Vinay Gangidi
Vinay Gangidi is a senior technology leader specializing in n8n-based automation and AI-driven workflow orchestration. He builds intelligent, self-hosted workflows using n8n, LLM-powered agents, Python, and Azure AI to eliminate manual work across GTM, product development, finance, legal, business operations, HR, revenue operations, and compliance, with a strong focus on governance and real business impact.
Templates by Vinay Gangidi
Automate invoice-bank statement reconciliation with Mistral AI and OpenAI GPT-4
Cash Reconciliation with AI This template automates daily cash reconciliation by comparing your open invoices against bank statement transactions. Instead of manually scanning statements line by line, the workflow uses AI to: Match transactions to invoices and assign confidence scores Flag unapplied or review-needed payments Produce a reconciliation table with clear metrics (match %, unmatched count, etc.) The end result: faster cash application, fewer errors, and better visibility into your cash flow. Good to know Each AI transaction match call will consume credits from your OpenAI account. Check OpenAI pricing for costs. OCR is used to extract data from PDF bank statements, so you’ll need a Mistral OCR API key. This workflow assumes invoices are stored in an Excel or CSV file. You may need to tweak column names to match your file headers. How it works Import files:The workflow pulls your invoice file (Excel/CSV) and daily bank statement (from OneDrive, Google Drive, or local storage). Extract and normalize data: OCR is applied to bank statements if needed. Both data sources are cleaned and aligned into comparable formats. AI matching: The AI agent compares statement transactions against invoice records, assigns a confidence score, and flags items that require manual review. Reconciliation output:A ready-made table shows matched invoices (with amounts and confidence), unmatched items, and summary stats. How to use Start with the manual trigger node to test the flow. Once validated, replace it with a schedule trigger to run daily. Adjust thresholds (like date tolerances or amount variances) in the code nodes to fit your business rules. Review the reconciliation table each day most of the work is automated, you just handle exceptions. Requirements OpenAI API key Mistral OCR API key (for PDF bank statements) Microsoft OneDrive API key and Microsoft Excel API key Access to your invoice file (Excel/CSV) and daily bank statement source Setup steps Connect accounts: Enter your API keys (OpenAI, Mistral OCR, OneDrive, Excel). Configure input nodes: Point the Excel/CSV node to your invoice file. Connect the Get Bank Statement node to your statement storage. Configure AI agent: Add your OpenAI API credentials to the AI node. Customize if needed Update column mappings if your file uses different headers. Adjust matching thresholds and tolerance logic.
By Vinay GangidiAutomate loan document analysis with Mistral OCR and GPT for underwriting decisions
LOB Underwriting with AI This template ingests borrower documents from OneDrive, extracts text with OCR, classifies each file (ID, paystub, bank statement, utilities, tax forms, etc.), aggregates everything per borrower, and asks an LLM to produce a clear underwriting summary and decision (plus next steps). Good to know AI and OCR usage consume credits (OpenAI + your OCR provider). Folder lookups by name can be ambiguous—use a fixed folderId in production. Scanned image quality drives OCR accuracy; bad scans yield weak text. This flow handles PII—mask sensitive data in logs and control access. Start small: batch size and pagination keep costs/memory sane. How it works Import & locate docs: Manual trigger kicks off a OneDrive folder search (e.g., “LOBs”) and lists files inside. Per-file loop: Download each file → run OCR → classify the document type using filename + extracted text. Aggregate: Combine per-file results into a borrower payload (make BorrowerName dynamic). LLM analysis: Feed the payload to an AI Agent (OpenAI model) to extract underwriting-relevant facts and produce a decision + next steps. Output: Return a human-readable summary (and optionally structured JSON for systems). How to use Start with the Manual Trigger to validate end-to-end on a tiny test folder. Once stable, swap in a Schedule/Cron or Webhook trigger. Review the generated underwriting summary; handle only flagged exceptions (unknown/unreadable docs, low confidence). Setup steps Connect accounts Add credentials for OneDrive, OCR, and OpenAI. Configure inputs In Search a folder, point to your borrower docs (prefer folderId; otherwise tighten the name query). In Get items in a folder, enable pagination if the folder is large. In Split in Batches, set a conservative batch size to control costs. Wire the file path Download a file must receive the current file’s id from the folder listing. Make sure the OCR node receives binary input (PDFs/images). Classification Update keyword rules to match your region/lenders/utilities/tax forms. Keep a fallback Unknown class and log it for review. Combine Replace the hard-coded BorrowerName with: a Set node field, a form input, or parsing from folder/file naming conventions. AI Agent Set your OpenAI model/credentials. Ask the model to output JSON first (structured fields) and Markdown second (readable summary). Keep temperature low for consistent, audit-friendly results. Optional outputs Persist JSON/Markdown to Notion/Docs/DB or write to storage. Customize if needed Doc types: add/remove categories and keywords without touching core logic. Error handling: add IF paths for empty folders, failed downloads, empty OCR, or Unknown class; retry transient API errors. Privacy: redact IDs/account numbers in logs; restrict execution visibility. Scale: add MIME/size filters, duplicate detection, and multi-borrower folder patterns (parent → subfolders).
By Vinay Gangidi