Farseer AI is a built-in assistant that answers questions about your model in natural language. Ask it for a summary of a variable, an explanation of how a number was calculated, the definition of a dimension, or anything else about the structure or contents of your workspace — and it produces an answer grounded in your real data.

Under the hood, the assistant runs Python code in an isolated sandbox using the Farseer SDK. It can fetch entities, evaluate formulas, search dimension members, and inspect uploaded files — all scoped to your workspace.

Click the Farseer AI entry in the side navigation. The chat opens in one of two layouts:

Use the layout toggle in the chat header to switch between them. Your choice is remembered across sessions.

Each topic you work on can live in its own chat. From the chat header you can:

When you send your first message in a workspace with no existing chats, a new one is created automatically. Its title is generated for you a few seconds after the first reply lands — based on what you asked.

The assistant gives sharper answers when you're concrete about what you're looking for. The more clearly you scope year, version, entity, and metric, the more directly you can act on the result.

Worth specifying whenever it's relevant:

If you leave something out, the assistant uses sensible defaults (Actual version, current year) and tells you which assumptions it applied — confirm or correct in the next message.

When a chat is empty, Farseer AI shows a set of starter prompts to help you get going. They cover common entry points like:

Click any prompt to use it as a starting point, or type your own question.

Five patterns that cover most of what you'll do day-to-day, from quick checks to executive summaries. The examples below use a sample group with four product lines — Aurora, Helios, Vertex, and Nimbus.

Prompt: "What's the Gross Margin for Aurora in February 2025 in Actual? Include GM%."

What you get: The absolute Gross Margin for the period in your model's reporting currency, GM% as a percentage, and a note confirming the version and period that were used. Best for quickly verifying one specific number.

Prompt: "Show revenue by sales channel for Nimbus for 2025 in Actual. Sort largest to smallest."

What you get: A table of revenue per channel (Direct, Wholesale, Online, Distribution, …), sorted descending, with a total row. Surfaces concentration and distribution across channels.

Prompt: "Compare Revenue, Gross Margin %, and OpEx ratio for all four product lines (Aurora, Helios, Vertex, Nimbus) for 2025 in Actual. Which line has the smallest gap between Actual and Plan?"

What you get: A comparison table with the three KPIs per product line in Actual, alongside Plan, with absolute and percentage variance per line — plus a comment identifying the smallest and largest gap to plan.

Prompt: "What are the main drivers of the Gross Margin variance between Actual and Plan for the whole group YTD through Q1 2026? Break it down by Revenue, COGS, and Direct OpEx, and call out which product line contributes most to the variance."

What you get: A waterfall-style breakdown of the GM variance — Actual vs Plan — split by line item (Revenue, COGS, Direct OpEx). For each line: absolute variance and share of total variance. Identifies the entity that contributes the largest deviation from plan.

Prompt: "Write an executive summary of group performance for H1 2026. Compare Actual vs Plan at the Revenue, Gross Margin, and EBITDA level — show absolute and percentage variance. Identify 2–3 key drivers, call out which entity or channel contributes most to the variance, and end with a short management comment with one concrete recommendation."

What you get: A structured summary in several parts:

The assistant has access to a single sandboxed Python tool with the Farseer SDK preinstalled. Through this tool it can:

The assistant can find one of your apps (the same automation scripts you'd otherwise launch from the Apps screen) and run it on your behalf. A typical exchange looks like this:

Apps run with your permissions — the assistant can't reach an app you couldn't run yourself. Because apps can write to the model (imports, transformations, etc.), this is the one capability where the assistant goes beyond read-only.

→ Learn more about Apps

Replies stream back as the assistant works. While a request is in flight, the chat shows an agent-emitted intent line describing what the model is doing right now — something concrete like "Calculating Q1–Q4 channel mix by margin" or "Fetching brand-level margin for 2025". The intent line updates as the agent moves through the steps of its plan, so you can tell at a glance whether it's doing a quick metadata lookup or a longer analytical pass.

Only one request is processed per user at a time. If you try to send a new message while one is still running, you'll see a "previous message is processing" notice. You can cancel an in-flight request from the chat header.

Beyond plain text, tables, and code blocks, replies can include callouts, collapsible sections, and inline coloring drawn from the Farseer theme — useful for highlighting variances, structuring long answers, or hiding optional detail behind a click. The agent decides whether to use these elements based on what fits the question; most replies stay plain prose.

Each assistant reply has a small stats row underneath it with three numbers:

Use these stats as a sanity check on how much work went into an answer — a one-line lookup with one action and 200 ms is fundamentally different from a multi-step analysis that ran for 30 s and did a dozen actions.

Every assistant reply has 👍 and 👎 buttons next to the stats row. Click 👎 to open a comment popover where you can explain what went wrong — the comment is sent alongside the thumbs-down signal so the team can spot patterns in what's failing. Use Clear feedback to undo a rating.

Yes. The assistant understands and responds in any language your team uses, and answers in the same language the question was asked in.

Yes — within the same chat, the assistant retains context. After an executive summary you can ask "break down the GM variance for Aurora by month" without re-stating the year, version, or entity.

Correct the assumption in your next message: "I didn't want YTD, just June." or "Use Plan instead of Actual." The assistant recalculates with the corrected scope.

If the data isn't in the model (e.g., a period that hasn't been imported yet), the assistant says so explicitly rather than inventing a number. Check whether the import or data load for that period has run.

The assistant can drill down to whatever level of detail your model supports — including individual dimension members, custom hierarchies, and line items at the lowest level of your P&L. For granular slices, name the entity or member explicitly.

Chat activity is recorded in the workspace's audit log. Relevant audit types: CHAT.CREATE, CHAT.RENAME, CHAT.DELETE, CHAT.ASK. Admins can review who used the assistant and when from the Audit Logs page.

→ Learn more about Audit Logs