OCR on anything
Reads scans, photos, and PDFs: not just clean digital files: including messy real-world documents.
Task tier · Documents
An AI agent that reads your documents so people don't have to.
Invoices, contracts, forms, receipts, it reads them (even scans and PDFs), pulls the fields you need, classifies them, validates against your rules, and writes clean structured data into your systems. The data-entry job nobody wants, gone.
ModelMultimodal Workers AI models in your own Cloudflare account
Cost per taskModel usage billed at cost, measured per task in your pilot
Connects toEmail inboxGoogle DriveSharePointQuickBooksXero
Autonomy
- Suggests
- Acts with approval
- Acts on its own
Extracts and validates on its own. Documents that fail validation wait in a queue for a person to approve. How autonomy works
What does an AI document processing agent do?
A document processing agent turns unstructured documents into structured data. It runs OCR on scans and PDFs, extracts the fields you care about (amounts, dates, parties, line items), classifies each document by type, validates against your rules, and writes the result to your accounting, CRM, or database. Anything low-confidence is flagged for a human, not guessed. Task tier, running on Workers AI in your own Cloudflare account. Highest-volume, lowest-judgement work, ideal for finance ops, legal intake, and back office.
theagency47 · Updated June 2026 The spec sheet
How the Document Processing Agent actually works.
| Job-to-be-done | Convert incoming documents into validated, structured records in your systems |
| Tier | Task (bounded, high volume) |
| Underlying model | Multimodal Workers AI models in your own Cloudflare account for vision + document understanding |
| Trigger | New document (email attachment, upload folder, scan, integration) |
| Inputs | Document, field schema per type, validation rules, target system mapping |
| Tools available | OCR, extraction, classification, validation, write to accounting/CRM/DB |
| Autonomous decisions | Document type, field extraction, validation pass/fail, routing |
| Escalation rules | Low extraction confidence → human review queue · Failed validation → flag, don't post · Unknown doc type → quarantine + notify |
| KPIs measured | Documents processed, straight-through rate, extraction accuracy, exceptions, time saved |
| Eval suite | 22 test cases (field accuracy, low-confidence flagging, validation correctness). |
What it does
The work it takes off your team.
Field extraction
Pulls exactly the fields you define per document type: amounts, dates, parties, line items, references.
Classification
Sorts incoming documents by type and routes each to the right workflow automatically.
Validation
Checks extracted data against your rules (totals, formats, required fields) before anything posts.
Exception handling
Sends low-confidence or failing items to a human review queue instead of posting bad data.
System write-back
Writes validated records straight into accounting, CRM, or your database: no re-keying.
What it looks like in practice
A typical run.
- Continuously, New documents arrive by email, upload, or scan; each is read, extracted, classified, and validated.
- Straight-through, High-confidence, valid documents post directly to the target system.
- Exceptions, Low-confidence or failing items land in a review queue for a quick human check.
- Daily, Digest: volume, straight-through rate, exceptions, accuracy.
FAQ
Questions about the Document Processing Agent.
How accurate is the extraction?
High on well-defined fields, and crucially, it knows when it's unsure. Low-confidence extractions are flagged for human review rather than posted, so the data that goes straight through is trustworthy. Accuracy and flagging are both in the eval suite.
Can it handle messy scans?
Yes, it's built for real-world documents: photos, skewed scans, mixed layouts. Genuinely unreadable items are quarantined and flagged, not guessed.
Where does the data go?
Straight into your accounting system, CRM, or database via the mapping we set up, so there's no re-keying step. Often paired with a workflow automation to trigger the next action. If the target system has no API, an RPA bot can enter the data instead; see how RPA works.
How is it delivered?
As a Spark build configured to your document types, rules, and target systems.
Related
Agents and engagements that pair with this.
Related: For accounting firms →
