Enterprise AI Across Your Databases for Finance

Enterprise AI across your databases · Finance & FP&A

From reports to ongoing analysis and follow-ups answered immediately with AI

Trusted answers across your data with explainable, reusable reasoning

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An FP&A example: ask, get the answer, dig deeper.

01

Ask in plain language

No dashboard, no semantic model, no engineering ticket. Type the question the way you’d say it — across as many databases as the answer needs: the warehouse, the ERP general ledger, the billing system.

02

Get the answer — with the why

LangGrant answers from your live data and shows how it got there: the sources, the steps, and the numbers behind each one. No black box — and nothing you can’t defend in a review.

03

Dig deeper, instantly

“Which product?” “Just the EMEA region?” “Versus last year?” Each follow-up builds on the last answer — you keep the thread, and you pay only for the incremental change.

04

Keep the plan — run it next quarter

The first answer is saved as a Reasoning Plan: the databases it spans, the approved definition of “net revenue”, the steps, the access role, the result. Re-run it on demand at the monthly close — so you never rebuild the same question.

Snowflake · SQL Server · Oracle · PostgreSQL · BigQuery · Redshift · Azure SQL · Amazon Aurora & RDS · Databricks · Hive · MySql · Salesforce

Explainable trust

Trust the number, because you can see the work.

Finance answers get audited. A Reasoning Plan is the audit trail, written as you go.

See how it was reached

Every step is laid out in plain terms, with the figures behind it — the proof, not just the conclusion.

The approved numbers

Plans use the metric definitions your organization has agreed on, so “net revenue” means the same thing every time.

The same answer, on demand

Re-run a plan and get a current, consistent answer — useful for the monthly close, the board deck, or a quick gut-check.