Intake & Extraction
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Intake & Extraction
AI Financial Document Agent
Bank statements, receipts and other financial documents classified and extracted into structured, usable data.
Bank statements, receipts and statements of account arrive as PDFs and scans, and someone has to retype the figures before anyone can use them. It is slow, dull and easy to get wrong. This agent sorts each incoming financial document by type, extracts the fields you need into structured data, and queues anything uncertain for a person to check.
- Financial documents sorted by type as they arrive
- Key figures extracted into clean, structured data
- Uncertain fields queued for a person, never guessed
$3,900
20–30 working days
Optional maintenance $249/mo
What it is
AI Financial Document Agent classifies incoming financial documents such as bank statements, receipts and statements of account, then extracts the fields you need into structured data, queuing anything uncertain for review. It works through an email inbox or upload folder. You get clean data instead of PDFs.
Why businesses buy it
Retyping figures from financial documents is one of the least valuable jobs in finance, and one of the most error-prone. Every typo becomes a reconciliation problem later. After this build, statements and receipts are classified and extracted as they arrive, with uncertain values checked by a person, so your team works from clean data instead of PDFs. Reconciliations go faster and errors drop. The Scope Card lists the document types, fields and accuracy testing before you pay.
Problems this solves
Bank statements and receipts pile up as unread PDFs
The agent classifies incoming documents such as bank statements, receipts and statements of account. It extracts the fields you need into structured data that your systems can use.
Someone retypes figures from documents into the ledger
Up to three document types are read, with up to 25 fields each. Extracted data goes to your destination system, so manual keying shrinks to checking the uncertain items.
Extraction errors slip into the books unnoticed
Validation rules check each extracted field. Anything uncertain is queued for a person to review first, so low-confidence data is never passed on without a check.
A good fit if
You handle financial document types beyond supplier invoices, such as bank statements, receipts or statements of account.
Someone on your team currently retypes figures from PDFs or scans into a spreadsheet or system by hand.
You can provide at least 20 real sample documents per document type, up to three types, to train the extraction.
You want low-confidence extractions queued for a person, not silently accepted.
Not the right fit if
Your documents are supplier invoices you want extracted, validated and coded to your chart of accounts. See AI Invoice Intelligence.
You need expense claims checked against policy rather than documents extracted. See AI Expense Audit.
You want ledger and policy questions answered directly, not documents processed. See AI Finance Assistant.
You need more than three document types or more than 25 fields per type. Add a note at checkout for a Change Quote.
How it works
- You send real sample documents. At least 20 examples per document type, up to three types such as bank statements, receipts or statements of account, train what the agent recognizes.
- You define the fields to extract. Up to 25 fields per document type, plus any validation rules, such as a total that must match a sum of lines.
- Documents arrive through one intake route. An email inbox or upload folder you already use, so nobody changes how documents get to you.
- The agent classifies and extracts. Each document is identified by type, then its fields are pulled out and checked against your validation rules.
- Low-confidence items go to a person. Anything the agent is not confident about is queued for the reviewer you named, rather than passed through unchecked.
- You review accuracy before launch. A 100-document test run with an accuracy report lets you confirm the extraction is reliable, with two rounds of changes to tighten it.
What you provide within 72 hours
Your portal lists these the moment you order. Delivery starts the working day after everything is in and your access is checked.
- At least 20 real sample documents per document type, redacted if needed, up to three types.
- The list of fields to extract from each document type and any validation rules.
- Access to the destination system where extracted data should land.
- The person who reviews items the agent is not confident about.
- Admin access and API keys for every connected system, shared by adding our team as a user or creating a key you control, never by email.
- Your business information (company basics, hours and brand voice), shared during intake.
- One decision-maker who can approve the field list and validation rules.
- An API key for your own AI model account (for example OpenAI or Google Gemini), created for this build with a spending limit.
The 72-hour rule. Send everything on your checklist, including access, within 72 hours of ordering so your build starts on time.
Timeline
Start
Intake verified and document types and fields confirmed.
Weeks 1–2
Extraction built and trained on your sample documents.
Week 3
Source and destination systems connected, review queue configured.
Weeks 3–4
100 sample documents processed, with an accuracy report sent to you.
Week 4
Your review and two rounds of changes to fields and validation rules.
Go-live
Live, recorded handoff session held, and 30 days of support begins.
| Option | Delivery time | Price |
|---|---|---|
| Standard | 20–30 working days | $3,900 |
| Express | 15–25 working days | $4,600 |
| Rush | 10–20 working days | $5,250 |
What you receive at handover
- AI Financial Document Agent running on one intake route (email inbox or upload folder)
- Classification and extraction for up to three document types, up to 25 fields each
- Validation rules and a human review queue
- Connection to your agreed source and destination systems
- Test report with an accuracy report from 100 sample documents
- Admin guide, recorded handoff session and 30 days of post-launch support
Expected benefits and how to measure them
- Can cut the time spent typing figures from documents
- Helps keep uncertain data out of the books until a person checks it
- Makes financial documents searchable as data rather than files
- Documents processed without manual keying
- Compare documents keyed by hand in the month before launch with the review queue volume after launch.
- Corrections after posting
- Count entries corrected because of data-entry errors in the month before and the months after launch.
- Review queue size
- Check the share of documents sent to review each week, and watch it as rules are tuned.
Before you start: Count how many financial documents you handle a month and the hours spent keying them. Note recent data-entry corrections.
These are things to track, not promised results. Outcomes depend on your data, your team and your market.
Running costs
You pay for the build once. Running it afterwards costs roughly $30 to $200 a month depending on page volume, covering OCR and AI model usage plus storage, billed directly in your own accounts. Build and test usage follows the same rule, so every provider relationship stays yours.
Ask about running costsCustom requirements
If you need a fourth document type or more than 25 fields per type, add a note at checkout. You get a written Change Quote before any extra work starts. Larger or bespoke projects go through the Custom AI request form.
Request a custom versionKeep it running
Monthly maintenance covers monitoring, fixes and change hours each month, enough to adjust a field list or tighten a validation rule as document layouts shift. It starts after the 30 days of included support.
$249/mo (the minimum fee, includes 2 change hours)
Optional. Starts after the post-launch support period. Cancel any month.
How maintenance worksHow this compares
| Do it yourself with a SaaS tool | Hire a custom agency | Upmark Labs | |
|---|---|---|---|
| Price | A document extraction platform charges a monthly fee per page or per user | A custom extraction build is quoted after discovery calls | Ours is one fixed price, shown before you buy |
| Scope | A generic platform needs you to configure templates for every document layout | An agency agrees scope as the project develops | Ours is trained on your own sample documents, on the Scope Card |
| Delivery | Self-serve extraction tools still take weeks of template setup | An agency project usually runs several weeks, set per project | Ours has a fixed delivery time shown before you buy, with faster options |
| Uncertain items | Many tools pass low-confidence extractions through unless you configure a check | An agency build depends on what was specified | Ours queues anything uncertain for your named reviewer by default |
Questions about this product
What document types can AI Financial Document Agent handle?
Up to three document types per build, commonly bank statements, receipts, or statements of account, each with up to 25 fields extracted. You provide at least 20 real sample documents per type so the extraction is trained on your actual layouts, not a generic template. Add a note at checkout if you need a fourth type.
How is this different from AI Invoice Intelligence?
AI Invoice Intelligence is built for supplier invoices only, with line checks and coding to your chart of accounts. AI Financial Document Agent covers other financial documents, such as bank statements, receipts and statements of account. If invoices are your main volume, start with AI Invoice Intelligence. Choose this agent when the documents vary in kind.
What happens to documents the agent is not confident about?
They are queued for the reviewer you named during intake rather than passed through automatically. This keeps a person in the loop on anything uncertain, which matters most for financial documents where an extraction error could misstate a figure. The queue is part of the standard scope, not an add-on.
How much does AI Financial Document Agent cost to run each month?
Roughly $30 to $200 a month depending on page volume, covering OCR and AI model usage plus storage, paid directly in your own accounts. Higher document volumes sit toward the top of that range. There is no separate ongoing platform fee from Upmark Labs beyond the one-time build price.
How accurate is the extraction before we go live?
We run 100 of your sample documents through the agent before launch and send you an accuracy report broken down by field, so you see real performance rather than a marketing claim. Two rounds of changes let you tighten fields or validation rules if the report shows a weak spot.
Is our financial document data handled securely?
Yes. Access credentials for connected systems are shared by adding our team as a user or creating a key you control, never sent by email, and sample documents can be redacted before you send them if they contain sensitive figures. Extracted data goes only to the destination system you named. PII redaction is available as an add-on for extra sensitive fields.
Can we add a fourth document type later?
Yes, as a Change Quote. Add a note at checkout or reach out after launch, and you get a written quote before the work starts. Many businesses begin with the three most common document types and add a fourth once volume justifies it.
Can we cancel AI Financial Document Agent maintenance after launch?
Yes, plans bill monthly with no lock-in and can be cancelled for any coming month. The 30 days of support included with the build continue regardless of whether you take a plan. If you cancel, extraction keeps running on its existing configuration until you change something yourself.
Stop retyping figures from PDFs by hand.
Order AI Financial Document Agent and your financial documents are classified and extracted into clean data, on a delivery date fixed before you pay.
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