Document Processing & Extraction

Vantage includes specialized nodes for extracting structured data from unstructured sources — PDFs, images, audio/video files, and web pages. Combined with AI Enrichment, these nodes let you turn mountains of documents into queryable, actionable data sets.

Process Invoices and Automate Accounts Payable

Extract data from vendor invoices (PDF) and reconcile against purchase orders.

Scenario: An accounts payable department receives hundreds of vendor invoices monthly as PDF attachments. They need to extract line items, validate against POs, and route for approval — without manual data entry.

Workflow Steps:

1. Logical Trigger — Fire when a new file is uploaded to the invoices folder
2. File Upload — Accept the incoming PDF invoice
3. PDF Extract — Extract all text, tables, and structured data from the invoice
4. Text Extraction — Parse the extracted text to identify key fields: vendor name, invoice number, date, line items (description, quantity, unit price, total), tax, grand total
5. AI Enrichment — Validate and clean the extraction:
   • Correct OCR errors in vendor names
   • Normalize date formats
   • Map vendor names to your vendor master list
   • Flag missing or suspicious fields (e.g., mismatched totals)
6. Database Query (PostgreSQL) — Look up the matching purchase order by PO number or vendor + amount
7. Data Validation — Three-way match: invoice, PO, and receipt
   • Quantity match: invoice qty ≤ received qty
   • Price match: invoice unit price = PO unit price (within 2% tolerance)
   • Total match: calculated total = stated total
8. Multi-Conditional — Route by validation result:
   • Full match → DB Write (post to AP subledger) + Send Email (approval notification to cost center manager)
   • Partial match (minor discrepancy) → Dashboard Output (review queue) + Send Message (Slack) to AP analyst
   • No match or major discrepancy → Send Email to procurement + Create Issue (Jira) for investigation
9. Dashboard Output — Populate:
   • Metric Tile — Invoices processed today, auto-match rate %
   • Table Tile — Invoice review queue with match status
   • Bar Tile — Processing volume by vendor
   • Stat Tile — Average processing time (submission to posting)

Key Nodes: Logical Trigger, File Upload, PDF Extract, Text Extraction, AI Enrichment, Database Query, Data Validation, Multi-Conditional, DB Write, Send Email, Send Message, Create Issue (Jira), Dashboard Output

Analyze Contracts and Extract Key Clauses

Extract key terms, clauses, and obligations from legal contracts automatically.

Scenario: A legal team needs to review hundreds of vendor contracts to identify key dates, termination clauses, liability caps, and auto-renewal terms for an upcoming audit.

Workflow Steps:

1. Schedule Trigger — Run once (or on demand via Workflow Input)
2. Database Query (PostgreSQL) — Pull the list of contracts due for review with their storage locations
3. Read Files (Google Drive) — Download contract PDFs from the shared legal folder
4. PDF Extract — Extract full text from each contract
5. AI Enrichment — Analyze each contract to extract:
   • Parties: who are the contracting parties?
   • Key dates: effective date, expiration date, auto-renewal date, notice period
   • Financial terms: total contract value, payment schedule, penalties
   • Key clauses: termination for convenience, limitation of liability, indemnification, non-compete, confidentiality
   • Obligations: specific deliverables, SLAs, reporting requirements
6. Data Validation — Flag contracts with:
   • Expiration within 90 days (requires renewal decision)
   • Missing key clauses (no termination for convenience = risk)
   • Unlimited liability (no cap on damages)
7. Multi-Conditional — Route by risk:
   • Expiring soon (< 90 days) → Send Email to contract owner + Dashboard Output (List Tile with renewal deadlines)
   • High risk clauses → Send Email to General Counsel + Create Issue (Jira) for legal review
   • Routine → Dashboard Output only
8. Write Excel — Generate a contract summary workbook: one row per contract with all extracted fields
9. Dashboard Output — Populate:
   • Gantt Tile — Contract timelines (start → end) with renewal windows
   • Table Tile — Full contract inventory with sortable extracted fields
   • Metric Tile — Contracts expiring this quarter, % with risk clauses
   • Pie Tile — Contracts by risk category

Key Nodes: Schedule Trigger, Database Query, Read Files (Google Drive), PDF Extract, AI Enrichment, Data Validation, Multi-Conditional, Write Excel, Send Email, Create Issue (Jira), Dashboard Output

Relevant Integrations: Google Drive, OneDrive, Jira, Gmail

Transcribe Meetings and Track Action Items

Transcribe recorded meetings, extract action items, and distribute them to the team.

Scenario: A project management team records all client meetings. They need automatic transcription, with action items extracted and assigned in Jira, and a summary posted to the project Slack channel.

Workflow Steps:

1. Logical Trigger — Fire when a new recording is uploaded
2. File Upload — Accept the meeting recording (MP4, MP3, WAV)
3. AI Transcriber — Transcribe the audio to text with speaker identification
4. AI Enrichment — Analyze the transcript to extract:
   • Meeting summary: key topics discussed, decisions made
   • Action items: what, who, by when
   • Questions raised: unresolved questions that need follow-up
   • Sentiment: overall meeting tone and any areas of disagreement
5. Multi-Conditional — For each action item:
   • Has assignee and due date → Create Task (Jira) with all details
   • Missing assignee → Send Message (Slack) to project manager asking for assignment
6. Write PDF — Generate meeting minutes document with sections: Attendees, Agenda, Discussion, Decisions, Action Items
7. Send Email (Gmail) — Email the meeting minutes PDF to all attendees
8. Send Message (Slack) — Post the AI-generated summary and action item list to the project channel
9. DB Write — Store the full transcript and extracted data for searchability
10. Dashboard Output — Populate:
    • Activity Timeline Tile — Meeting history log with links to transcripts
    • Table Tile — Open action items across all meetings
    • Stat Tile — Action items created this week, % completed on time

Key Nodes: Logical Trigger, File Upload, AI Transcriber, AI Enrichment, Multi-Conditional, Create Task (Jira), Write PDF, Send Email, Send Message, DB Write, Dashboard Output

Automate Web Research and Data Aggregation

Scrape and consolidate information from multiple web sources into structured datasets.

Scenario: A research team needs to collect, structure, and update data from dozens of web sources — government databases, industry publications, and competitor websites — and maintain a regularly updated knowledge base.

Workflow Steps:

1. Schedule Trigger — Run weekly
2. Web Scraper — Scrape target URLs: industry reports, government statistical releases, competitor pricing pages
3. URL Reader — Parse additional data feeds: RSS feeds, API endpoints returning HTML/JSON
4. Union — Merge all scraped data into a single dataset
5. Text Extraction — Extract specific data points from unstructured web content: dates, numbers, names, categories
6. AI Enrichment — Normalize and classify the extracted data:
   • Standardize date formats, currency values, and units
   • Classify by topic, source reliability, and data freshness
   • Detect changes from previous scrape cycle
7. Deduplicate — Remove duplicate entries from overlapping sources
8. Data Validation — Flag questionable data: outlier values, stale content, broken sources
9. DB Write — Update the research database with new and changed records
10. AI Summary — Generate a weekly research digest highlighting new findings, key changes, and notable trends
11. Send Email — Distribute the research digest to the analysis team
12. Dashboard Output — Populate:
    • Table Tile — Research database with filters by source, topic, and date
    • Event Trends Tile — Publication frequency and topic trends
    • Metric Tile — Sources monitored, records updated, data freshness score

Key Nodes: Schedule Trigger, Web Scraper, URL Reader, Union, Text Extraction, AI Enrichment, Deduplicate, Data Validation, DB Write, AI Summary, Send Email, Dashboard Output

Example Dashboard: Document Intelligence Hub

Build this dashboard to monitor document ingestion pipelines, extraction accuracy, and processing queues.

Row 1 — Processing Metrics

Row 2 — Pipeline & Quality

Row 3 — Exceptions & Review

Row 4 — Trends & Insights

Getting Started

To build document processing workflows:

1. Choose your input — File Upload for manual uploads, Read Files for cloud storage, Logical Trigger for automated processing
2. Extract the data — Use PDF Extract for PDFs, AI Transcriber for audio/video, Web Scraper for web pages, Text Extraction for unstructured text
3. Enrich and validate — Add AI Enrichment to classify and normalize, Data Validation to check quality
4. Store the results — DB Write to your database, Write Excel/PDF/CSV for file outputs
5. Route and notify — Multi-Conditional to route by classification, Send Email/Message for alerts