AstraForge Achieves 98% Extraction Accuracy

AstraForge Manufacturing is a tier one global manufacturer with multi regional sales teams and complex, contract driven pricing. The sales operations group is a mid-sized team (customer size: 201–500 employees) that processes a high volume of incoming quotations across PDF, Word and Excel attachments. Microsoft Teams is AstraForge’s primary collaboration platform and SAP is the authoritative system of record for pricing and quotations. The company’s digital transformation initiative emphasized the use of cloud services and enterprise software development practices to enhance automation and data accuracy.

AstraForge needed a seamless, low friction approach to ingest quotation emails and attachments, extract and validate quotation fields, and keep SAP synchronized in near real time while preserving an auditable trail inside Teams. To meet this need, we implemented an integrated Quotation Details Extractor workflow that continuously monitors the sales inbox, extracts key quotation fields using document intelligence, validates them against SAP records, enables inline review inside Teams, and updates SAP securely. Processed emails are automatically moved to a centrally managed “Completed” folder to ensure mailbox hygiene and traceability. The workflow was architected under Azure consulting services guidelines to ensure reliability and scalability for enterprise operations.

• Sector: Manufacturing
• Project Type: Quotation automation & SAP integration
• Platform: Microsoft Teams & Outlook | SAP ECC/S/4HANA | Azure Functions | Document Intelligence/IDP service

• Incoming quotations arrived in inconsistent formats (PDF/Word/Excel) and lacked a standardized structure, making reliable automated capture difficult. 
• Each quotation required manual review and entry into SAP, consuming 10–15 minutes per quotation and creating a high error surface. 
• Fragmented visibility across teams produced duplicate records, missed follow ups, and slow coordination during sales cycles. 
• Absence of real time SAP synchronization caused sales and pricing data to be stale during order negotiations. 
• Manual email tracking and folder management produced weak auditability and made dispute resolution time consuming. 

These failure modes were confirmed through inbox audits, interviews with sales ops staff, and time motion observations, revealing a strong need for technology consulting, structured product engineering, and improved quality assurance mechanisms.

• Continuous Sales Inbox Monitoring inside Outlook / Teams – The workflow watches the sales inbox for new quotation emails and attachments and surfaces them in Teams for structured processing. 
• Document Intelligence + Multi-format IDP – AI Builder / Document Intelligence pipelines extract header fields (quotation number, customer, validity, currency, delivery terms) and line items (SKUs, quantities, unit prices, discounts) from PDFs, Word and Excel files, returning confidence scores per field. 
• SAP Matching & Secure API Updates – Extracted fields are validated against SAP masters (customer, material, pricing conditions). Confirmed quotations are written to SAP via secure API calls, and existing records are updated where applicable. 
• Inline Review and Correction in Microsoft Teams – Users review extracted data inside Teams cards, edit low confidence fields, and perform one click actions such as Confirm & Push to SAP or Return to Sales Rep, eliminating costly context switches. 
• Automated Inbox Management & Audit Trail – After confirmation, the original email is moved automatically to a managed “Completed” folder; all actions append time stamped logs to support audits and reconciliation. 
• Exception Queue with Automated Notifications – Discrepancies or missing contractual pricing route to an exception queue with Teams notifications and suggested remediation steps to accelerate resolution. 
• Operational Dashboards & Continuous Tuning – Dashboards track extraction confidence, match rates, time to SAP, and exceptions; this KPI feedback loop drives weekly model and rule tuning. 

• Image retrieval speed improved by 78% – Editors could now find event specific photos in under 2 minutes compared to 9-10 minutes earlier.
• Manual tagging reduced by 85% – AI automation eliminated most repetitive tagging, saving 2,500 staff hours annually (1.2 FTE).
• Fan engagement increased by 42% – Personalized photo albums and curated event galleries attracted more interaction across social media and fan portals.
• Publishing cycle shortened by 55% – Breaking stories now went live with supporting visuals within minutes, enhancing Foto Hub’s reputation for timeliness.
• Metadata accuracy raised to 97% – Consistency in tags and credits improved content reliability and reduced licensing disputes.
• Operational costs reduced by 30% – Lower reliance on manual labour for tagging and reduced rework saved significant costs across departments.
• New monetization opportunities unlocked – Organized photo libraries made it possible to license curated sets to sponsors, brands, and fan clubs at premium rates.