PromptQLthe factitious intelligence unicorn valued at over $1 billion is launching an unconventional consulting practice that puts its own AI engineers directly in front of Fortune 500 decision-makers for $900 an hour — a move that would disrupt traditional consulting firms struggling to deliver on their AI transformation guarantees.
The San Francisco-based company, which has quietly signed seven-figure contracts with among the world's largest corporations in recent months, announced this week that it “AI investment evaluation“Services designed to deal with current issues MIT Research The failure rate for enterprise AI implementations has been found to be 95%.
Unlike traditional consulting firms that send MBA graduates with limited technical experience, PromptQL's service leverages the actual engineers who’ve developed $2 billion price of products in three years. The consulting offering represents a big departure from the everyday provider-customer relationship and positions PromptQL as each a technology provider and a strategic advisor.
“Every data leader I check with – after I ask them what they measure, they don’t measure accuracy and adoption,” said Tanmai Gopal, co-founder and CEO of PromptQL, in an exclusive interview with VentureBeat. “You measure the info preparation goals. You measure things like, 'This quarter we're going to centralize all of our data into this data warehouse.' Do what they don't measure? Whether anyone will actually use it.
Why AI systems in corporations are “definitely unsuitable” and price corporations thousands and thousands
PromptQL's entry into consulting comes at a time when corporations are grappling with what the corporate calls “definitely unsuitable“Problem – AI systems that give unsuitable answers with unwavering certainty. This fundamental problem, Gopal argues, has created a universal problem.”Verification tax“Where business users must fact-check every AI response, stopping productivity gains.
“The biggest problem shouldn’t be with the ability to answer the query accurately,” Gopal explained. “The big problem is that the AI pretends to be accurate even when it isn't. So the AI is confidently unsuitable. That's the issue.”
The company's core innovation focuses on teaching AI systems to signal uncertainty and learn from feedback – capabilities that set it aside from traditional large language models that hallucinate information without confirmation. This approach made it possible PromptQL to realize what it guarantees to realize near-perfect accuracy for enterprise customers across all analytics and automation use cases.
From open source success to billion-dollar AI platform: the journey from Hasura to PromptQL
PromptQL emerged from it encounteredthe open source data access platform that achieved unicorn status by raising enterprise capital Lightspeed Ventures, Greenoaks, Vertex VenturesAnd Nexus Venture Partner. Hasuras GraphQL engine reached over a billion downloads and is utilized by 50% of Fortune 100 corporations.
“We went from 2 million to 100 million downloads in the primary yr,” said Gopal, describing Hasura’s explosive growth. This success enabled the team to tackle the following big challenge: enabling AI to accurately access and analyze enterprise data.
Two years ago, the team arrange a research laboratory from which the corporate grew PromptQLRecruiting talent Google search, Microsoft ResearchAnd Intuitive research. The timing proved prescient as corporations began to confront the constraints of general AI tools.
“We realized that apps aren’t any longer the first things that need access to data,” Gopal said. “The future is AI talking to data – AI accessing data on behalf of the user.”
Seven-figure contracts with Fortune 500 giants signal a shift available in the market for enterprise AI
PromptQL's customer base includes what Gopal describes as “the world's largest networking company with roughly $50.60 billion in revenue, the world's largest fast food chain, and the world's two largest food delivery corporations.” While the corporate can't yet publicly name those customers on account of ongoing legal approvals, it has closed seven-figure contracts in three to 5 months for a product that’s technically still in beta.
The scale of deployments reflects corporations' need for reliable AI systems. For corporations with high transaction volumes PromptQL processes nearly a petabyte of knowledge. For large sales organizations, deployments reach 25,000 users across multiple subsidiaries and bought corporations.
“We are doing seven-figure deals in three to 5 months for a product that continues to be in beta,” Gopal said. “The momentum is such that PromptQL is now our primary focus and we’re running the GraphQL API product on autopilot.”
How PromptQL's architecture solves enterprise AI's biggest technical challenges
PromptQL's architecture addresses enterprise AI challenges through three core innovations: one Agentic semantic layer that captures the evolving business context, including “Tribal knowledge“that exists only within the minds of employees; a domain-specific language that separates query planning from execution to avoid hallucinations; and a distributed query engine that accesses data across systems without the necessity for centralization.
The company's approach is in stark contrast to Retrieval Augmented Generation (RAG) Systems that treat all requests equally. Instead, PromptQL uses intent-driven routing to optimize various kinds of questions, achieving 26 to 90 times faster response times for easy queries while maintaining higher accuracy for complex analyses.
“Instead of generating answers, we create plans in a domain-specific language tailored specifically to your corporation,” Gopal explained. “These plans are compiled into deterministic actions with runtime validations and policy checks.”
AI engineers vs. McKinsey MBAs: How PromptQL goals to revolutionize the $200 billion consulting market
The launch of the advisory service goals at something PromptQL sees a fundamental mismatch between traditional consulting approaches and the fact of AI implementation. While McKinsey, Deloitteand other Big Four firms charge thousands and thousands for AI transformation strategies, but their success rates remain disappointingly low.
The contrast is stark: While traditional consulting giants spend thousands and thousands on AI transformation strategies that rarely deliver measurable results, PromptQL's tech-focused approach is already producing significant cost savings for first-time customers, despite being only a fraction of the value.
While the $900 hourly rate is premium, it represents a fraction of typical Big Four deployment costs while providing direct access to engineers who’ve successfully deployed AI systems in production. Early customers are reporting “thousands and thousands of dollars in savings” by replacing over-the-top AI systems with reliable alternatives.
The 95% AI Failure Rate: Why Most Enterprise Deployments Never Make It Past the Pilot Phase
PromptQL's advisory launch comes against the backdrop of widespread difficulties in AI deployment. This is shown by a current MIT study 95% of AI pilot projects in corporations fail to deliver measurable ROI, with value concentrated in a small minority of integrated, learning systems.
The failure rate is partly on account of treating AI as traditional software quite than recognizing its unique requirements for continuous learning and feedback. The focus of PromptQL is on measurement and evaluation – through so-called “GATs” (GenAI assessment tests) – closes this gap by providing concrete metrics for AI performance and business impact.
“Before you fund one other 'AI for X' pilot, ask: Will it tell me when it's unsafe – and why?” Gopal advises managers. “Is it learning from the correction I just gave? Will the following user avoid the identical trap without being asked again?”
The latest AI consulting playbook: How technical expertise could reshape strategic consulting services
PromptQL's move signals a broader shift in the way in which AI corporations approach corporate relationships. Rather than simply selling technology, leading AI corporations are positioning themselves as strategic partners who can guide implementation from conception to deployment.
This shift reflects the growing recognition that successful AI adoption requires greater than just technical solutions – it requires organizational change, process redesign, and cultural adaptation that traditional consultants often struggle to supply alongside technical depth.
The advisory service also creates a strong feedback loop for PromptQL's core platform. Each engagement generates insights into corporations' AI challenges that will be integrated into product development, potentially shortening the gap between them PromptQL and competitors who lack experience in direct implementation.
As more AI unicorns observe PromptQL's success, the consulting industry could also be facing its own “definitely unsuitable” moment: realizing that technical expertise, not only strategic frameworks, will determine whether enterprise AI initiatives succeed or join the 95% that never deliver value.
For an industry built on the premise that intelligence is more essential than implementation, PromptQL's bet is easy: When it involves AI, the engineers who develop the systems understand the business higher than the consultants who’ve never coded a line.
