issue that can undermine the credibility of analyses in high-stakes financial environments. The Excel writer connected to the product converts the analysis into live, traceable Excel formulas, providing consultants with the assurance they need to follow along with the calculations.
In addition to technical challenges, selling software to major consulting firms requires navigating a complex landscape of enterprise security requirements. These organizations handle sensitive client data across various industries, and vendor security assessments can take months to complete. Ascentra has proactively invested in obtaining enterprise-grade certifications, achieving SOC 2 Type II and ISO 27001 certifications, and is currently under audit for ISO 42001, an emerging standard for AI management systems. The company’s data handling policies reflect the sensitivity of its target market, with client data being deleted within 30 to 45 days based on contractual terms, and Ascentra does not use customer data to train its models.
Ascentra’s pricing model deviates from the subscription-based approach that dominates enterprise software. Instead, the company charges on a per-project basis, aligning with how consulting firms allocate budgets. This structure allows Ascentra to bypass central IT procurement hurdles, facilitating quicker adoption among consulting teams. However, this model also introduces revenue unpredictability, as the company’s success will depend on converting project-level usage into broader enterprise relationships. Devbhandari suggests that this transition is already underway, with submitted business cases for enterprise rollouts indicating positive momentum.
The implications of AI adoption in consulting extend beyond operational efficiencies; they raise questions about the future of consulting employment itself. Devbhandari challenges the notion that AI will eliminate consulting jobs, arguing instead that the role of consultants will evolve. He believes that the best solutions will emerge from individuals within the industry who understand the intricacies of their work and can build products around it. While the current influx of graduates into consulting roles often involves significant time spent on Excel and PowerPoint tasks, Devbhandari envisions a future where the nature of consulting work will be fundamentally transformed.
Ascentra plans to utilize its recent funding to expand its presence in the United States, where over 80% of its customers are already located. Devbhandari intends to relocate to the U.S. to lead the company’s go-to-market efforts, recognizing that the American consulting landscape is ripe for innovation and experimentation. The seed funding represents a strategic bet by NAP, with co-founder Stefan Walter emphasizing the overdue disruption in the consulting industry. He notes that while many knowledge work sectors have been reshaped by new technology, consulting has remained stubbornly manual.
As Ascentra moves forward, it faces the challenge of converting pilot programs into lasting enterprise contracts while fending off well-funded competitors who are likely to enter the space as the opportunity becomes more apparent. The company’s deliberate focus on survey analysis provides a defensible beachhead, but expanding into adjacent workflows will necessitate the development of entirely new products without sacrificing the domain expertise that Devbhandari considers Ascentra’s core advantage.
Oliver Thurston, Ascentra’s co-founder and chief technology officer, who previously led machine learning initiatives at Mathison AI, offers a candid assessment of the challenges ahead. He acknowledges that consulting workflows are uniquely complex and difficult to build products around, which explains why the space has remained relatively unchanged until now. However, he expresses confidence that the industry is on the brink of transformation, with no doubt that it will look significantly different in five years.
For now, Ascentra is making a focused wager: that the consultants who once spent countless nights formatting spreadsheets will be the ones to lead the AI transformation of their own industry. The irony is palpable; after years of advising Fortune 500 companies on digital transformation, the consulting sector may finally have to embrace its own medicine. Ascentra Labs stands at the forefront of this shift, poised to redefine how consulting firms operate in an increasingly digital world.