When a vendor sends a PDF invoice by email, Quantbit's AI connector captures it from the ERPNext email account, extracts raw text using OCR, and sends that text to OpenAI's API with a structured extraction prompt. The AI model identifies and extracts the vendor name, GSTIN, invoice number, date, HSN codes, line items, tax amounts, and total — even from invoices with non-standard formats. This extracted data is used to pre-fill a Purchase Invoice draft in ERPNext. Your accounts payable team reviews the draft for accuracy and submits. Invoices that used to take 3 to 5 minutes of manual entry now take under 30 seconds to review and approve.
Yes. Quantbit's AI connector for ERPNext supports Ollama as a local inference backend — meaning all AI processing happens on your own server hardware, and no document data is sent to OpenAI, Anthropic, or any other external service. This is particularly relevant for businesses handling sensitive financial documents, healthcare records, or defence-sector procurement where data residency is a compliance requirement. Ollama running Mistral 7B or Llama 3 handles most ERPNext AI automation tasks reliably on a standard server with a GPU.
Connect ERPNext to OpenAI, Anthropic Claude, Azure AI, Whisper, or local Ollama models. Automate invoice extraction, document classification, customer query handling, and data entry — without changing how your team works.
Different use cases need different models. We support cloud-based and local inference — and you can switch or combine them as your needs evolve.
The strongest general-purpose model for complex document extraction, nuanced customer query handling, and multi-step reasoning tasks inside ERPNext workflows.
Claude excels at long document analysis, policy document processing, and situations where careful, step-by-step reasoning is more important than raw speed.
Azure's pre-trained document models are optimised for structured documents — invoices, receipts, ID cards, and forms — with high accuracy on Indian and Arabic formats.
Whisper converts speech to text with remarkable accuracy across Indian languages and English. Use it to transcribe customer calls, voice memos, and site inspection recordings into ERPNext records.
Run Mistral 7B, Llama 3, or Phi-3 entirely on your own server. No data leaves your premises. Ideal for sensitive industries — healthcare, defence, banking — that cannot send documents to external APIs.
Before any AI model can read a scanned invoice or image, it needs OCR. Our connector includes OCR pre-processing using Tesseract (local) or Azure AI Vision, optimised for Indian and Arabic scripts.
Here is a practical map of what each AI capability handles — and which model does it best.
These are not hypothetical use cases. They are situations we have seen across manufacturing, healthcare, trading, and facility management businesses — and what happened after AI took over the reading.
A precision engineering company in Kolhapur was buying components from 80+ vendors. Every vendor invoice arrived differently — some were PDFs with clean digital text, some were photographs of handwritten challans, and a few were WhatsApp images of invoices. The accounts team would receive these, open each one, manually read the details, and type them into ERPNext. On a heavy day, one accounts executive would spend her entire afternoon on nothing but invoice data entry. A single mistake — wrong amount, wrong vendor, wrong tax code — could take two days to trace and fix. After deploying the AI document connector with GPT-4o and Azure Document Intelligence, every invoice that arrives by email goes through automatic extraction. The accounts executive now spends her afternoons reviewing exception alerts — the three or four invoices per week where the AI flagged a mismatch or low confidence — rather than typing 40 invoices from scratch. Invoice processing time dropped from an average of 4 minutes each to under 30 seconds of human review.
A 250-bed hospital in Pune wanted to use AI to extract data from lab reports and discharge summaries — but their legal team flagged that patient records could not be sent to OpenAI or Azure due to data residency and HIPAA-adjacent concerns. The hospital had invested in a decent server with an NVIDIA GPU. Quantbit's AI connector was deployed with Ollama running Llama 3 on their local server — a completely on-premises AI setup. Lab reports arriving from their LIS system are now processed locally: Ollama reads the report, extracts key values (blood counts, HbA1c levels, culture results), and populates the patient record in ERPNext HISx. The hospital gets AI-powered document automation with zero data leaving the building. The setup has been running for six months without a single cloud AI API call for clinical data.
An import-export trading business in Mumbai was getting inquiries, order confirmations, shipping instructions, and complaints all mixed together in a single company inbox. One person — the owner's son — was responsible for reading every email and deciding what it was, and then manually creating the relevant record in ERPNext: a lead, a sales order, a delivery request, or a support note. He was spending two to three hours every morning just on email triage. Quantbit's AI connector now reads every incoming email, classifies it into one of seven categories the business defined, extracts the relevant details (customer name, items, quantities, urgency), and either creates the ERPNext record automatically or surfaces it in a classification review queue. He now spends 20 minutes reviewing the AI's work rather than three hours doing it himself. The inbox went from a source of anxiety to a predictable, manageable workflow.
A facility management company in Muscat employs 40 site technicians who perform daily inspections across commercial and residential properties. After each inspection, technicians were supposed to fill in an ERPNext form — but filling out a form on a phone while standing in a plant room or on a rooftop is inconvenient, and completion rates were poor. Half the inspection data never made it into ERPNext at all. After deploying the Whisper voice transcription connector, technicians record a one to two minute voice note on their phone describing what they found, any issues, and what needs to be done next. Whisper transcribes the Arabic or English voice note, and the AI connector structures it into an ERPNext maintenance log with issue classification, recommended action, and urgency level. Inspection data completeness went from 45% to 93%.
A construction company in Nashik was bidding on MSRDC and Municipal Corporation tenders. Each tender document was 200 to 400 pages of specifications, eligibility criteria, bill of quantities, and legal terms. The project manager responsible for reviewing these was spending three to four days on each tender just reading and extracting the key information he needed to decide whether to bid and at what price. Claude's long-context document analysis was integrated into ERPNext — the project manager uploads the tender PDF, the AI reads the entire document, and within minutes produces a structured summary: eligibility criteria met or missed, key milestones and submission deadlines, scope of work summary, and a flag list of unusual or high-risk clauses. The project manager now spends half a day per tender instead of three days — and catches eligibility issues early, before spending hours on a bid they would eventually be disqualified from.
The finance team at a group company in Oman with operations in India and the GCC was importing bank statements into ERPNext monthly. The problem was not the import — the problem was the 30 to 40 unmatched transactions every month that the system could not automatically reconcile. These were legitimate business payments — international wire transfers, bank charges, forex conversion fees, subsidiary cross-charges — but they had reference formats that ERPNext could not auto-match. A junior accountant was spending two days every month going through these manually, calling colleagues to understand what each transaction was for. AI classification now analyses each unmatched transaction — reads the description, amount, counterparty name, and date — and suggests the most likely ERPNext ledger account, cost centre, and party based on historical matching patterns. The accountant now approves or rejects the AI's suggestion rather than researching each one from scratch. Those two days became two hours.
A PDF invoice lands in your ERPNext email inbox, or is uploaded manually, or is pushed from a supplier portal. The connector detects new documents based on your configured trigger rules.
If the document is a scanned image or a non-text PDF, the OCR layer converts it to machine-readable text first. Image quality enhancement runs automatically for low-resolution scans.
The extracted text is sent to your configured AI model with a structured extraction prompt specific to the document type — vendor invoice, delivery challan, expense receipt, or custom format you define.
Extracted fields are mapped to ERPNext document fields and a draft record is created — Purchase Invoice, Journal Entry, Expense Claim, or any document type you configure.
Your team reviews the pre-filled draft, makes any corrections, and submits. For documents where AI confidence is high, you can configure auto-submission with a daily review report instead of per-document review.
For the technical team evaluating this integration — what the connector is built on and how it fits into your existing infrastructure.
Show us one document your team processes manually every day. We will demonstrate exactly how AI extracts it into ERPNext — live, with your actual documents, in the first call.
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