Case Study · Neoflo · In Production

Better reads.
Lower cost.

Document reading sits at the start of every finance workflow. A wrong field doesn't fail quietly — it becomes manual work that every downstream system inherits. Neoflo built the reading layer once and got it right: 92.5% field accuracy, $0.36 total cost per invoice, and 5–33× cheaper to run than the best commercial alternative.

92.5%
Field accuracy
$0.36
Total cost / invoice
5–33×
Cheaper to run
200
Invoices benchmarked
Product walkthrough
Video walkthrough coming soon
A full product demo and architecture walkthrough will live here
The problem
📄
Wrong reads become payroll

A misread invoice number doesn't just need retyping — it fails the downstream PO match and exits the automated run entirely. At scale, extraction accuracy stops being a technical metric and starts showing up as headcount.

💰
Better vendors charge more per page, not less overall

Commercial OCR platforms benchmark on clean digital PDFs. On real financial documents — scanned delivery notes, handwritten fields, multilingual invoices — accuracy drops sharply. You pay the per-page premium on every document, including the ones they read wrong.

Benchmark — invoice extraction (200 invoices · 15 fields each)

200 invoices — clean digital PDFs, phone scans, handwritten fields, and non-English documents — run through six extraction platforms and scored against a hand-verified answer key.

Neoflo IDP
92.5%
Mindee
86.1%
Rossum
79%
Veryfi
78%
Docsumo
76.9%
Nanonets
72.2%
Coverage: 97.6% — Neoflo attempted extraction on nearly every applicable field, not just the easy ones. Mindee (next closest) attempted fewer fields at a lower success rate.
Benchmark — chart & table extraction (mutual fund factsheets)

A second benchmark on financial documents containing tables and charts — the hardest reading task and the one that triggered the vendor decision. Six systems tested.

Neoflo
99.0%
Claude Opus 4.7
86.1%
Claude Opus 4.8
86.0%
Claude Sonnet 4.6
80.4%
Extend
55.9%
Reducto
5%
This is the test that ended the vendor conversation. Reducto's 5% wasn't a near-miss — it detected zero tables. Extend's 56% was the best any commercial tool offered on financial documents. The gap didn't suggest optimisation; it suggested a different approach.
Consistency under pressure

A system that reads cleanly on a demo PDF but falls apart on a phone-scanned delivery note is an accuracy number, not an accuracy result. Predictability across document types is what actually changes the economics.

Neoflo IDP
93%
Clean PDF
88%
Handwritten
−5 pt drop on handwriting
Rossum
82%
Clean PDF
55%
Handwritten
−27 pt drop on handwriting
What accuracy does to cost

High accuracy means fewer fields go to manual review. Fewer corrections means lower labour cost per invoice. The processing cost gap is real, but it's not the main story — the total cost difference is driven almost entirely by how often the system reads correctly.

Processing cost per invoice
$0.009
Neoflo IDP
vs
$0.044–$0.30
Next best
5–33× cheaper to run than commercial alternatives
Total cost including human corrections
$0.36
Neoflo IDP
vs
$0.69
Next best
Nearly half the end-to-end cost — driven by accuracy, not just processing

The $0.36 vs $0.69 gap isn't mostly processing cost — it's correction cost. A system that reads correctly fewer times is cheap to run and expensive to own. Every wrong field becomes a person's time. At invoice volume, that's a headcount number, not a rounding error.

Five calls that shaped what got built
How it works
01
Ingest

File arrives and is acknowledged in under 200ms. Validation, virus scan, format normalization, and deduplication happen before anything else touches the document.

02
Extract

Layout parsed, document classified, fields extracted with a confidence score and a bounding box on the source page for every value returned.

03
Verify

Fields above the confidence threshold pass automatically. Below it, a reviewer sees only the doubtful ones — each highlighted on the original PDF. Every correction feeds back to data science as training signal.

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Shubham Shrivastava
Shubham ShrivastavaHead of Product · Neoflo.ai
© Shubham Shrivastava 2026