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Sometimes, it’s not about what you do, it’s about how you do it. At QuaerisAI, we chose to climb the mountain first, so our users have an easier walk.
We firmly believe that the current data environment is broken — we have a hierarchical, medieval data society of haves and have-nots. In a culture where a Jedi-certified person believes they are superior to a mere professional-certified person, then we have failed in democratizing data. A culture where a sales executive depends on someone else to answer the question “last month’s bookings by sales rep,” proves that only the chosen ones have the keys to the kingdom. That to me is reminiscent of medieval times.
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When the expectation of every BI tool, is to copy data to our cloud, it sounds like a 17th-century army that plundered and brought back riches to be enjoyed by a few. Why make another copy of data, when we know that doing so, adds latency, adds costs, limits the data that can be analyzed, limits the users, and creates yet another point of security failure? Yet, every single ‘cloud BI’ does that.
QuaerisAI enables and thrives on a strong partnership between IT and business. It firmly establishes IT as the custodian of the platform and enabler of data. For business users, QuaerisAI empowers them to consume that data without any limits, bars, or constraints of any kind. By fostering this partnership, our goal is simple and practical. We aim to help organizations reduce meaningful waste in how data work gets done. By 2025, wasteful spend across the US data economy is estimated to exceed $40B each year, with projections reaching $70–$80B by 2030 as data volumes, tooling, and operational complexity continue to rise.
Much of this waste shows up in data teams. Repeated requests. One off analyses. Manual reconciliation across systems. Long cycles between a question and a usable answer. We believe a measurable share of this drag can be reduced when teams spend less time rebuilding context and more time delivering clarity.
We are not promising overnight change. We are committing to steady, provable improvement. Less rework. Fewer stalled handoffs. More trust in the answers that move work forward.
Most importantly, we are focused on bringing data work into the present. Data should be usable when decisions are being made, not after the moment has passed. With AI and natural language at the core, data teams can shift from reactive support to durable, shared understanding across the organization.
Welcome to QuaerisAI.
