While artificial intelligence has stormed into law firms and accounting practices with billion-dollar startups like Harvey leading the charge, the global consulting industry—a $250 billion behemoth—has remained stubbornly analog. A London-based startup founded by former McKinsey consultants is betting $2 million that it can crack open this resistant market, one Excel spreadsheet at a time.
Ascentra Labs announced Monday that it has closed a $2 million seed round led by NAP, a Berlin-based venture capital firm formerly known as Cavalry Ventures. The funding comes with participation from notable founder-angels including Alan Chang, chief executive of Fuse and former chief revenue officer at Revolut, and Fredrik Hjelm, chief executive of European e-scooter company Voi.
The investment is modest by the standards of enterprise AI — a sector that has seen funding rounds routinely reach into the hundreds of millions. But Ascentra's founders argue that their focused approach to a narrow but painful problem could give them an edge in a market where broad AI solutions have repeatedly failed to gain traction.
Consultants spend countless hours on Excel survey analysis that even top firms haven't automated
Paritosh Devbhandari, Ascentra's co-founder and chief executive, spent years at McKinsey & Company, including a stint at QuantumBlack, the firm's AI and advanced analytics division. He knows intimately the late nights consultants spend wrestling with survey data—the kind of quantitative research that forms the backbone of private equity due diligence.
"Before starting the company, I was working at McKinsey, specifically on the private equity team," Devbhandari explained in an exclusive interview with VentureBeat. The work, he said, involves analyzing encoded survey responses from customers, suppliers, and market participants during potential acquisitions.
"Consultants typically spend a lot of time doing this in Excel," he said. "One of the things that surprised me, having worked at a couple of different places, is that the workflow — even at the best firms — really isn't that different from some of the boutiques. I always expected there would be some smarter way of doing things, and often there just isn't."
That gap between expectation and reality became the foundation for Ascentra. The company's platform ingests raw survey data files and outputs formatted Excel workbooks complete with traceable formulas — the kind of deliverable a junior associate would spend hours constructing manually.
AI has transformed legal work but consulting presents unique technical challenges that have blocked adoption
The disparity between AI adoption in law versus consulting raises an obvious question: if the consulting market is so large and the workflows so manual, why hasn't venture capital flooded the space the way it has legal tech?
Devbhandari offered a frank assessment. "It's not like people haven't tried," he said. "The top of the funnel in our space is crowded. When we speak to our consulting clients, the partners say they get another pitch deck in their LinkedIn inbox or email every week—sometimes several. There are plenty of people trying."
The barriers, he argued, are structural. Professional services firms move slowly on technology adoption, demanding extensive security credentials and customer references before granting even a pilot opportunity. "I think that's where 90% of startups in professional services, writ large, fall down," he said.
But consulting presents unique technical challenges beyond the sales cycle. Unlike legal work, which largely involves text documents that modern large language models handle well, consulting spans multiple data modalities — PowerPoint presentations, Excel spreadsheets, Word documents — with information that can be tabular, graphical, or textual.
"You can have multiple formats of Excel in itself," Devbhandari noted. "And that's a big contrast to the legal space, where you could have a multi-purpose AI agent, or collection of agents, which can actually do a lot of the tasks that lawyers do day to day. Consulting is the opposite of that."
Ascentra's private equity focus reflects a calculated bet on repeatable workflows
Ascentra's strategy hinges on extreme specificity. Rather than attempting to automate the full spectrum of consulting work, the company focuses exclusively on survey analysis within private equity due diligence — a niche within a niche.
The logic is both technical and commercial. Private equity work tends to be more standardized than other consulting engagements, with similar analyses recurring across deals. That repeatability makes automation feasible. It also positions Ascentra against a less formidable competitive set: even the largest consulting firms, Devbhandari claimed, lack dedicated internal tools for this particular workflow.
"Survey analysis automation is so specific that even the biggest and best firms haven't developed anything in-house for it," he said.
The company claims that three of the world's top five consulting firms now use its platform, with early adopters reporting time savings of 60 to 80 percent on active due diligence projects. But there's a notable caveat: Ascentra cannot publicly name any of these clients.
"It's a very private industry, so at the moment, we can't announce any clients publicly," Devbhandari acknowledged. "What I can say is that we're working with three of the top five consulting firms. We've passed pilots at multiple organizations and have submitted business cases for enterprise rollouts."
Eliminating AI hallucinations becomes critical when billion-dollar deals hang in the balance
For an AI company selling into quantitative workflows, accuracy is existential. Consultants delivering analysis to private equity clients face enormous pressure to be precise—a single error in a financial model can undermine credibility and, potentially, billion-dollar investment decisions.
Devbhandari described this as Ascentra's central design challenge. "Consultants require a very, very high degree of fidelity when they're doing their analysis," he said. "So with quantitative data, even if it's 95% accurate, they will revert to Excel because they know it, they trust it, and they don't want there to be any margin for error."
Ascentra's technical approach attempts to address this by limiting where AI models operate within the workflow. The company uses GPT-based models from OpenAI to interpret and ingest incoming data, but the actual analysis relies on deterministic Python scripts that produce consistent, verifiable outputs.
"What's different is the steps that follow are deterministic," Devbhandari explained. "There's no room for error. There's no hallucinations, and the Excel writer that we've connected to the product on the back end converts this analysis into Excel formula, which are live and traceable, so consultants can get that assurance that they can follow along with the maths."
Whether this hybrid approach delivers on its promise of eliminating hallucinations while maintaining useful AI capabilities will be tested as the platform scales across more complex use cases and client environments.
Enterprise security certifications give Ascentra an edge over less prepared competitors
Selling software to major consulting firms requires clearing an unusually high security bar. These organizations handle sensitive client data across industries, and their vendor security assessments can take months to complete.
Ascentra invested early in obtaining enterprise-grade certifications, a strategic choice that Devbhandari framed as essential table stakes. The company has achieved SOC 2 Type II and ISO 27001 certifications and claims to be under audit for ISO 42001, an emerging standard for AI management systems.
Data handling policies also reflect the sensitivity of the target market. Client data is deleted within 30 to 45 days, depending on contractual terms, and Ascentra does not use customer data to train its models.
There's also an argument that survey data carries somewhat lower sensitivity than other consulting materials. "Survey data is unique in consulting data because it's collected during the course of a project, and it is market data," Devbhandari noted. "You interview people in the market, and you collect a bunch of data in an Excel, as opposed to—you look at Rogo or some of the other finance AI startups—they use client data, so financials, which is confidential and strictly non-public."
Per-project pricing aligns with how consulting firms actually spend money
Ascentra's pricing model departs from the subscription-based approach that dominates enterprise software. The company charges on a per-project basis, a structure Devbhandari said aligns with how consulting firms allocate budgets.
"Project budgets are in consulting set on a per project basis," he explained. "You'll have central budgets which are for things like Microsoft, right, very central things that every team will use all of the time. And then you have project budgets which are for the teams that are using specific resources, teams or products nowadays."
This approach may ease initial adoption by avoiding the need for central IT procurement approval, but it also introduces revenue unpredictability. The company's success will depend on converting project-level usage into broader enterprise relationships—a path Devbhandari suggested is already underway through submitted business cases for enterprise rollouts.
AI may not eliminate consulting jobs, but it will fundamentally transform what consultants do
Perhaps the most interesting tension in Devbhandari's vision concerns what AI ultimately means for consulting employment. He pushed back on predictions that AI will eliminate consulting jobs while simultaneously describing an industry on the cusp of fundamental transformation.
"People love to talk about how AI is going to remove the need for consultants, and I disagree," he said. "Yes, the role will change, but I don't think the industry goes away. I think the best solutions will come from people within the industry building products around the work they know."
Yet he also painted a picture of dramatic change. "At the moment, you have a big intake of graduates who just do—for the most part, you know, they have the strategic work as part of what they do, but they also have a lot of work in Excel and PowerPoint. I think in a few years' time, we'll look back at these times and think, you know, very, very different."
The honest answer, he acknowledged, is that no one truly knows how this plays out. "I don't think even AI leaders truly know what that looks like yet," he said of whether productivity gains will translate to more work or fewer workers.
Ascentra plans to use seed funding to expand its U.S. presence and go-to-market team
The $2 million will primarily fund Ascentra's expansion into the United States, where more than 80 percent of its customers are already based. Devbhandari plans to relocate there personally as the company builds out go-to-market capabilities.
"One of the things that we've really noticed is that with consulting being an American industry, and I think America being a great place for innovation and trying new things, we've definitely drawn ourselves to the U.S.," he said. "American hires are very expensive, and I'm sure that a lot of the raise will go towards that."
The seed round represents a bet by NAP on what its co-founder Stefan Walter called an overdue disruption. "While most knowledge work has been reshaped by new technology, consulting has remained stubbornly manual," Walter said. "AI won't replace consultants, but consultants using Ascentra might."
The startup now faces the hard work of converting pilot wins into lasting enterprise contracts
Ascentra enters 2026 with momentum but no guarantee of success. The company must transform pilot programs at elite firms into sticky enterprise contracts — all while fending off the inevitable well-funded competitors who will flood into the space once the opportunity becomes undeniable. Its deliberately narrow focus on survey analysis provides a defensible beachhead, but expanding into adjacent workflows will require building entirely new products without sacrificing the domain expertise that Devbhandari argues is the company's core advantage.
Oliver Thurston, Ascentra's co-founder and chief technology officer, who previously led machine learning at Mathison AI, offered a clear-eyed assessment of the challenge. "Consulting workflows are uniquely complex and difficult to build products around," he said in a statement. "It's not surprising the space hasn't changed yet. This will change though, and there's no doubt that the industry is going to look completely different in five years' time."
For now, Ascentra is placing a focused wager: that the consultants who once spent their nights formatting spreadsheets will be the ones who finally bring AI into an industry that has long resisted it. The irony is hard to miss. After years of advising Fortune 500 companies on digital transformation, consulting may finally have to take its own medicine.
