@airquery is the Analytics Agent.
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
Thinkboxes are contextual harnesses for trusted data analytics, created and managed by subject matter experts. Where regular AI may guess, a Thinkbox reasons — combining the language understanding of modern AI with the business knowledge that exists within a specific domain: the metrics your team has agreed on, the relationships in your data, and the rules that define what each number truly means.
The result is answers you can trust, sources you can verify, and consistent responses every time the same question is asked. Think of a Thinkbox as a tireless analyst who understands your business data, follows your governed logic, and shows its work for every answer.
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.
Every answer ships with the steps Thinkbox took, the rules it followed, and the reasoning behind it.
verified_revenue metric × region entityAPAC revenue fell 22% due to 3 enterprise churns in Singapore totalling $840k ARR. Renewal risk score for the segment: High. Recommended action: schedule QBRs with the remaining 7 APAC enterprise accounts before Aug 15.
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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