@airquery is the Analytics Agent, powered by ThinkBox Intelligence.
Thinkbox is a contextual harness for trusted data analytics, created and managed by your own subject matter experts. Your tables, the relationships between them, the metrics your team has agreed on and the rules that govern them are assembled into a living hypergraph the agent reasons over — so where ordinary AI guesses, AirQuery reasons, and the same question returns the same answer every time, with sources you can verify and a trace you can follow.
Explore Thinkbox →The era of guessing AI is over. AirQuery pairs Artificial Intelligence with Human Intelligence to produce answers that are reproducible, auditable, and safe to act on — the only kind of analytics worth betting the business on.
Show revenue broken down by Ship Mode
LLMs that understand natural language. Pattern recognition at scale. Creative hypotheses.
Verified metrics. Domain rules. Institutional context. Approval from the people who own the data.
Same question, same answer. Every time. Cited, sourced, and auditable. The only kind of analytics worth betting the business on.
The Wise App lives inside Slack and Microsoft Teams — mention @airquery in any channel or thread and get a reasoned, sourced answer in seconds. No app to open. No dashboard to find. The agent comes to the conversation.
APAC revenue fell 22% in Q2 driven by 3 enterprise churns in Singapore totaling $840k ARR: Acme, Globex, Initech. Renewal risk for the segment is now High.
orders × regions × customersNo new dashboard to learn. @airquery in any Slack channel or Teams chat — group, DM, or thread.
Every answer is reasoned, sourced, and reproducible. Same input, same answer — auditable for finance, safe for ops.
Decisions become threads. Pin answers, set alerts, share with one click — the work happens where it’s already happening.
AirQuery reasons directly over your data warehouse or operational database — no copies, no movement, no surprise bills. Bring your own data; keep your governance.
Snowflake
BigQuery
Databricks
Redshift
Athena
Azure Synapse
Postgres
MySQL
MongoDB
SQL Server
DuckDB
Oracle
Iceberg
dbtDon’t see your warehouse? AirQuery speaks ANSI SQL — if it has a JDBC driver, we can read it. (Native data pipelines & managed ingestion coming soon.)
forecasts × gl_entries × deals from ERP (Oracle, Workday, SAP), Salesforce & the planning model.Gap of $1.2M driven by slipped enterprise renewals and FX headwind in EU. Forecast confidence revised to 62%.
attribution × subscriptions × events from HubSpot, Stripe & Segment.Referral has 4.2× LTV vs Paid Search. Recommended reallocating $80k/mo from Google Ads into the referral program.
shipments × vendor_invoices × skus from SAP, ShipHero & the cost model.Margin compression of 4.1pts in the Midwest DC — root cause: 3 vendors raised prices in May without sourcing being notified.
events × plans × accounts from Mixpanel, Stripe & the user table.Accounts using shared workspaces + API in week 1 upgrade at 38% vs 4% baseline. PLG signal added to growth model.
A single endpoint that speaks SQL, English, or MCP. Ship deterministic analytics inside your own product in an afternoon.
Read the docs →# Ask AirQuery anything — get answer + reasoning trace from airquery import Client aq = Client(api_key="aq_live_...") result = aq.ask( "top 10 at-risk customers ranked by revenue", context="renewals_q4", deterministic=True, ) print(result.answer) # natural language print(result.dataframe) # pandas DF print(result.reasoning) # six-mode trace print(result.sql) # auditable SQL print(result.confidence) # 0.0 – 1.0 (reflective)
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