The document says one thing. The form says another.
Claude reads every document your client uploads, extracts the fields, cross-checks them against what was entered in the form, and tells you exactly where things don't match — in plain language, not "error 0x4F". It's not an OCR: it's judgment applied to what the OCR copied.
0s
per document
read + extract + cross-check
0%
accuracy
discrepancy detection
0x
speed
vs manual eye-to-eye review
0
industries
same engine running
UNDER THE HOOD
It doesn't say "rejected". It tells you which field, against what, and why.
A validator that only says "invalid document" forces your team to open the PDF and guess. Claude exposes what it extracted, which field it cross-checked, what didn't match, and what to ask the client — in a sentence anyone can understand.
Lead de ejemplo
Lead crudo entra
Client uploads a photo of their ID document to activate an RCV policy. Form says: name "María Fernanda Rojas", ID "V-18.402.117", date of birth "1990-03-12".
Document extraction
Vision over the ID → name, ID number, date of birth, expiry readable
Field-by-field cross-check
name ✓ · ID ✓ · date of birth ✓ — normalizing accents and separators
Expiry check
future expiry date → document valid, not expired
Image quality
no glare or cropping on key fields → reliable reading
Score
96/100
Veredicto de Claude
Approved. All 3 fields match and the document is current. Activation continues.
WHAT THE AI DOES WITH EACH DOCUMENT
Five tasks your team shouldn't be doing by eye anymore
Every document a client uploads goes through these five steps automatically, in seconds. Your team receives the verdict and the observation — not the PDF.
claude.extract(doc)
Reads the document even if it's tilted, blurry, or a screenshot
OCR + vision over ID documents, invoices, titles, licenses, RIF, certificates of origin, or bank references. Outputs structured fields — not a plain text block.
› input
Document: ID photo (JPEG, slightly tilted)
› claude →
type: national ID document
name: "María Fernanda Rojas"
document: "V-18.402.117"
dob: "1990-03-12" · expiry: 2031
→ 4 fields extracted · high confidence
THE SAME ENGINE
One validator. The document changes, not the logic.
We don't train a model per document type. The same engine reads the document, cross-checks it against each business's form, and observes in plain language. This is already running in production.
Señales propias de la industria
VALIDADOR EN VIVO
El documento entra. Sale con veredicto.
Mira el ciclo completo: el documento entra, la IA lo escanea, los campos se resaltan uno a uno conforme se extraen, y el veredicto aparece con la observación lista para el cliente.
nombre
María Fernanda Rojas
documento
V-18.402.117
fecha_nac
1990-03-12
vigencia
2031-03-12
Los 4 campos cruzan con el formulario y el documento está vigente. Continúa la activación.
CRUCE CAMPO A CAMPO
Lo que dice el documento, lado a lado con tu formulario.
Cada fila es un campo. La IA normaliza acentos, mayúsculas y formatos antes de comparar — y cuando algo no cruza, lo dice en una frase que tu cliente entiende.
Observación que recibe el cliente
“El serial de carrocería de tu factura no coincide con el que registraste (termina en 1, no en 7). Verifica el número o sube la factura correcta.”
ERRORES QUE ATRAPA
Los errores que se le escapan a un ojo cansado. A las 2am, no.
Serial que no cuadra, documento vencido, monto distinto, RIF equivocado, foto ilegible. Abajo, el log crudo de la máquina — y al lado, lo que tu equipo y tu cliente realmente leen.
CONNECTED STACK
Lives inside your document flow. Doesn't replace it.
The validation layer connects wherever your clients already upload documents. If your core has a REST API, we talk to it.
Claude · Anthropic
Certified PartnerThe engine: vision extraction, field cross-check, classification, and observation
Supabase
Certified PartnerDocument storage + Deno Edge Functions for real-time validation
Make / n8n
Webhooks to your core or ERP when a document is approved or flagged
WhatsApp Business
The friendly observation reaches the client through the same channel they used to upload
Kommo CRM
Certified PartnerThe process status (approved/flagged) syncs with the opportunity in your CRM
Monday.com
Queue of flagged documents for an analyst to resolve edge cases
Frequently asked questions about document validation with AI
No. An OCR copies text and stops there. Here, Claude reads the document, extracts structured fields, cross-checks them against what the client entered in your form, decides whether the difference is blocking, and drafts an observation the client understands. OCR is just the first of five steps.
National ID, driver's license, RIF, property title, certificate of origin, invoice, bank reference, articles of incorporation — and anything else with fields that can be cross-checked against a form. It's not tied to a template: it reasons about content, not a fixed layout.
Yes, within reason. The vision model tolerates tilt, mild glare, and low resolution. When a key field is unreadable, it doesn't guess: it marks it as "unreadable" and asks for a better photo rather than approving blindly. We prefer a "please re-upload" over a false positive.
You set the threshold. The typical setup: approves automatically when all critical fields match, flags and notifies the client automatically on a clear discrepancy, and escalates to a human only in ambiguous cases — with context already assembled. It's gradual: you increase autonomy as you build confidence in its judgment.
Because an "invalid document" with no explanation forces your analyst to open the PDF and guess what failed. Seeing which field didn't match, against what, and why turns a minutes-long review into a seconds-long decision — and gives the client an actionable observation, not a flat rejection.
In the configuration. An extra accent or space is usually tolerable; a serial, a RIF, or an expired document is usually blocking. You flag which fields stop the process and which are only noted. The engine respects those rules — it doesn't invent them.
Yes. If your core has a REST API we integrate directly; if documents live in Supabase Storage or a bucket, we read from there; if the flow is legacy, we connect via Make/n8n. The client keeps uploading where they already do — validation happens behind the scenes.
Setup + monthly fee + variable cost per document processed (cents of USD). Implementation: 3-4 weeks to have extraction + cross-check + observations live on your flow. Schedule a 30-min assessment and we'll give you the exact range for your document types.
A WORD FROM THE FOUNDER
“The problem was never reading the document. It was having someone compare it, field by field, against the form, 300 times a day.”
In insurance I saw an entire team stuck at RCV policy activation: every client uploaded an ID, an invoice, a title — and someone had to open each file and manually check whether the invoice serial matched the form serial. One digit off and the policy was issued wrong. The exhausting part wasn't reading — it was cross-checking, without mistakes, all day long.
Document Validation AI isn't about adding an OCR to your flow. It's about adding the eye of your best analyst: one that reads the document, cross-checks it against what the client wrote, knows when an accent doesn't matter and when a serial does, and explains to the client what to correct in a single sentence — at 2am, across all 300 documents, missing none.
If your team spends its time opening PDFs to compare fields, schedule 30 minutes with me. Not a sales rep. Me. I'll show you the cross-check live on a sample document from your own flow.
Gabriel Montiel
CEO · Boosty Digital
LET'S TALK
Ready to stop comparing documents by eye?
Schedule a 30-minute assessment. We bring a real document from your flow and show you the extraction, cross-check, and observation live. No corporate deck, no theater.