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Extracted Data · Invoice conf 0.99
1
2 ·· "document_type" "invoice"
3 ·· "extracted_at" "2025-09-29T10:00:00Z"
4 ·· "document"
5 ···· "invoice_number" "BORD_282_2025 / 0006806"
6 ···· "original_reference" "SIM_282_2025 / 1083480"
7 ···· "invoice_date" "2025-09-29"
8 ···· "purchase_date" "2025-09-17"
9 ···· "currency" "EUR"
10 ···· "status" "PAGADO"
11 ···· "seller"
12 ······ "name" "IKEA IBÉRICA S.A."
13 ······ "tax_id" "A28812618"
14 ······ "address" "Av. Matapiñonera Nº9, 28703 San Sebastián de los Reyes, Madrid"
15 ····
16 ···· "buyer"
17 ······ "name" "Windel SL"
18 ······ "tax_id" "B58372914"
19 ······ "address" "Av. de la Innovación 18, 41020 Sevilla, ES"
20 ····
21 ···· "line_items"
22 ······
23 ········ "article" "00567856"
24 ········ "description" "FÖRBÄTTRA PNL LAT 62X80 BLANCO MATE"
25 ········ "qty" 2
26 ········ "unit_price" 39
27 ········ "amount" 78
28 ········ "tax_base" 64.46
29 ········ "vat_pct" 21
30 ········ "vat_amount" 13.54
31 ······
32 ······
33 ········ "article" "10273189"
34 ········ "description" "VOXTORP FRNT C 60X10 BLANCO MATE 2UN"
35 ········ "qty" 1
36 ········ "unit_price" 25
37 ········ "amount" 25
38 ········ "tax_base" 20.66
39 ········ "vat_pct" 21
40 ········ "vat_amount" 6.48
41 ······
42 ······
43 ········ "article" "10301960"
44 ········ "description" "UTRUSTA RIEL HORNO GALVANIZADO"
45 ········ "qty" 1
46 ········ "unit_price" 12.5
47 ········ "amount" 12.5
48 ········ "tax_base" 10.33
49 ········ "vat_pct" 21
50 ········ "vat_amount" 2.17
51 ······
52 ······
53 ········ "article" "10417041"
54 ········ "description" "MA ACC EX 30 H"
55 ········ "qty" 1
56 ········ "unit_price" 79
57 ········ "amount" 79
58 ········ "tax_base" 65.29
59 ········ "vat_pct" 21
60 ········ "vat_amount" 13.71
61 ······
62 ······
63 ········ "article" "10418899"
64 ········ "description" "VOXTORP P 30X80 BLANCO MATE"
65 ········ "qty" 1
66 ········ "unit_price" 33
67 ········ "amount" 33
68 ········ "tax_base" 27.27
69 ········ "vat_pct" 21
70 ········ "vat_amount" 5.73
71 ······
72 ····
73 ··
74
1
2 ·· "documentType" "closing_disclosure"
3 ·· "closingDate" "2013-04-15"
4 ·· "settlementAgent" "Epsilon Title Co."
5 ·· "fileNumber" "12-3456"
6 ·· "property"
7 ···· "address" "456 Somewhere Ave, Anytown, ST 12345"
8 ···· "salePrice" 180000
9 ··
10 ·· "borrower"
11 ···· "name" "Michael Jones and Mary Stone"
12 ···· "address" "123 Anywhere Street, Anytown, ST 12345"
13 ··
14 ·· "seller"
15 ···· "name" "Steve Cole and Amy Doe"
16 ···· "address" "321 Somewhere Drive, Anytown, ST 12345"
17 ··
18 ·· "lender" "Ficus Bank"
19 ·· "loanTerms"
20 ···· "amount" 162000
21 ···· "interestRate" "3.875%"
22 ···· "term" "30 years"
23 ···· "monthlyPayment" 761.78
24 ···· "prepaymentPenalty" "Up to $3,240 (first 2 years)"
25 ··
26 ·· "projectedPayments"
27 ····
28 ······ "period" "Years 1-7"
29 ······ "principalInterest" 761.78
30 ······ "mortgageInsurance" 82.35
31 ······ "escrow" 206.13
32 ······ "total" 1050.26
33 ····
34 ····
35 ······ "period" "Years 8-30"
36 ······ "principalInterest" 761.78
37 ······ "mortgageInsurance" 0.00
38 ······ "escrow" 206.13
39 ······ "total" 967.91
40 ····
41 ··
42 ·· "closingCosts" 9712.10
43 ·· "cashToClose" 14147.26
44
1
2 ·· "documentType" "payslip"
3 ·· "periodYear" 2025
4 ·· "issueDate" "2026-05-14"
5 ·· "consultant" "415802"
6 ·· "employer"
7 ···· "mandant" "7184"
8 ···· "name" "Marquardt & Söhne GmbH"
9 ···· "address" "Königsallee 47, 40212 Düsseldorf"
10 ··
11 ·· "employee"
12 ···· "name" "Heinemann, Friedrich-Wilhelm"
13 ···· "persNr" "0327"
14 ···· "vkz" "K2P"
15 ··
16 ·· "totals"
17 ···· "ravRate" 21.87
18 ···· "kpvRate" 20.52
19 ···· "bgrs" 134134
20 ··
21
1
2 ·· "document_type" "invoice"
3 ·· "confidence" 0.98
4 ·· "invoice_id" "1277139"
5 ·· "invoice_date" "2016-02-29"
6 ·· "vendor" "Cox Media West"
7 ·· "vendor_address" "P.O. Box 165355, Atlanta, GA 30348"
8 ·· "client" "Trump Political NCC MW"
9 ·· "client_id" "161827"
10 ·· "contract_id" "1219837"
11 ·· "estimate_id" "504"
12 ·· "po_number" "62246950"
13 ·· "bill_cycle" "02/16"
14 ·· "asst_exec" "NATIONAL_ARKANSAS"
15 ·· "payment_terms" "Net 30"
16 ·· "net_advertising_fee" 1663.00
17 ·· "total" 1663.00
18 ·· "currency" "USD"
19
1
2 ·· "documentType" "invoice"
3 ·· "invoiceNumber" "INV-784232"
4 ·· "orderNumber" "11450414"
5 ·· "invoiceDate" "2016-07-31"
6 ·· "period" "2016-06-27 / 2016-07-31"
7 ·· "billTo"
8 ···· "name" "Creative Communications (EDI)"
9 ···· "attention" "Susan Kines"
10 ···· "address" "P.O. Box 24189, Greenville, SC 29616"
11 ··
12 ·· "remitTo"
13 ···· "name" "Spectrum Reach"
14 ···· "address" "PO Box 952993, St Louis, MO 63195-2993"
15 ···· "phone" "844-634-2216"
16 ··
17 ·· "agency"
18 ···· "name" "Creative Communications (EDI)"
19 ···· "agencyNo" "SCA2000"
20 ···· "aeName" "Blackwell, Donald R"
21 ··
22 ·· "customer"
23 ···· "name" "SC Sen Rep Caucus Dist 2 Larry Martin"
24 ···· "customerNo" "54851"
25 ···· "contractNo" "92756"
26 ··
27 ·· "lineItems"
28 ····
29 ······ "lineNo" 1
30 ······ "date" "2016-06-27"
31 ······ "timePeriod" "UD:05:00-09:00"
32 ······ "network" "CNN"
33 ······ "spotsOrdered" 4
34 ······ "spotsAired" 4
35 ······ "spotRate" 9.00
36 ······ "grossTotal" 36.00
37 ····
38 ····
39 ······ "lineNo" 2
40 ······ "date" "2016-06-27"
41 ······ "timePeriod" "UD:19:00-24:00"
42 ······ "network" "CNN"
43 ······ "spotsOrdered" 3
44 ······ "spotsAired" 3
45 ······ "spotRate" 11.00
46 ······ "grossTotal" 33.00
47 ····
48 ··
49 ·· "totalSpots" "7"
50 ·· "totalAmount" "69.00"
51

Powering document extraction for teams at

Invofox is different

The document infrastructure
that just works.

Other solutions give you a toolbox. Invofox gives you results. The perfect pipelines you'd spend a year building and a team maintaining, ready from day one.

Data extraction has no shortcuts.
You need a pipeline.

Great document processing is not just a feature. It's a complex infrastructure that is ready for edge-cases, scales and learns from feedback.

01 1
Upload a document Send any PDF, image or scanned file
You
02
File intake & integrity Handle corrupt and password protected files
Ingestion
03
Pre-processing Deskew, denoise, and sharpen for clean OCR.
Parsing
04
Dual-pass OCR Two passes: one reads the text, one maps the layout
Parsing
05
Page splitting Separate multi-document files into subdocuments
Parsing
06
Classification Index and categorize each document
Parsing
07
Format conversion Get your documents LLM-ready
Parsing
08
Multi-step extraction AI models identify every relevant value
Extraction
09
Tables & line items Reconstruct tables, reconcile subtotals to totals
Extraction
10
Entity normalization Normalize dates, currencies, numbers and tax codes
Extraction
11
Schema mapping Map raw fields into your exact data model
Extraction
12
Cross-field validation Check amounts and business rules
Extraction
13
Confidence scoring Build field and document level confidence scores
Extraction
14
Provenance Every extracted value linked back to its page, region and source
Extraction
15
Webhook delivery Send final result to your system
Delivery
16
Detect edge cases Flag docs to avoid errors and get feedback
Improve
17
Learn from feedback Improve results with a single API call
Improve
18
Pipeline tuning Continuous iteration on real docs and corrections
Improve
19
Live upgrades Roll out new AI models
Improve
20
Avoid regressions Catch accuracy drops on every change
Improve
21
Scaling & throughput Queues, autoscaling and peak-traffic handling
Infra
22
Monitoring & drift Real-time alerts on latency, accuracy and format drift
Infra
23
Zero-retention Documents deleted after delivery, never stored
Infra
24
Agentic review Re-check and self-correct low confidence fields
Delivery
25
Large-file handling Chunk and process PDFs with hundreds or thousands of pages without timeouts
Infra
26
On-prem & private cloud Deploy fully on-prem when data residency demands it
Infra
27
Certified by default SOC 2, ISO 27001, GDPR and HIPAA. Audited, current and contractually committed
Compliance
28 3
Receive JSON Clean, validated, schema-mapped structured data
You
INVOFOX
Everything between
upload and JSON.
1endpoint
99%+accuracy
Infrastructure High accuracy Edge cases Learn from feedback Compliance Reporting

Ship in an
afternoon.

Integrate one endpoint into your codebase. Get back clean, structured JSON from any document, without building the extraction pipeline, training models, or handling edge cases. Ever.

bash — invofox
$ curl -X POST \
··https://api.invofox.com/v1/extract \
··-H "Authorization: Bearer $KEY" \
··-F "file=@invoice.pdf"
200 OK · 1.2s
{
··"type": "invoice",
··"vendor": "Meridian Ltd",
··"total": 6720.00,
··"confidence": 0.99
}

Battle-tested across continents,
hundreds of teams run on Invofox.

Invofox runs in production today across the US, EU and LATAM — for fintechs, marketplaces, logistics ops, accounting platforms and top enterprises. Here's a snapshot of what flows through every day.

Live
// overview

Production metrics

Today
2,184,392
Documents processed
Across all docs
99.2%
Average accuracy
SLA-bound
End-to-end
<2s
Average response time
p50 0.8s
p95 1.4s
p99 1.9s
Edge cases
0.4%
Auto-flagged for review
NO CONTENT PWD PROTECTED INVALID FORMAT
Recent extractions streaming
PDF
invoice_8237.pdf Invoice
9.2s done
PDF
bundle_482.pdf Multi-doc
Split into 5 11.4s done
PNG
payslip_2104.png Payslip
8.1s done
PDF
statement_5821.pdf Bank statement
11.7s done
JPG
invoice_8238.jpg Invoice
processing
PDF
batch_201.pdf Multi-doc
Split into 3 10.5s done
PNG
receipt_2298.png Receipt
8.3s done
Splitter active
13,482
multi-doc bundles split today
+12% vs yesterday
12
Recent splits
bundle_482.pdf → 5 documents
batch_201.pdf → 3 documents
package_009.pdf → 4 documents
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SLA tier available on plans processing 1M+ documents per year.

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Documents deleted right after delivery. No copies, no backups, no logs.

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Frequently asked questions.

~/invofox / faq.json
accuracy.json
1
2 ··"question" "How accurate is Invofox?"
3
4 ··"answer" "Accuracy thresholds are guaranteed in your SLA, per document type and per field. Every extraction is validated before it counts toward your bill. The feedback loop means accuracy improves over time as your team flags edge cases. Stable use cases reach up to 99%."
5
Accuracy accuracy.json
main 0 errors 0 warnings UTF-8 LF JSON

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