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Example Automation: “Auto-Ingest and Seat Reconciliation Bot”

A simple, high-impact automation that a firm like Deloitte would deploy for SplitRight workstreams is a bot that ingests supplier contracts, reconciles seat counts against payment data and flags over-subscription in minutes.

1. Objective

Reduce manual effort spent matching license counts in software agreements to actual usage and invoices so analysts can focus on negotiation strategy, not data wrangling.

2. Inputs

  1. Contract files in PDF or DOCX pulled from the client’s CLM or shared drive

  2. Vendor invoice data (CSV exports from AP system)

  3. Active-user lists from identity management or SaaS admin portals (CSV)

3. Tools Used

  • Microsoft Power Automate Desktop for file handling and RPA steps

  • Azure Form Recognizer or AWS Textract for clause and numeric extraction

  • Python micro-service with Pandas for data matching and variance checks

  • Power BI for near-real-time dashboard of over-subscription savings

4. Workflow Steps

StepAction1Trigger fires nightly when new contracts or invoices land in dedicated SharePoint folder2RPA bot copies documents to Azure Blob, tags metadata and calls Form Recognizer3Parsed output feeds a Python script that:
• Locates seat or user count fields in contracts
• Pulls matching SKUs from invoice line items
• Joins with active-user CSV by SKU4Script calculates over- or under-subscription variance for each supplier5Variance table uploads to Power BI; items over tolerance (e.g., >10 percent over-subscribed) are flagged red6Bot emails a “Daily Seat Variance Report” to the engagement team with a CSV attachment and link to dashboard

5. Outputs

  • Power BI dashboard showing contract seats, paid seats, active users and potential monthly savings

  • CSV variance file archived for audit trail

  • Daily email alert summarizing top five variance offenders

6. Effort Saved and Value Added

TaskManual Hours/MonthAutomated Hours/MonthSavingsContract seat lookup404Analyst time freedInvoice matching252Faster variance detectionReporting151Consistent, error-free updatesTotal807>90 percent time reduction

7. Why It Fits SplitRight

  • Directly supports Duplicate / Split decisions by revealing where seats can be reassigned or terminated

  • Feeds synergy dashboards automatically so executives see live savings potential

  • Scalable across any portfolio because it only requires standardized folder structure and common OCR service

This “auto-ingest and seat reconciliation” bot is simple enough to build in two to three weeks, yet delivers measurable savings on every carve-out or post-merger integration engagement.

Sample Code

Contacts

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