The platform

One control layer between your systems and every AI.

What sits behind the promises: how FrontierOps keeps answers right, runs work in isolation, enforces who can do what, and stays open to whichever AI you choose.

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01 · The right answer

“Same number every time. Revenue means what finance says it means.”

Your definitions, enforced on every query.

  • Define the data the AI may use in a semantic layer: tables, columns, descriptions and relationships, in FrontierOps YAML or the open OSI format.
  • Already have dbt? Import it through the official dbt-to-OSI converter. No dbt? Author and edit layers in the dashboard, with AI help.
  • Every SQL query is parsed before it runs. Tables outside the layer, schema dumps and side-channel functions are refused, and anything ambiguous fails closed. Covers Snowflake, BigQuery, PostgreSQL, Redshift, MySQL and SQL Server.
  • Put several domain layers on one warehouse (sales, finance, operations). Each team and each agent only gets the layers assigned to it.
  • Keep layers in Git: your CI pushes the generated file through the Management API with GitHub OIDC, so there are no long-lived secrets on either side.
02 · The work gets done

“Monday’s report is in your inbox before anyone asks.”

Agents that run on their own, each in a sealed environment.

  • Agents start on a schedule, an inbound email, a Slack or Telegram message, a webhook, or a visitor on your website chat.
  • Every run executes in an isolated microVM that is never shared across agents or organizations and never runs inside the platform. Every tool call goes back through the same permission checks as a person.
  • Skills are reusable playbooks in the open Agent Skills format. Knowledge-only skills cost nothing to run; executable skills run sandboxed, and their contents are hash-verified before any command executes.
  • Mid-run, an agent can stop and ask a person in Slack or Telegram, then continue with the answer.
  • Dashboards built in Claude become Artifacts: live pages rendered for each viewer under their own permissions, or PDFs delivered on a schedule.
  • Pipelines index files and Slack or Telegram history into a vector store, so agents can search your documents and conversations.
03 · You stay in control

“Everyone sees what they should, nothing more. Every question is on record.”

One permission model across every system.

  • Every grant is explicit: a resource, an action, a scope and an effect. Deny always wins over allow.
  • Reading and writing are separate permissions on every tool, and every tool declares which of its actions write. Out of the box: Admin, Editor, User and Webhook roles, plus any custom roles you define.
  • Managing something never lets you use it. Seeing a workspace’s settings does not let you connect to it.
  • Nobody can grant a permission they don’t hold themselves.
  • Agents carry roles like people do, and database writes are opt-in.
  • The audit log records sign-ins with source IP, every tool call, the SQL and the tables it touched, and every denial. A permission simulator shows who can do what before you change anything.
04 · No lock-in

“Works in the AI you use today and whichever one you switch to tomorrow.”

Open standards in. Open standards out.

  • Each team workspace is a standard MCP endpoint: OAuth sign-in for Claude and ChatGPT connectors, API keys for scripts and other clients.
  • Bring your own model keys: Anthropic, OpenAI, Google Gemini, AWS Bedrock, DeepSeek, xAI Grok, or hundreds more through OpenRouter.
  • Put any remote MCP server behind FrontierOps and it inherits the same permissions and audit trail.
  • Open formats throughout: OSI for semantic layers, Agent Skills for playbooks, plain HTML for artifacts.
  • Provision everything from code through the Management API.
Security & operations

Built to be approved by IT.

For engineers

Connecting an AI to a database takes an afternoon. This doesn’t.

Wiring one MCP server to one system is easy. Here is what it takes to let a whole company use AI on its real systems, safely.

  1. 01 One permission model across 50+ systems, down to read vs. write on every tool
  2. 02 A SQL parser per dialect that checks every query against your definitions before it runs
  3. 03 An audit trail of every question, by every person and every agent
  4. 04 Agents on schedules and triggers, every run isolated in a microVM
  5. 05 Reports and live dashboards that render with each viewer’s own permissions
  6. 06 Support for any MCP client and seven model providers, with no rebuild when you switch
  7. 07 Separate workspaces per team, white-label and multi-org built in
Integrations

50+ systems out of the box. Any MCP server on top.

Every connector inherits the same permissions, read vs. write split and audit trail. Need something that isn’t listed? If it has a remote MCP server, it works today.

Data & warehouses

  • Snowflake
  • BigQuery
  • Amazon Redshift
  • PostgreSQL
  • MySQL
  • SQL Server
  • Elasticsearch
  • Redis
  • Qdrant
  • Amazon S3

CRM & sales

  • Salesforce
  • HubSpot
  • Pipedrive
  • Zoho CRM
  • Attio
  • Apollo
  • Clay
  • Seamless.AI
  • Gong

Commerce, billing & marketing

  • Shopify
  • Magento
  • Stripe
  • Chargebee
  • Klaviyo
  • Customer.io

Support, engineering & ops

  • Zendesk
  • PagerDuty
  • Sentry
  • Datadog
  • Amazon CloudWatch
  • GitHub
  • Jira & Confluence

Docs, files & messaging

  • Google Sheets
  • Google Docs
  • Google Slides
  • Google Calendar
  • Notion
  • Zoho WorkDrive
  • Zoho Calendar
  • Slack
  • Telegram
  • Email (IMAP & SMTP)

Industry systems

  • Hostaway
  • Hostfully
  • Fleetio
  • Victron VRM

AI services

  • Web search (Anthropic)
  • Web search (OpenAI)
  • ElevenLabs voice

Everything else

  • Any remote MCP server

See it on a question your team actually asks.

Bring your stack and your hardest permission question. We’ll walk through how FrontierOps handles both.

Book a 30-minute call