The AI operator
for commerce

It was built for the executive team and tested by the executive team: the CEO who wants one number, the CFO who has to stand behind it, the CMO defending spend, the COO chasing a stockout. When they are all working from the same definitions, the meeting stops being about whose number is right.

It is just as much for the people doing the work. The analyst who rebuilds the same pull every Monday, the media buyer checking yesterday’s creative, the ops lead watching stock. They ask in plain language and get an answer they can act on without opening a ticket.

You can, and for one person asking one question it works. It stops working the moment the company depends on the answer. With nothing modeled in between, the assistant reads each tool’s API and reinvents what your metrics mean every time it is asked, so two people ask the same question and get two numbers.

The warehouse, and the semantic layer on top of it, is where that stops. Blended CAC, contribution margin and LTV are defined once, over your own data, and every answer traces back to those definitions.

It also decides where your knowledge ends up. Every “this looks off”, every clarification of a metric, is a correction someone at your company made. Captured in your own layer, it holds for everyone from then on. Left in a chat with a vendor, it teaches the vendor about your business, and tomorrow you start again.

Connecting a general assistant to your data gives it access, not understanding. It still guesses what your metrics mean, it forgets your business rules between conversations, and it only works when someone opens a chat.

Polar Operator arrives with your semantic layer, your integrations and your business context already wired in. It keeps what it learns, and it runs recurring work without being asked.

The MCP is the raw connector: an endpoint any AI tool can call. You bring your own model, tokens, and setup. Polar Operator is the managed operator on top of it: configured, secured, tuned for commerce, and included in your Polar bill.

You can. Running it yourself means hosting it, wiring every integration, writing the skills that query your data correctly, and watching consumption forever. Polar Operator arrives already inside your warehouse and semantic layer, managed by the team that built both. It is more opinionated and less flexible on purpose. The trade is better results.

Viktor users reported having to “watch their credits and occasionally double-check it for accuracy since it does occasionally make AI-type mistakes”. Polar Operator includes a large usage allowance estimated based on your company size. Polar Operator doesn’t hallucinate because it lives inside your warehouse and your definitions, so it starts with your whole business and never has to guess what a metric means. Then it can act, with your permission.

Multiple Triple Whale customers complained about Moby’s lack of accuracy. Unlike Moby, Polar Operator doesn’t run Text-to-SQL, but it calls a Semantic Layer, which makes it more accurate. Polar Operator works on your own warehouse, across every source you connect. Moby also supports a narrowed list of actions across these areas: Facebook Ads, Google Ads, AppLovin, Shopify, Audiences (CDP), Klaviyo, and social comments on Meta ads. Polar Operator can work in Meta, Google Ads, Amazon Ads, Klaviyo, Google Sheets, etc. It can also learn any tool, such as Figma, Asana, etc.

Whatever recurring analytical work you would hand to a sharp junior operator. Live examples: a daily Amazon Search audit ranked by dollar impact with budget moves held for confirmation, matching active campaigns against live inventory to catch spend on sold-out products, drafting on-brand Klaviyo campaigns from segment data, filling a daily pacing sheet, a weekly business digest, and reading a shipment inspection against prior lots.

Every number comes from your semantic layer, so the definition behind it is the one your team approved, and Polar Operator shows the path it took to get there. If something looks off, you are checking a definition rather than reverse-engineering a prompt.

When a definition is wrong, it gets corrected once, in the layer. Everyone who asks after that gets the corrected number, and so does the agent.

Minutes. In your Polar settings, click Connect Slack. The agent spins up in 5 to 10 minutes and joins a channel you share with your Polar solutions engineer. From there, you brief it like a new hire: pull last week’s numbers, flag what moved, propose three things to focus on. Most teams have their first standing custom workflow automated within the first week.

Slack today. Through our partnership with Microsoft, Polar MCP is available on every Microsoft product (Copilot, Excel, etc.). Tell us in the demo to be a design partner for Polar Operator in Teams.

They stay, and most teams keep a small number of them. What changes is where the questions go. Dashboards are good at the ten things you already know to watch, and they are the wrong tool for the hundred questions that come up between meetings.

Polar Operator takes those, from the same definitions the dashboards use, so the answer in Slack and the number on the dashboard cannot drift apart.

That is the normal starting point, and it is most of what setup is for. We connect your sources, land your history, then work through the metrics that matter with the people who own them until there is one version of each.

The arguments about which number is right happen once, at the beginning, instead of in every meeting after.

Your data stays in your own dedicated warehouse and is never used to train a model. Access is scoped to the workspace the agent runs in, and any action that changes something outside Polar is held for human confirmation.

More detail on our controls and certifications is on our security page.

No. Polar Operator is part of your Polar plan rather than metered per question, so the cost does not spike with how much your team uses it. Your solutions engineer also sets guardrails on what it is allowed to run on its own.