An AI-assisted invoice platform for an ethanol manufacturer
Vendor invoices become governed records — AI reads them, a person confirms them.
At a glance
Mindrops built a mobile-first, AI-assisted invoice platform with a web admin portal for an India-based ethanol manufacturer with revenue above ₹100 crore. OCR and AI extract six fields from each vendor invoice (PDF, JPG or PNG), and a person confirms every record before it is saved, with three access roles, OTP/JWT authentication and a searchable repository.
The challenge
The manufacturer receives a high daily volume of vendor invoices — freight, raw material, stores and spares, fuel, chemicals and services — as PDFs, scans and phone photos across email and vendor channels. Invoice volume had outgrown what the finance team could process by hand.
Every invoice had to be opened, read and keyed into internal systems. Classification was left to individual judgement, so the same expense was coded differently by different staff. Manual entry was slow and error-prone — wrong numbers, dates or amounts, and missing supplier details surfaced late in reporting. There was no central view of whether an invoice had been processed, reviewed or paid, no searchable history, and limited control over who could do what. The goal was to turn each unstructured invoice into a governed, searchable record while keeping human oversight in place.
The solution
Mindrops built an AI-assisted invoice platform with a human check at its core. Capture and validation run on a mobile app; oversight and administration run on a web portal. AI does the reading; a person does the confirming.
The platform turns an unstructured invoice document into a structured business record. Nothing becomes data until a person reviews the extracted fields and commits the invoice. Role-based access and a searchable repository complete the platform.
OCR + AI extraction
Uploaded invoices are read automatically — vendor, invoice number, date, amount, company and a suggested category — removing most of the manual keying.
Confirm before save
Extracted values are shown for review; nothing becomes a record until a person checks the fields and commits the invoice.
Re-run on demand
If a read fails or looks wrong, extraction can be re-run on the same file, or fields edited by hand — resilience to poor-quality scans, without re-uploading.
Standard categories
Invoices are coded against a controlled, admin-managed category list, so expense classification is consistent across the team.
Role-based access
Administrator, Staff and Operations roles, with per-module permissions and per-company scope set by administrators — least-privilege and auditable.
Searchable repository
Every invoice is searchable by number, company, vendor or category, with supporting documents stored separately for fast retrieval and fewer duplicates.
Results
The same invoices now arrive as files and become governed records — read by AI, confirmed by a person, found in seconds.
Data entry
Before: six fields keyed per invoice, by hand.
After: fields auto-extracted; staff review and confirm.
Accuracy
Before: errors surfaced late, in reporting.
After: misreads caught against the source before saving.
Classification
Before: the same expense coded differently.
After: one controlled category list, consistently applied.
Status & audit
Before: hard to tell what was processed or paid; little trail.
After: processing and payment status on every record, searchable, with edit history.
Less manual keying, fewer entry errors, consistent classification, clear status at a glance, faster retrieval, controlled access and company-level segregation — with a structured, searchable record produced from the very first uploaded invoice.
Client details are anonymised at the client’s request. The before/after positions describe the change in process delivered and have not been independently audited.
Technologies used
An AI-assisted pipeline with a mandatory human checkpoint, role-based governance and a searchable record store — mobile-first for the people who do the work.
Extract & confirm
OCR and AI extract six fields per invoice, re-runnable per file; a mandatory human confirm with field rules (category, company, valid date, numeric amount) gates every save.
Access & segregation
Role-based access — Administrator, Staff, Operations — with per-module permissions and per-company scope, so data separates cleanly by internal company.
Data, audit & documents
Structured, searchable invoice records with edit history and processing/payment status; supporting documents stored separately and securely.
Frequently asked questions
What did Mindrops build for the ethanol manufacturer?
Which invoice fields does the AI extract?
Which technologies and controls does the platform use?
What problem did the platform solve?
Could Mindrops automate a document-heavy process for us?
Contact us
Tell us about your document-heavy process. Mindrops builds AI-assisted capture and validation systems that turn unstructured documents into governed, searchable records.