Your customers want to ask questions about their data in plain language. AirQuery Embedded gives your product a governed AI analyst, under your brand, in weeks instead of quarters. Each customer only sees their own data, and every answer is reproducible, cited, and the same every time.
Your Chicago → Dallas lane had 42 late deliveries this month, 31% of all late shipments. Most were with one carrier during the week of the 14th.
Many product teams start by wiring an LLM to their database. The demo works. Then it meets real customers, real data, and real security reviews.
A generic LLM guesses at joins and metric definitions. One wrong number in front of a customer and they stop using the feature.
Making sure one customer never sees another customer's rows, on every question, is the problem that blocks launches and fails security reviews.
If the same question returns a different number tomorrow, your support team pays the price.
Prompt tuning, evaluation, model upgrades, and cost control become a permanent second product to maintain.
AirQuery is a fully managed cloud service. Connect it to the warehouse or database behind your product with a read-only connection you control. Queries run where the data lives, and your tables are never copied.
Your team defines tables, relationships, approved metrics, and business rules in a Thinkbox, plus the security rules for which tenant and which user can see which rows and columns. One model serves every customer.
Drop in the white-label assistant, or call the API from your own UI. Your app passes the signed-in customer and user, and AirQuery returns answers scoped to exactly what they're allowed to see.
A ready-made conversational panel you add to any page. Match your colors, name, and tone so it feels native to your product.
Build your own experience. One endpoint takes plain English or SQL and returns the answer, data, SQL, and reasoning trace as structured output.
Already building an AI agent or copilot? Give it AirQuery as a tool over MCP so it can answer data questions accurately instead of guessing.
Security is part of the AirQuery data model, not something you bolt on in prompts. It's enforced on every question, from every channel.
Tenant rules in the Thinkbox scope every query to the customer who is asking. One shared model, with no cross-tenant answers.
Within a tenant, map your product's roles to the rows and columns each user can see, and mask sensitive fields such as pricing or personal data.
Your app signs each session with the customer and user context. AirQuery trusts that context, so you don't need a second user directory.
Already use row-level security in your database, such as Snowflake row access policies, Redshift RLS, or Oracle VPD? AirQuery supports those too.
Every answer records the tenant, user, question, model, security rules applied, and generated SQL, ready for your customers' security reviews.
SOC 2 Type II, encrypted connections, encryption in transit and at rest, and your customers' data is never used to train models.
Ship a production AI feature without hiring an AI team or pausing your roadmap.
Deterministic reasoning over approved metrics. Same question, same answer, with sources your customers can check.
Conversational analytics is a feature customers will pay for. Package it as an add-on or use it to win competitive deals.
Switch or upgrade models without rebuilding. AirQuery keeps up with the AI market so your team doesn't have to.
"Which vendors are we paying late most often, and what's it costing us?"
"Which lanes had the most late deliveries this month, and with which carrier?"
"Where is overtime highest this quarter compared with last year?"
"Which products are selling faster than we can restock?"
"Which clinics have the longest wait times, and is it getting worse?"
"Which campaigns brought in the most pipeline per dollar last quarter?"
Talk to our team about embedding AirQuery in your product, or start free with a 14-day enterprise trial, no credit card required.