While artificial intelligence has stormed into law firms and accounting practices with billion-dollar startups like Harvey leading the charge, the worldwide 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 could actually crack open this resistant market, one Excel spreadsheet at a time.
Ascentra Labs announced Tuesday that it has closed a $2 million seed round led by NAP, a Berlin-based enterprise capital firm formerly generally 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 a whole lot of hundreds of thousands. 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 realize traction.
Consultants spend countless hours on Excel survey evaluation 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 type of quantitative research that forms the backbone of personal equity due diligence.
"Before starting the corporate, I used to be 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 loads of time doing this in Excel," he said. "One in every of the things that surprised me, having worked at a pair of various places, is that the workflow — even at one of the best firms — really isn't that different from a few of the boutiques. I all the time expected there could be some smarter way of doing things, and sometimes there just isn't."
That gap between expectation and reality became the muse for Ascentra. The corporate's platform ingests raw survey data files and outputs formatted Excel workbooks complete with traceable formulas — the type of deliverable a junior associate would spend hours constructing manually.
AI has transformed legal work but consulting presents unique technical challenges which have blocked adoption
The disparity between AI adoption in law versus consulting raises an obvious query: if the consulting market is so large and the workflows so manual, why hasn't enterprise capital flooded the space the best way it has legal tech?
Devbhandari offered a frank assessment. "It's not like people haven't tried," he said. "The highest of the funnel in our space is crowded. Once we speak to our consulting clients, the partners say they get one other pitch deck of their LinkedIn inbox or email every week—sometimes several. There are many people trying."
The barriers, he argued, are structural. Skilled services firms crawl on technology adoption, demanding extensive security credentials and customer references before granting even a pilot opportunity. "I feel that's where 90% of startups in skilled 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 might be tabular, graphical, or textual.
"You possibly can have multiple formats of Excel in itself," Devbhandari noted. "And that's a giant contrast to the legal space, where you might have a multi-purpose AI agent, or collection of agents, which may actually do loads of the tasks that lawyers do day after day. Consulting is the other of that."
Ascentra's private equity focus reflects a calculated bet on repeatable workflows
Ascentra's strategy hinges on extreme specificity. Slightly than attempting to automate the complete spectrum of consulting work, the corporate focuses exclusively on survey evaluation inside private equity due diligence — a distinct segment inside a distinct segment.
The logic is each technical and industrial. 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 most important consulting firms, Devbhandari claimed, lack dedicated internal tools for this particular workflow.
"Survey evaluation automation is so specific that even the largest and best firms haven't developed anything in-house for it," he said.
The corporate 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 lively due diligence projects. But there's a notable caveat: Ascentra cannot publicly name any of those clients.
"It's a really private industry, so in the intervening time, we are able to't announce any clients publicly," Devbhandari acknowledged. "What I can say is that we're working with three of the highest 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 within the balance
For an AI company selling into quantitative workflows, accuracy is existential. Consultants delivering evaluation to non-public 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 really, very high degree of fidelity once they're doing their evaluation," he said. "So with quantitative data, even when it's 95% accurate, they are going to revert to Excel because they comprehend it, they trust it, and so they don't want there to be any margin for error."
Ascentra's technical approach attempts to deal with this by limiting where AI models operate throughout the workflow. The corporate uses GPT-based models from OpenAI to interpret and ingest incoming data, however the actual evaluation 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 author that we've connected to the product on the back end converts this evaluation into Excel formula, that are live and traceable, so consultants can get that assurance that they will follow together with the maths."
Whether this hybrid approach delivers on its promise of eliminating hallucinations while maintaining useful AI capabilities will probably be tested because 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 finish.
Ascentra invested early in obtaining enterprise-grade certifications, a strategic alternative that Devbhandari framed as essential table stakes. The corporate 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 goal market. Client data is deleted inside 30 to 45 days, depending on contractual terms, and Ascentra doesn’t use customer data to coach its models.
There's also an argument that survey data carries somewhat lower sensitivity than other consulting materials. "Survey data is exclusive in consulting data since it's collected throughout the course of a project, and it’s market data," Devbhandari noted. "You interview people available in the market, and also you collect a bunch of information in an Excel, versus—you take a look at Rogo or a few 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 corporate 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 that are for things like Microsoft, right, very central things that each team will use all the time. After which you might have project budgets that are for the teams which are using specific resources, teams or products nowadays."
This approach may ease initial adoption by avoiding the necessity for central IT procurement approval, nevertheless it also introduces revenue unpredictability. The corporate's success will rely 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 can fundamentally transform what consultants do
Perhaps probably 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 concurrently describing an industry on the cusp of fundamental transformation.
"People like to speak about how AI goes to remove the necessity for consultants, and I disagree," he said. "Yes, the role will change, but I don't think the industry goes away. I feel one of the best solutions will come from people throughout the industry constructing products across the work they know."
Yet he also painted an image of dramatic change. "In the mean time, you might have a giant intake of graduates who just do—for probably the most part, you recognize, they’ve the strategic work as a part of what they do, but additionally they have loads of work in Excel and PowerPoint. I feel in just a few years' time, we'll look back at these times and think, you recognize, very, very different."
The honest answer, he acknowledged, is that nobody truly knows how this plays out. "I don't think even AI leaders truly know what that appears like yet," he said of whether productivity gains will translate to more work or fewer staff.
Ascentra plans to make use of seed funding to expand its U.S. presence and go-to-market team
The $2 million will primarily fund Ascentra's expansion into the USA, where greater than 80 percent of its customers are already based. Devbhandari plans to relocate there personally as the corporate builds out go-to-market capabilities.
"One in every of the things that we've really noticed is that with consulting being an American industry, and I feel America being a fantastic place for innovation and trying recent things, we've definitely drawn ourselves to the U.S.," he said. "American hires are very expensive, and I'm sure that loads 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 recent technology, consulting has remained stubbornly manual," Walter said. "AI won't replace consultants, but consultants using Ascentra might."
The startup now faces the labor of converting pilot wins into lasting enterprise contracts
Ascentra enters 2026 with momentum but no guarantee of success. The corporate must transform pilot programs at elite firms into sticky enterprise contracts — all while warding off the inevitable well-funded competitors who will flood into the space once the chance becomes undeniable. Its deliberately narrow concentrate on survey evaluation provides a defensible beachhead, but expanding into adjoining workflows would require constructing entirely recent products without sacrificing the domain expertise that Devbhandari argues is the corporate'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 construct products around," he said in an announcement. "It's not surprising the space hasn't modified yet. This may change though, and there's little question that the industry goes 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 probably be those who finally bring AI into an industry that has long resisted it. The irony is tough to miss. After years of advising Fortune 500 corporations on digital transformation, consulting may finally should take its own medicine.
