AI for Document Analysis: Extract, Compare, and Check — Locally
How to use AI for document analysis — extracting data, comparing versions, finding risks in contracts, and pulling together reports. A breakdown by profession, and an honest look at privacy, formats, and limits.
"Analyze this document" sounds like one task, but in practice it's several different ones. Sometimes you need to pull specific figures out of a file. Sometimes you need to compare two versions and understand what changed. Sometimes you need to read forty pages and say where the catch is. AI handles all of this, but the result depends heavily on how you connect it to your documents. Let's break it down — both by task and by profession.
What "document analysis" actually means
It's useful to separate four different actions, because a model has different strengths and weaknesses for each.
The first is extraction: pulling structured data out of a document. Amounts, dates, names, details, terms. A model does this well if the text in the file is actually readable text.
The second is comparison: taking two versions of a contract or report and showing what changed. Here the agent goes through both texts and flags the discrepancies.
The third is summarization: condensing a long document, answering a question about its content. Useful when there's no time to read the whole thing but you need the gist.
The fourth is verification: finding risks, inconsistencies, and missing clauses in the text. This is the most valuable and, at the same time, the most demanding in terms of accuracy — the result absolutely needs double-checking, not taking on faith.
By profession
All of this sounds the same in the abstract, so let's look at real examples.
Lawyer. Working with contracts where every wording matters. The agent goes through the text, pulls out key terms, compares drafts, and flags what changed between versions. When the documents are sensitive, it's critical that all of this can be done locally, without sending the contract outward.
Analyst. The task is turning an export into conclusions quickly. The agent pulls together data from files, computes, drafts a report, and explains how it arrived at the numbers — in checkable steps. Not a "magic answer," but something traceable.
Accountant or manager. Reconciling an estimate against actuals, finding discrepancies between tables, pulling amounts and dates from a stack of acceptance reports into one summary. Routine that takes half a day by hand, one pass for an agent.
Estimator, procurement specialist, buyer. Checking estimate line items against an instruction and a folder of documents, finding a specific record in an archive without remembering its exact name, cross-checking parts of a bid submission for inconsistencies before filing.
Which formats it understands
Worth being precise here, so expectations stay realistic. Doka reads attached PDF, Word, and text files directly, and works with spreadsheets and entire folders through filesystem access in the working folder.
An important caveat: documents where the text is actually text get parsed well. If you have a scan or a photo of a contract, where the pages are essentially images, regular reading won't extract text from them — the document needs OCR first. That's a limitation of the format itself, not of Doka, but worth knowing ahead of time.
A tip before you start: if you have a lot of documents, begin with one and confirm the agent understood its structure correctly. Ask "what kind of document is this, and what sections does it have" — the answer immediately shows whether the file is being read properly.
Why local isn't about paranoia
Documents are the most sensitive material in a job. Contracts, payroll sheets, exports with personal data. And those are exactly the files that are scariest to upload to a cloud service, because you lose control the moment you upload.
Local processing removes that trade-off between convenience and safety. Files get parsed on your own computer, and if you connect a local model, the AI itself runs with no network access either. For trade secrets and personal data, that's not overcaution, it's a normal requirement.
How this happens, step by step
From the outside it looks like a conversation, but underneath the agent works sequentially. It opens the relevant files, reads their content, does what you asked — extracts, compares, computes — and assembles the result. Doka shows the steps it takes along the way, so you can see which files it touched and what it did with them.
That traceability matters more than it seems. You can't work with documents on a "the AI said so, must be true" basis. Being able to check where a conclusion came from turns the model's answer from nice-sounding text into something you can actually rely on.
Local model or cloud
A short rule. For sensitive documents — a local model: data never leaves the machine, even at the cost of slightly less capability. For a one-off heavy task with no confidentiality concerns, you can connect a cloud model over API. Doka lets you keep both and switch per document.
What not to expect
The model makes mistakes, even on short documents. It can miss a clause, misunderstand a tricky wording, or embellish a conclusion. So checking is mandatory, especially where the cost of an error is high — in legal and financial documents. The right way to work: the agent does the heavy draft work, and the final decision stays with a human.
Also worth accounting for very large files. If a document is huge, it might not fit into the model in one pass — in that case the agent works through it in pieces, and it helps to ask specific questions rather than "analyze all of it at once."
Where to start
Take one typical document you work with regularly, and run through it what you'd normally do by hand — extracting data or comparing versions. That's the fastest way to see where the agent saves time and where you need to keep control. Download Doka for free, and similar scenarios by profession are collected in the use cases section.