Why use cases, not just a feature list
A feature list tells you an API can extract fields; it rarely tells you whether it fits the specific internal tool you're actually building. This page is written the other way around — eight concrete situations a citizen developer runs into, each mapped to how the extraction API addresses it specifically, so you can find the scenario closest to your own build rather than translating a generic feature list yourself.
Each scenario below is drawn from a recurring pattern across real internal-tool builds, not a hypothetical. The underlying mechanics — one call, typed response, a confidence threshold — are the same across every one, so understanding one scenario well makes the rest easy to apply.
The shape of an internal tool's document problem
Most of these scenarios share an underlying shape: a small team, a document that currently gets typed into a spreadsheet or a form by hand, and a recurring moment where that manual step is the bottleneck in an otherwise simple workflow. The API doesn't change per-document depending on which scenario it's used in — every use case below is the same /extractcall, applied to a different moment in a small team's workflow.
What differs between scenarios is almost entirely how the response gets used afterward — which table it's written to, what conditional runs next — rather than anything about the extraction itself.
1. An expense-approval tool for a small team
A finance-adjacent team member builds a Retool app where anyone can upload a receipt, the app extracts merchant, date and total, and routes the request to the right approver based on amount. This is covered in full, with a worked example, on the document extraction API for internal tools page.
For most teams, this single scenario alone justifies the integration — it's the most common first internal tool a citizen developer builds involving documents, and the one where manual re-typing is the most visible daily friction.
2. A vendor-invoice intake app
A small ops team builds a Bubble app where a vendor invoice, forwarded by email and downloaded manually, gets uploaded and turned into a structured record — vendor, invoice number, amount, due date — feeding a simple internal payables tracker without a full accounting-software integration.
This scenario is often the first place a team notices the difference between a header field and a line-item table — an invoice with several billed items needs the line-item array from the response, not just the total, to be genuinely useful downstream.
3. A receipt log built on Airtable
A team already using Airtable to track expenses adds a script step that reads each newly attached receipt and populates the record's fields automatically, turning an attachment field that used to require manual entry into structured data the moment a file is added.
Because Airtable Automations can trigger on a new attachment, this scenario often runs with no manual action at all beyond uploading the receipt — the extraction and field population both happen automatically as part of the same automation.
4. A customer-facing onboarding form that reads ID documents
A Bubble-built customer onboarding flow asks a new user to upload a document, and the app extracts the relevant fields to pre-fill the rest of the form instead of asking the person to retype information already printed on the document they just uploaded.
This scenario differs from the others in audience — an external user rather than an internal teammate — but the underlying call and response handling are identical, which is why it's included here rather than treated as a separate integration pattern.
5. Migrating a team off a shared spreadsheet
A team currently tracking documents in a shared spreadsheet, with someone manually retyping figures from each PDF, replaces that manual step with an internal tool where the extraction happens automatically on upload — the spreadsheet itself often stays, at least initially, but the manual data entry that fed it disappears.
This scenario is a common trigger for building a first internal tool at all — the team already knows exactly what data they need, because they've been typing it by hand for months or years, which makes scoping step 1 of the build unusually fast.
6. A support tool that reads an attached invoice
A customer-support team building an internal Retool tool adds a step where an agent can drop in an invoice a customer attached to a ticket, instantly seeing the amount and date without opening the PDF manually — small on its own, but a real time saving multiplied across every ticket that involves a billing document.
This scenario tends to spread quickly once one team sees it working — a support tool built for one use case often gets a second and third document type added once colleagues notice how little extra work each addition takes.
7. A prototype that needs to look real before real engineering starts
A product manager building a Retool prototype to pitch a document-handling feature to engineering wants the demo to show real extracted data, not mocked placeholder values, to make the pitch concrete. A single API call gets a working, realistic prototype built in an afternoon rather than waiting for an engineering sprint just to validate the idea.
This is a specific, high-value use of the same mechanics described in the other scenarios — a short-lived prototype rather than a permanent internal tool, but processed through the identical endpoint with the identical response shape.
8. An internal tool that outgrows a single app builder
A tool that started in Airtable grows to the point where a team wants a more polished interface in Retool or Bubble, reading from the same underlying data. Because the extraction API works identically across all three, the migration doesn't require rethinking the document-handling piece at all — only the UI layer changes.
This scenario is a useful reminder that choosing an app builder and choosing a document-extraction approach are genuinely separate decisions — switching one doesn't force revisiting the other.
Picking the scenario closest to your build
| If you're building... | Start with scenario |
|---|---|
| An approval workflow for team spending | 1 — expense approval |
| A tracker for money owed to vendors | 2 — vendor invoice intake |
| An Airtable base you already use | 3 — receipt log on Airtable |
| A user-facing signup or onboarding flow | 4 — customer-facing onboarding |
| Something to replace a shared spreadsheet | 5 — spreadsheet migration |
These eight are the most common patterns, not an exhaustive list — the underlying call is the same for any workflow that starts with a document and needs structured data out, so a build that doesn't map neatly onto one of these can typically still apply the same mechanics.
A useful test if none of the rows above fits cleanly: describe your build in one sentence, replacing the specific document type with the word "document." If that sentence still makes sense — "an app where uploading a document creates a record" — the same pattern almost certainly applies.
The ROI case, worked through
Across every scenario above, the return comes from the same place: time no longer spent retyping a document's fields by hand, redirected to whatever the internal tool was actually built to do. For a small team processing roughly 300 documents a month across a couple of internal tools, the extraction cost runs about €21 a month against hours of manual entry time freed up — time worth considerably more than the extraction cost at any reasonable rate, covered with full numbers on the PDF data API page.
It's worth measuring this against a realistic baseline rather than an idealized one — most teams underestimate how much time manual document entry actually consumes, because it's spread across small, individually unremarkable moments rather than one visible block of hours. Adding up those moments over a month is usually what makes the case for automating them concrete.
This ROI compounds across scenarios rather than being a one-time gain — the same freed time shows up whether the tool is an expense approver, a receipt log, or a customer onboarding form, since all of it flows through the same flat per-page cost rather than a separate line item for each tool.
Measuring success after launch
The simplest useful metric is how often someone still opens the original document manually after using the tool — if that number stays low, the extraction is doing its job. A second, more qualitative signal is whether the review-queue habit from the confidence threshold actually gets used, or whether it's quietly ignored — an ignored review queue is a sign the threshold needs revisiting, not that review isn't needed.
A less obvious but telling signal is whether colleagues start asking for the same tool to handle a second document type unprompted — that request is usually the clearest sign the first version actually solved a real problem, rather than merely working in a technical sense.
A third measure worth a glance after the first month is whether extraction spend tracks roughly linearly with document volume — it should, and a mismatch is usually a sign something unexpected is happening in the pipeline worth a quick look.
The common thread across every scenario
Every scenario on this page is, underneath, the same trade: replacing the mechanical step of typing a document's fields by hand with an API call, while leaving whatever judgment or workflow logic comes after entirely in your app's own hands. None of these use cases involve the API deciding where data belongs, approving anything, or making a judgment call — it turns a document into structured, validated data, and everything downstream of that stays exactly as it was.
This is worth stating plainly because it's easy, reading eight different scenarios, to imagine eight different capabilities. There is really only one capability being applied eight different ways — a distinction that matters when evaluating how much new work each additional scenario actually requires, which is usually very little once the first one is built and understood.
Getting buy-in from a skeptical teammate
The fastest way to convince a skeptical colleague is a live demo with a real document from your own team, not a theoretical pitch — running an actual invoice or receipt through the API and showing the structured result next to the original PDF makes the value concrete in a way a feature description never quite does.
Framing matters too: positioning this as removing a specific, named annoyance — "no more retyping vendor invoices into the tracker" — lands better than a general pitch about "automation," because it's a concrete problem the teammate has actually experienced.
Rough cost per scenario, for planning purposes
Because every scenario bills at the same flat €0.035 per page, estimating cost for any of them is the same simple arithmetic: expected document volume multiplied by the rate. The figures below are illustrative, sized to a small team, to give a sense of scale before running the numbers against your own actual volume.
| Scenario | Illustrative volume | Illustrative cost |
|---|---|---|
| Expense approval (~50 receipts/month) | ~50 pages | ~€1.75/month |
| Vendor invoice intake (~40 invoices/month) | ~80 pages | ~€2.80/month |
| Customer onboarding (~200 signups/month) | ~200 pages | ~€7/month |
| One-off historical backfill (~500 documents) | ~1,000 pages | ~€35 one-time |
Beyond the eight: a ninth pattern worth naming
A pattern that comes up often enough to mention separately: a one-off historical backfill — a team that's about to start using an internal tool for new documents going forward also has a backlog of old ones sitting in a folder, and wants them in the same structured form. The batch mechanics are identical to processing new documents one at a time; the only difference is running through the backlog once rather than as an ongoing trickle.
This is worth calling out specifically because it's easy to assume a backfill needs special handling. It doesn't — it's the same call, just looped over more files in one sitting.
What these use cases don't cover
None of the eight scenarios above involve the API making an approval decision, choosing where data is stored, or replacing a person's judgment about an unusual document — every one of them ends with structured, validated data landing in your app's own logic, not a fully autonomous process. Builders looking for a tool that also makes business decisions on their behalf are looking for something this API is deliberately not.
This boundary is worth being explicit about, because it's the difference between a tool that genuinely speeds up a team's existing process and one that quietly removes a human from a decision they should still be making. Every scenario above keeps that decision squarely with a person; the API's job ends at turning a document into something a person, or your app's own deterministic logic, can act on quickly.
Who these use cases are written for
Citizen developers building their first internal tool
Looking for a concrete scenario that matches their actual problem, not a generic feature list.
Small ops, finance or support teams
Deciding which internal process to automate first and how to pitch it internally.
Product managers prototyping quickly
Wanting a realistic demo before committing engineering time to a feature.
Teams already using more than one app builder
Needing document handling to work the same way across their whole stack.
If none of these four descriptions fits precisely, the underlying question is still the same one worth asking: is there a recurring point in your internal tool where a document arrives and needs to become structured data before anyone can act on it? If so, one of the eight scenarios above is almost certainly a close enough match to start from.
It's also worth reading through even the scenarios that don't immediately match your situation — the mechanics they describe (a confidence threshold, a review state, a specific platform's binding pattern) tend to transfer directly to whatever variant you're actually building, even when the surface-level use case looks different.
Get your API key
Pick the scenario closest to your build and run a small real test against it. A free plan account processes real documents at full accuracy against a smaller monthly allowance — enough to validate any of the eight scenarios above before committing to full usage.
If you're not sure where to start, expense approval is the safest default — it's the most common first internal tool involving documents, and proving it out gives you a working integration you can extend to every other scenario on this page afterward.
Whichever scenario you start with, the same key and the same call carry over to every other one on this page — there's no separate setup to repeat, only a new binding and a new confidence threshold for each new tool you add.