AirQuery exists for one reason — to put the power of real analytics in the hands of every business user, not just the data team. We're a software company headquartered in Alpharetta, Georgia, building the foundations of Deterministic Intelligence for the data-driven enterprise.
The world has more data than ever, and yet the average business question still takes days to answer — if it gets answered at all. We started AirQuery to fix that gap. Not with more dashboards. Not with more LLM noise. With a structured AI harness that produces deterministic, sourced, reproducible answers — in the language your team already uses, against the data you already have.
We spent years building and selling BI tools. We watched the same scene repeat itself in every customer meeting: a business leader has a question, an analyst opens a ticket, a dashboard ships three days later — usually answering a different question than the one that was asked. Most decisions kept happening on gut. Then generative AI arrived, and somehow made things worse — confidently producing plausible numbers that nobody could verify. We saw the same problem from both sides: the answers people get aren't trustworthy, and the trustworthy answers take too long. AirQuery is the third option.
Pretty answers that change every time are worse than plain answers that stay the same. We optimise for repeatability before flash.
Every answer ships with the sources, the SQL, and the reasoning. If you can't audit it, you can't trust it — and we won't claim it.
The CFO with a question matters more than the design system. The analyst who needs to ship matters more than the architecture purity. Profit follows.
We build for the next year and the next decade simultaneously. We resist hype cycles and we resist false modesty in equal measure.
Jargon hides bad ideas. Our marketing reads like our engineering reads like our customer calls. If we can't say it cleanly, we don't ship it yet.
Every change goes through evals. Every answer carries its lineage. Every customer can audit what we shipped. Trust isn't a marketing claim — it's a build process.
AirQuery is built by people who've spent careers in BI, analytics, and applied AI — engineers, data scientists, designers, and customer-facing operators who've seen the same problems from every angle. We hire for taste, depth, and the willingness to take ownership of an outcome end-to-end. We work in small teams with short feedback loops, ship fast against a clear product spine, and resist the urge to grow headcount just because we can.
Distributed systems, neuro-symbolic reasoning, semantic modelling, query optimisation, and the unglamorous reliability work that makes deterministic answers possible.
The Wise App, the Embed SDK, the docs experience. The team that decides which idea ships and which gets cut, and how every surface looks and feels.
Solutions architects who deploy AirQuery in customer environments. They've built Thinkmaps in finance, retail, healthcare, manufacturing, and SaaS. They know what works.
Sales, marketing, and partnerships. The people closest to "what customers say when they're being honest." Their notes drive the roadmap as much as anyone else's.
SOC 2 Type II, GDPR, HIPAA, and the architecture reviews behind them. Embedded with engineering, not bolted on at the end.
Finance, people, legal — the team that keeps a 100-person company running like a 10-person one. Quietly indispensable.
We operate in small product teams (typically 4–8 people) with full ownership of an outcome. Most communication is asynchronous; most planning is quarterly; most retros are honest. We close out an OKR cycle every quarter and we publish what we shipped.
We use @airquery internally to answer most of our own questions about the company. (It's how we eat our own cooking.)
Engineering, customer engineering, product, design, GTM. If you've spent time in BI, analytics, applied AI, or the customer-facing side of any of those — and you care about the difference between an answer and a real answer — we'd like to talk.
Or try the product first — the fastest way to understand AirQuery is to ask it a question about your own data.
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