We present Parsley, the framework we use at Plate IQ to process and automate more than 2M+ invoices and over $2B+ transaction volume a month. Plate IQ is a leader in the Accounts Payable automation industry. Accounts Payable automation is a global multi-billion dollar industry that is transforming how businesses operate and is essential for businesses to streamline their operations, reduce costs and be more efficient. Automating Invoice Processing is a key fundamental component that is required for automating accounting workflows and doing it at scale has been an incredible challenge for the industry.

Parsley uses a human in the loop AI approach towards Invoice Automation and is able to process invoices at scale at a very low cost with a very high accuracy. We use state of the art Deep Learning models on Invoice images and text to extract structured information at both Invoice header and line item level. Using validation models that use statistics and domain heuristics we select a fraction of the model predictions that require human review. Our review tools are designed to empower the human reviewer to only focus on data points that require intervention and correction, thereby maximizing the human throughput. Corrections from human reviews are used in training the next version of our models, thereby making the system self-corrective over time. The combined effect of our Deep Learning models and Human review system dramatically reduces the cost of processing an invoice to less than one-tenth of the industry average while maintaining a < 3% error rate. In this talk we will present Parsley’s architecture and challenges in building a self adaptive human in the loop AI system.

Krishna Janakiraman is the VP of Engineering at Plate IQ, Plate IQ is a leader in Accounts Payable and Payments industry. At Plate IQ he was a founding engineer and led the development of Plate IQ’s industry leading Invoice and AP Automation platform. Krishna received his Masters degree from the School of Information, UC Berkeley. At Berkeley Krishna did research on Record Linkage algorithms and Probabilistic Generative Models for Music. His current research interests include Deep Learning for Document Processing and Lifelong Machine Learning