How to use an AI agent for work reports — not describing data in words, but building the report from real files on your computer, locally and without sending numbers to an outside service.

Worth being clear upfront about which task this is. If you need a report for a class or a school assignment, this article isn't about that — any general-purpose chatbot is fine for schoolwork. This is about something else: a recurring work report that gets built from real numbers — sales, expenses, campaign metrics — not made up from a general description.

How this differs from a regular report generator

Most "AI for reports" services work the same way: you describe in words what you need — "put together a Q3 financial report" — and get back text built from your description. That's convenient when there isn't much data and it's easier to summarize than to upload. But if the report needs to be grounded in a real export — a sales table, campaign stats, an expense list — describing it in words takes longer than just handing over the file.

Doka works with real files. You show the agent a spreadsheet or a folder of exports, and it builds the report from what's actually written there, not from your brief summary. The difference shows up on the second report: you don't have to dictate the numbers again each time — you just swap the source file.

What an agent can do with source data

Merge several exports into one report — pull together monthly campaign reports into a single summary with totals, for instance.
Compute metrics by condition — shares, averages, deviations from plan — and explain where each number came from.
Draft the text portion of a report from the numbers in a table: what grew, what dropped, what's worth flagging.
Compare the current period to the previous one and show what changed, if both files are on hand.
Build a report from a recurring template, if the format repeats month to month — describe the structure once, then just swap the source data.

The point isn't that the agent invents the report — it's that it removes the mechanical part: merging, computing, formatting — while the final conclusions and emphasis stay yours.

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Start with one report you've already built by hand. Give the agent the same source file and ask for the same result — that's the easiest way to compare where it saves time, and where you need to adjust the task's phrasing or the source data's structure.

Why this is worth doing locally

Work reports often contain things you don't want handed to an outside service: real sales figures, campaign budgets, customer data. With most online report generators, you either type numbers into a chat by hand, or, less often, upload a file to their server. Doka reads files on your own computer, and if you connect a local model, the data itself never leaves the machine at any step.

How this looks in practice

The mechanics are the same as with any spreadsheet: show the agent a file or a folder of exports and describe the task in words.

The folder has july.xlsx and august.xlsx with spending by ad channel. Build a report: totals per channel for both months, the percentage change, and a paragraph with a conclusion — which channels grew, which dropped. For every number, cite which file and row it came from.

The agent opens the files, reads the data, and computes, showing which files it touched and what it did with them. That traceability isn't a formality — without it, a report becomes text you just have to take on faith; with it, every number can be traced back to its source in seconds.

Being honest about the limits

Clean source data matters more than anything. A tidy spreadsheet with clear headers gets parsed well; a messy export with merged cells and an arbitrary structure, noticeably worse. This isn't a limitation of any particular model — it's true of any automated spreadsheet processing.

Numbers need checking. A model can miscount or misread a condition — especially if the task's phrasing allows for more than one reading ("quarterly spend" — is that a sum or a monthly average?). In financial reporting, that's not a formality, it's a required step: the agent does a draft calculation, the final check on totals stays with you.

Conclusions and emphasis aren't automatic. An agent can honestly compute numbers and write a draft paragraph with an observation, but deciding what matters in a report for a specific manager or client is still a job for a human who knows the context.

Where to start

Take a report you build regularly, and describe its structure and data sources to the agent once, in detail. For the next period, all you'll need is to swap the source file and repeat the request — the payoff shows up on the repeat, not the first pass. Download Doka for free, and for automating similar routine work, see the article on automating routine tasks with AI.

AI for Work Reports Built From Your Own Files, Not From a Description