Your team asks Claude or ChatGPT about your real data and gets the number finance would sign off on. Then the same layer sends the reports and runs the agents that do the work, with your definitions, your permissions and every step on record.
Bring one question your team keeps asking. We'll show you how it gets answered, and what a one-week proof of concept would look like.
Claude 
ChatGPT 
Grok 
Perplexity and any AI that speaks MCP Claude What were gross sales per location last month, vs. last year?
Here's September, using your finance team's definition of gross sales:
| Location | Sep 2026 | vs. 2025 |
|---|---|---|
| Downtown | $2.31M | +12% |
| Westside | $1.87M | +6% |
| Airport Rd | $1.42M | −3% |
| Total | $5.60M | +6% |
It started with
Business questions and automated reports on their dbt project.
Then
They connected it to Claude through FrontierOps.
Now
People across the group ask their own questions, every day.
30
people using it, from a single data project
FrontierOps puts it in order.
Your people & agents
Claude ChatGPT Grok Perplexity 
Slack 
FrontierOps Your definitions
Revenue means what finance says it means
Who can see and do what
Per team, per system, read or write
Every question on record
By every person and every agent
Your systems
Your team keeps working in the AI they already use. FrontierOps sits underneath, so every answer, every task and every action follows your rules.
01 · The right answer
AI pointed at raw tables guesses, and guesses wrong. FrontierOps gives it your definitions first, so it answers the way your finance team would and only uses the data you’ve approved.
How it works: your definitions live in a semantic layer, built from your dbt project or defined with us. Every query is checked against it before it runs.
02 · The work gets done
The Monday report lands in your inbox. New leads get researched overnight. Requests get sorted before the team logs in. Set it up once, and it runs on a schedule or the moment something happens.
How it works: AI agents triggered by a schedule, an email, a Slack or Telegram message, a webhook or your website chat. Reports go out as PDFs or live dashboards.
03 · You stay in control
Sales sees sales. Finance sees finance. The AI can read your CRM without being able to change it. And every question, by every person and every agent, is on record.
How it works: roles and permissions down to read vs. write on each tool, a workspace per team, a full audit log, and every automated run sealed in its own environment.
04 · No lock-in
Works in Claude, ChatGPT, Grok, Perplexity and whatever comes next. Switch models when prices or quality change. Your connections, definitions and permissions stay exactly where they are.
How it works: one open standard (MCP) to every AI client, and your choice of model provider.
Traditional BI takes weeks to turn your data model into one static dashboard. AI-driven BI uses the same model for the dashboard and every question after it: why it moved, which customers, what changed. That’s where most of the value is.
Traditional BI
The question
“What’s our ROI by channel?” It joins the analyst’s queue.
The model
Built for that one question.
The first answer
A static dashboard. That’s where it stops.
The next ten
“Why did paid search drop?” Back to the queue.
Week 4
1 of 11 answered. 10 waiting in the queue.
AI-driven BI
The question
Same question, in plain English.
The model
Built once. AI drafts it, and your team signs off on the definitions.
The first answer
A live dashboard, or a Monday email.
The next ten
“Why did paid search drop?” “Which customers?” Answered from the same model.
Week 4
11 of 11 questions answered
More than 50 systems out of the box, plus any service that already speaks MCP.
Snowflake 
BigQuery 
Redshift 
PostgreSQL 
MySQL 
dbt 
Salesforce 
HubSpot 
Zoho CRM 
Shopify 
Stripe 
Zendesk Slack 
Notion 
Google Sheets 
GitHub 
Telegram 
Sentry Those tools point AI at raw tables and let it guess what the columns mean. FrontierOps gives the AI your definitions first, like what revenue is and how a customer gets attributed to a channel, and checks every query against them. That is the difference between a guess and a number your finance team would sign off on.
Built-in connectors link one AI to one system at a time, each with its own settings, usually through each person's own login. That works for one person. For a company it means no shared definitions, no single place to decide who can do what, and starting over if you change AI. FrontierOps is one control layer across all your systems and all your AI tools.
No. Your analyst stops building dashboards on request and owns the definitions everyone’s answers come from, plus the harder questions nobody had time for.
No. If your data already sits in one database, like Postgres, SQL Server or Snowflake, we start there. If it is scattered across tools, we bring it together first, in your accounts.
Nothing breaks. Your connections, definitions and permissions live in FrontierOps, not in the AI.
You don't need a data team or an AI project. We connect your systems, define your key numbers and launch your first workspace, then hand you a platform your team runs. Everything we set up lives in your accounts.
We connect the systems you run on. Data scattered across tools? We bring it into one place first, in your accounts.
We sit with your team and define the numbers that matter, the way your finance team already uses them.
Your first workspace goes live and your team asks its first questions. Then it is yours to run.
We start with a working proof of concept on your data. Bring one question your team asks every week, and we’ll start there.
