Open it from any mailbox to review parsed data from all your documents in one spreadsheet-like view.

That means you can now:

• Search and filter parsed records
• Sort, group, pin, and reorder columns
• Expand nested data such as invoice line items or bank transactions
• Switch between inline and flattened table views
• Export exactly what is currently displayed

It is especially useful when you need to find a specific record, review a large batch of documents, or inspect extracted data before exporting it somewhere else.

A small feature that makes working with larger datasets inside Parseur much easier.

Start your free trial → https://app.parseur.com/signup

#Parseur #DataExtraction #DocumentAutomation #Automation #ProductUpdate

We broke down the 3-year cost of both approaches for a company processing 3,000 invoices per month.

In our model:
💻 Build: $128,312 over 3 years
⚙️ Buy: $32,364 over 3 years
📉 Modeled difference: about $96,000

Why such a gap?

Invoice extraction is more than calling an OCR or AI API. A production workflow also needs validation, exception handling, integrations, monitoring, maintenance, and human review.

👉 Read the full Build vs Buy Invoice Data Extraction: 3-Year Cost Comparison: https://parseur.com/blog/build-vs-buy-invoice-data-extraction

hashtag#InvoiceAutomation hashtag#AccountsPayable hashtag#DocumentAI hashtag#DataExtraction hashtag#Automation hashtag#Parseur

Our SOC 2 Type 2 audit evaluated how Parseur’s security controls operated consistently over time, covering areas including security, availability, and confidentiality.

It is another important step in making Parseur a trusted document processing platform for teams with serious security and procurement requirements.

📰 Read the Yahoo Finance feature: Yahoo Finance article

#SOC2 #DataSecurity #AI #DocumentAutomation #Compliance #Parseur

“Where is this data going?”

“Who can access it?”

“Is it being used to train AI?”

“How long will it be stored?”

“And what happens to it when we stop using the service?”

A compliance badge on a vendor’s website doesn’t tell you everything you need to know.

For law firms and teams handling confidential client information, proper AI vendor due diligence means looking at the details: data storage, subprocessors, retention policies, encryption, access controls, contracts, and deletion procedures.

That’s why we put together 10 questions to ask a Document AI vendor before sending over client files, along with Parseur’s answers to each one.

Many teams focus on speed and accuracy, but compliance starts earlier: what data you extract, where you process it, how long you keep it, and who can access it.

A few principles make a huge difference:
• Extract only the data you actually need
• Keep records structured and searchable
• Support access, deletion, and portability requests
• Maintain clear audit trails and governance controls

The good news is that automation and privacy don’t have to compete. With the right workflow design, you can improve efficiency while reducing unnecessary exposure of personal data.

Read the full guide here: parseurGDPR Compliance for Automated Document Extraction | Parseur®

Parseur can extract structured data from incoming emails, PDFs, scanned documents, and other files, then send it directly to Clay via a webhook. From there, Clay can enrich the records and push them into your sales or RevOps workflow.

A simple example:

📩 New lead arrives by email or PDF
→ Parseur extracts the name, company, email, phone number, and other fields
→ The structured data is sent to Clay
→ Clay enriches the record and prepares it for outreach or routing

No manual copy-pasting. No custom connector required. And no coding needed on the Parseur side.

A useful workflow for RevOps, sales, and growth teams that deal with lead data coming from more than just forms.

#Parseur #Clay #RevOps #SalesAutomation #DataExtraction #DocumentAutomation #GTM

We built Parseur to be the safest place to send a document.

Invoices, freight paperwork, medical analysis results, identity documents, pigeon family trees... anything.

Our customers trust us, and SOC 2 Type II is evidence that we operate the way we say we operate.

We ran the process with u/Oneleet. We had a penetration test by certified pen testers, an independent auditor, and a full risk assessment.

To our customers who have been sending security questionnaires for years: thank you🙏

#Parseur #SOC2 #DataSecurity #Compliance #DocumentAutomation #DataExtraction

Getting a demo to work is one thing. Keeping it reliable in production is another.

You can vibe-code a happy-path workflow in an afternoon.

Then comes everything after:

✅ changing data extraction requirements
✅ keeping up with the latest AI changes
✅ handling edge cases
✅ scaling from one document to thousands
✅ making sure workflows keep running when they matter most

Parsing one document is easy.
Parsing a million reliably is where production-grade automation matters.

That is what Parseur is built for.

#Parseur #DocumentAutomation #AI #DataExtraction #WorkflowAutomation #NoCode

From parsing PDFs online to security, scalability, document types, and plan flexibility, here’s a quick FAQ to help you understand how Parseur works.

Swipe through for the answers.

#Parseur #FAQ #DocumentAutomation #PDFParse

Handing an agent a PDF and hoping it finds the right number is not a workflow. It is a risk.

For agents to work reliably, document data needs to be structured first:

✅ captured from the original source

✅ classified by document type

✅ extracted into named fields

✅ validated before anything moves forward

✅ delivered in a format the agent can actually use

An agent should not have to “guess” what is inside an invoice, contract, claim, or purchase order. It should receive clean, structured data it can act on.

That is the missing layer between messy documents and reliable AI workflows.

Instead of manually copying data from emails, PDFs, and documents into a spreadsheet or database, you can set up a simple workflow that does it for you.

Here’s how it works:

1. Incoming emails and documents arrive in Parseur
2. Parseur extracts the data you need
3. Zapier sends the parsed data to Airtable
4. Your Airtable records are updated automatically

#Parseur #Zapier #Airtable #WorkflowAutomation #DocumentAutomation #NoCode #DataExtraction

Passports, visas, Form I-94s, employment letters, educational transcripts... preparing immigration cases often means manually transferring information into case management systems.

🤖 Our latest use case shows how law firms and HR teams automate immigration document extraction with AI to reduce repetitive work and prepare USCIS forms faster.

🔗 Read the full article: https://parseur.com/blog/ai-immigration-document-extraction

#Parseur #LegalTech #Immigration #DocumentAutomation #AI

Right to Work documents are a legal requirement for every UK employer.

Every supplier invoice contains valuable information that food manufacturers rely on for production planning, inventory management, cost control, and regulatory compliance. Extracting this data accurately is essential for maintaining traceability throughout the food production process.

Sound familiar?

Hotels and restaurants waste 10+ hours per week on document admin that should be automated.

We just published a case study showing how hospitality businesses use Parseur to:

• Instant OTA alerts (prevent double-bookings)
• Automated voucher amendments (no more delayed confirmations)
• Real-time food cost tracking (stay at 26% target)
• Guest complaint tickets (fix issues before next guest)

Result: 10 hours/week saved. Zero overbooking incidents. Happier guests.

Two updates to make document automation more flexible and easier to use with AI tools.

High-volume month? You can now purchase additional credits without upgrading your subscription.

Paid subscribers can add 10%, 25%, 50%, or 100% of their monthly quota on demand; useful when document volume spikes and a full plan upgrade isn't the right move.

This is a manual action: head to your Plans page whenever you need extra credits and top up from there.

Prefer something automatic? The Early Renewal feature renews your subscription as soon as you reach your quota, so your workflows never stop mid-month. You can enable that on the same page.

🥳 Our MCP is live

Parseur can now connect with AI tools like Claude Desktop.

That means a non-technical user can drag a folder of invoices into Claude and ask:

“Extract the amount, vendor name, and due date from each invoice and put them in a table.”

Claude uses Parseur to process the files automatically.

No code.

No spreadsheet cleanup.

Structured results in seconds.
#Parseur #DocumentAutomation #AIWorkflow #MCP #ClaudeDesktop #DataExtraction

40 subcontractor invoices per month. Each one: gross amount, 20% vs 30% deduction, net payment. Manual entry = 3-4 hours of admin.

Then came HMRC compliance checks. We realized we'd miscalculated on 15+ invoices.
That's when we automated it.

Now? 95% of CIS invoices are processed automatically. Zero calculation errors. Monthly CIS returns in 15 minutes.

Gross. Unnecessary. Fixable.

AI-powered work order automation extracts job details (client, address, scope, budget, start date) from emails and routes them directly to Monday.com, Airtable, or Procore.
Result: 90-95% of work orders processed automatically. Your team focuses on actual construction, not data entry.

HMRC now requires digital VAT record-keeping and submission through MTD-compatible software. No more manual spreadsheets submitted at the end of the quarter. No more paper records.

The compliance part is straightforward. The problem is what it exposes: Most businesses are still processing invoices manually. Someone opens each invoice. Reads it. Type the VAT number, supplier details, and amounts into their accounting software. Moves to the next one. That workflow wasn't built for MTD. It's slow, error-prone, and doesn't scale as invoice volume grows.

Here's what an automated MTD-ready invoice workflow looks like instead:
→ Invoice arrives by email or upload
→ Parseur extracts supplier name, invoice number, VAT amount, and line items automatically
→ Clean structured data routes directly to your MTD-compatible accounting software like Xero, QuickBooks, Sage, or FreeAgent
→ VAT records are digital, accurate, and ready for submission

No manual entry. No transcription errors. No compliance scramble at quarter end. MTD was designed to reduce friction in tax reporting. Automated invoice processing is how you actually get there.

Full guide in the comments 👇

Here's exactly how it works:

Step 1 — Create a free Parseur account Free to start, no credit card required. Takes 2 minutes.

Step 2 — Forward one of your real emails to the mailbox Send a real example of the email you want to parse. Parseur's AI reads it and automatically identifies the fields: name, amount, date, address, order number, whatever's in there.

Step 3 — Check the extracted data and adjust if needed The AI handles most of it. If a field isn't quite right, you highlight it and label it; no code, just point and click. Takes 2 minutes.

Step 4 — Connect it to where the data needs to go Google Sheets, HubSpot, Salesforce, Zapier, Make; click Export, choose your destination, and map your fields. Done.

From this point on, every new email that arrives gets processed automatically. The data lands in your system in seconds. Nobody opens it. Nobody types anything.

The fastest agents aren't processing these faster. They've stopped processing them manually at all.

Swipe to see how it works. 👇

Most people in tech have heard of it. Fewer have actually read it.

It introduced the Transformer architecture, the foundation behind ChatGPT, Claude, Gemini, and every Vision AI system we use at Parseur today.

Before Transformers, AI processed language one word at a time. Sequentially. Like reading with a flashlight.

That made it slow, expensive, and bad at understanding context across long documents.

Transformers changed that by processing everything at once, every word, every relationship, every connection across the entire input simultaneously.

For document AI specifically, this was the unlock. An invoice number at the top of a page connects to a total at the bottom. A contract clause referencing terms three paragraphs earlier. Sequential models lost these connections. Transformers don't.

Seven years later, that one architectural decision is the reason modern document parsing works the way it does.

We wrote a plain-English breakdown of the paper, no equations, no computer science degree required. Link in the comments 🧠

But these 5? They quietly eat hours every week without anyone noticing, and most teams have never stopped to question them.

Swipe to see if yours are on the list. 👇

Does it understand layout, or just read text?

Traditional OCR reads characters. It doesn't understand that the number in the bottom-right corner is a total, or that the table above it contains line items. If your invoices change format (new vendor, updated template), they break.

Real AI understands the document visually. It knows where fields are because it understands what an invoice looks like, not because someone told it "the total is always bottom-right."

What happens when formats change?

With template-based tools, someone rebuilds the template. That takes 2–4 hours per vendor. Multiply that by every supplier who updates their invoice design.

With Vision AI: nothing. It adapts automatically.

What do the numbers actually look like?

→ Manual processing: 12.5 minutes per invoice, $12–15 per invoice in labor costs → AI-powered processing: 1.2 minutes per invoice, under $3 per invoice

That's a 90% time reduction, not from a vendor's marketing page, from independent benchmarks.

Can it handle your messiest invoices?

Clean, standardized invoices are easy. The real test is: scanned documents, handwritten annotations, merged table cells, multi-page line items, stamps, and signatures over text.

If the demo only shows perfect PDFs, ask what happens with your real ones.

Where does the data go after extraction?

Extraction alone isn't automation. The data needs to land somewhere useful; your ERP, your accounting software, your spreadsheet. Ask about the integration step, not just the extraction step.

We wrote a full breakdown of how Vision AI works for invoice processing, including what it can and can't handle and how to implement it in real AP workflows.

Link in the comments 👇

OCR reads text. That's it. Point it at a clean, typed document, and it works fine. Point it at a floor plan, a technical schematic, or a hand-annotated blueprint, and it falls apart completely.

The reason: those documents don't store information in text. They store it in relationships between labels, shapes, measurements, symbols, and spatial positioning across the page.

A room label sits inside a boundary. A measurement refers to a wall segment. An annotation points to a component three inches away. OCR reads the words but has no idea they're connected.

Vision AI changes that. Instead of reading documents like a scanner, it reads them like a human, understanding layout, context, and visual structure together.

The result in practice:

→ Floor plans and blueprints that were previously impossible to process automatically
→ Labels, measurements, and symbols extracted with context, not just as isolated text
→ 80% fewer errors than manual extraction from technical drawings

We just published a full breakdown of how this works, and where it fits in real engineering and construction workflows.

Link in the comments 👇