Ascentra Labs raises $2 million to automate consulting Excel work with AI

5 months ago7 min read

The global consulting industry, a $250 billion behemoth, has long presented a paradox in the age of automation. While AI has fundamentally reshaped adjacent fields like legal tech—propelling startups like Harvey to billion-dollar valuations—the core workflows of strategy and due diligence have remained stubbornly manual, anchored in the familiar but laborious world of Excel spreadsheets.A new London-based venture, Ascentra Labs, is betting that a highly focused, technically nuanced approach can finally crack this resistant market. The startup, founded by former McKinsey consultants, has just secured a $2 million seed round led by Berlin’s NAP Ventures, with participation from notable angels like Alan Chang of Fuse and Fredrik Hjelm of Voi.While modest by the eye-watering standards of enterprise AI funding, this investment underscores a calculated wager: that solving a narrow, painful problem with extreme precision offers a more viable path to adoption than broad, generic AI solutions that have repeatedly failed to gain traction in this skeptical sector. Ascentra’s co-founder and CEO, Paritosh Devbhandari, brings firsthand experience from McKinsey’s QuantumBlack AI division, where he witnessed the persistent gap between technological potential and on-the-ground reality.The specific pain point he targets is the analysis of encoded survey data in private equity due diligence, a quantitative task that consumes countless hours of junior consultants’ time, even at elite firms. ‘I always expected there would be some smarter way of doing things, and often there just isn’t,’ Devbhandari noted in an interview, highlighting the industry’s surprising lack of internal automation for such repetitive work.The technical challenge here is distinct from domains like legal tech, where large language models excel at processing text. Consulting workflows are multimodal, juggling PowerPoint, Excel, and Word documents with tabular, graphical, and textual data interwoven.This complexity, combined with professional services firms’ glacial adoption cycles and stringent security demands, has historically blocked AI incursions. Ascentra’s strategy to overcome this involves a hybrid technical architecture.It uses GPT models for initial data ingestion and interpretation but crucially relies on deterministic Python scripts for the core analysis, outputting traceable Excel formulas to eliminate the hallucination risk that would be fatal in billion-dollar deal environments. This design philosophy reflects a deeper understanding of the consultant’s need for verifiable fidelity over probabilistic cleverness.Furthermore, the company’s early investment in SOC 2 Type II and ISO 27001 certifications, alongside a per-project pricing model that mirrors consulting budget cycles, is a deliberate play to meet the industry on its own terms. The startup claims three of the world’s top five consulting firms as early, albeit unnamed, clients, reporting time savings of 60-80% on active projects.The road ahead, however, is fraught. Converting pilot wins into lasting enterprise contracts requires navigating opaque procurement processes and expanding from this niche beachhead into adjacent workflows without diluting the domain expertise that is its current advantage.As Oliver Thurston, Ascentra’s CTO and a former machine learning lead, observed, consulting workflows are uniquely complex to productize. Yet, the disruption seems inevitable.The broader implication, as Devbhandari suggests, is not the elimination of consultants but a fundamental transformation of the role. The industry may finally be forced to ingest its own digital transformation medicine, moving from an era of manual spreadsheet wrangling to one augmented by deterministic, trustworthy AI—a shift that could redefine the very nature of strategic advisory work within the decade.

#Ascentra Labs

#AI for consulting

#survey analysis automation

#private equity due diligence

#enterprise software

#seed funding

#featured

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