Anand Shah

The Consultant Who Automated Himself

At age six, Anand Shah was already working. Not in a metaphorical "entrepreneurial spirit" sense - literally behind the counter of his father's shop in London, watching a man who put in 13-hour days, 51 weeks a year for three decades. That particular lesson in effort-without-complaint gets carried forward in everything Shah does. When he decided to leave a 15-year career as an Accenture strategy partner to build an AI startup, he bootstrapped it for three years before accepting a single pound of venture capital.

The origin story of Databook is the kind that sounds tidy in retrospect but was years of observation. Somewhere around 2009, while deep inside a Scandinavian conglomerate's strategic review, Shah had a thought that became the next decade of his life: everything he was doing manually - the research, the synthesis, the personalized strategic narrative built from public and proprietary data - was repeatable. It was just nobody had written the software yet.

He filed the thought away. He kept running Accenture's Strategic Insights practice globally. He got an MBA from Columbia and London Business School simultaneously - a joint programme that gave him a ringside seat to how the world's most sophisticated corporations think about value creation. He led Accenture's engagement with the World Economic Forum on Digital Transformation, which meant walking into Davos with something to say about where enterprise technology was heading.

In 2016, he called Alex Barrett. They had first met at 13, in a London high school - Barrett went to Cambridge, Shah went to Imperial College London for computer science, and they'd stayed in the orbit of each other's lives ever since. Together with Govind Moghekar, they co-founded Databook. The premise was precise: enterprise sales teams at the world's largest software companies were showing up to C-suite meetings without the kind of strategic context that a good consultant would spend weeks preparing. Databook would be the consultant that never slept.

They built for three years without institutional capital. Shah's conviction - informed by watching how many startups optimize for fundraising rather than product-market fit - was that they should know exactly what they had before explaining it to investors. By the time Threshold Ventures, M12 (Microsoft's venture fund), and Salesforce Ventures arrived with seed funding, Databook had something real: customers, revenue, and a thesis that held water.

The 2022 Series B told the story in numbers. Bessemer Venture Partners led a $50M round at a $550M valuation. DFJ Growth, M12, Salesforce Ventures, Threshold, and Haystack all participated. The announcement noted 300% consecutive year-over-year growth over four years. Customers like Microsoft, Salesforce, Databricks, and Konica Minolta were not using Databook as a nice-to-have. They were counting on it to close the deals that matter.

What Databook built was never just a sales tool. The platform is described as a "decision system for enterprise sales" - a layer of verified intelligence and structured reasoning that gives sellers the context to navigate complex, multi-stakeholder deals. The product surfaces financial performance data, strategic priorities, executive intelligence, and competitive context, then activates that intelligence through guided workflows. Sellers using Databook don't just research faster. They sell differently.

The latest chapter is agentic AI. Shah has been explicit about where enterprise software is going: autonomous agents that don't just surface information but take action. In 2024, Databook deployed an agentic workflow with a $1 billion enterprise software company that scaled to 450 accounts within two months - helping the customer confirm they were sitting on customer value that was twice what they'd initially estimated. In 2024, DatabookGPT launched its integration with Microsoft Copilot for Sales, planting Databook's intelligence directly inside the tool that millions of enterprise sellers already use every day.

The board has the gravity to match. Former Oracle CFO Jeff Epstein joined in 2022. Paul Daugherty - Accenture's Chief Technology and Innovation Officer, one of the most cited voices in enterprise AI - joined in January 2025. The advisory board now includes Scott Barghaan from Salesforce, Shreyas Doshi who shaped product management at Stripe and Twitter, and Nancy Harlan from UiPath. These aren't vanity appointments. These are people who understand what "enterprise-scale" actually means.

In 2023, Databook launched what it called the industry's first Strategic Relationship Management (SRM) platform - a deliberate reframing. CRM manages transactions; SRM manages relationships. The distinction matters to Shah, who watched for 15 years how the most effective salespeople worked: not as vendors moving product, but as strategic partners who understood their customers' business better than some of the customer's own teams did. The SRM platform tries to put that capability at the fingertips of every enterprise salesperson, not just the top 1%.

Shah's personal motto - "Don't expect behavioral change" - might sound cynical from the outside. From the inside, it's a product philosophy. Enterprise software lives or dies by adoption. Products that ask sellers to dramatically change how they work get ignored. Databook is designed to fit inside the workflows people already have: inside Salesforce, inside Microsoft Copilot, inside Slack. The intelligence comes to the seller; the seller doesn't have to go find it.

He carries three generations of entrepreneurship in his approach. His grandfather built one of India's largest construction and engineering businesses. His father, who fled Uganda during Idi Amin's expulsion of South Asians in the early 1970s, rebuilt his life in England as a small business owner - 13-hour days, seven days a week, year after year. Shah absorbed that work ethic and combined it with something his consulting career gave him: an unusually precise understanding of how large organizations make decisions, and what they're actually willing to pay for.

At 17, he co-founded his first company - Information Design, which built websites for local businesses during the early internet's strange optimistic days. At 14, he'd gotten his first computer. By the time he was building Databook in his 40s, he'd been doing this for three decades already. The startup looked like an overnight success to the people watching. From the inside, it was the accumulation of a life's worth of pattern recognition finally finding its application.