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AI-powered automation of complex POs using LLMs and backend APIs.
OPS is an AI-driven Intelligent Document Processing (IDP) and workflow automation platform designed for Echem to automate the transformation of unstructured purchase order PDFs into structured, validated data. Leveraging a fine-tuned local and proprietary extraction pipeline, the system extracts critical PO data, allowing users to validate and correct via an intuitive dashboard before pushing it seamlessly into Echem’s Order Management System (OMS).
Echem is a specialized player in the chemical trading and sourcing domain, managing a high volume of supplier communications and purchase orders daily.
P reviously reliant on manual, email-driven workflows, Echem faced inefficiencies and human errors in processing complex POs. To address these challenges, Echem initiated the Order Processing System (OPS) project to digitize and automate the end-to-end PO lifecycle, enhancing operational speed and accuracy..
Pharmaceutical, Chemical
AI-powered Order Processing Automation System with LLM integration and backend API services.
Comprehensive solutions designed to enhance user experience and drive business growth.
Automatically fetches POs from emails and allows manual uploads through the interface.
Extracts CAS numbers, chemical names, quantities, and delivery terms using a fine-tuned Mistral-7B model.
Dashboard for validating extracted data, highlighting low-confidence fields for correction.
Pushes validated PO data into the OMS through secure API-based pipelines.
Maps extracted chemical data with internal records, handling inconsistencies.
Logs user actions and system outputs for traceability and compliance.
Tracks volumes, accuracy, and processing speed for actionable insights.
Dockerized modules for ingestion, extraction, and validation supporting independent scaling.
Alerts users to failures or review-needed documents to prevent losses.
We identified key pain points and developed targeted solutions to transform the resort's digital presence.
Wide variations in PO formats, including scanned documents and complex layouts.
Missing fields or inconsistent layouts across suppliers
Non-standard naming and CAS numbers posed challenges in standardization.
Matching extracted data accurately with internal records
Limited labeled historical data for effective model training
Trained models on Echem's historical PO data to improve extraction.
Handled format inconsistencies with domain-specific rules.
Used local Mistral-7B for contextual understanding and extraction.
Mapped extracted data to internal SKUs using fuzzy logic.
React-based interface for easy validation and correction.
Deployment architecture supports flexibility and scaling.
Visual highlights showcasing the transformation and key features of the new website.
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Significant time savings, faster procurement turnaround, and enhanced data accuracy.
Automated data capture reduces manual workload dramatically.
Processes POs faster with less manual validation.
Improved accuracy through contextual LLM extraction and validation feedback loops.
Integrates effortlessly into OMS and prepares for broader ERP syncing.
System design allows for easy expansion and maintenance.
Built groundwork for a fully automated, intelligent procurement lifecycle.
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