Turn policies, forms and invoices into checked, structured data. Processing runs inside your systems, with people reviewing failed checks.
Identify documents. Mask personal data.
Read fields against your rules.
Structured data, with exceptions for people.
Processing runs inside your infrastructure; failed checks go to a person.
Rule-based tools caught only part of the field list in the insurance case. Operations still retyped policy documents by hand.
The engine identifies the policy type, loads its rulebook, masks personal data and verifies extracted fields against format and policy rules. Failed checks go to a person.
WhatsApp, email, chatbot
Identify policy type and load its rulebook
Mask names, phones and IDs before any AI reads it
Extract 180+ fields
Verify every field against format and policy rules
Route failed checks to a person
Insurance marketplace, India
LiveDelivered by our founding team at a previous firm. Clients anonymised under NDA.
Some problems repeat across clients, so we package them as engines you deploy in your VPC or on-prem and pay for by volume. Real-time invoice parsing and validation for supply-chain finance: varied invoice layouts and handwritten tier-3 bills, three-way PO/GRN/invoice match, duplicate and tamper checks, sub-second against 30 seconds to 3 minutes for legacy async queues, 50–60% lower cost per invoice than template OCR (benchmarked on representative sets). Bank-statement analysis for underwriting as a companion.
Every field is checked against format and policy rules. Failed checks are routed to a person.
No. We build and deploy inside your infrastructure.
No. The invoice engine figures are benchmarked on representative sets. Proof of value runs on your own documents.
For custom builds, the code, the weights we train for you and your data are handed over to your team. Packaged engines use volume-based pricing; confirm their deliverables and usage terms when scoping the engagement.
Tell us what your team does today, where it gets stuck and what a better outcome would look like.