Simplify Document-Intensive Workflows with Intelligent Document Processing
Romulus blends the power of Computer Vision, Natural Language Processing & Knowledge Graph technologies to understand documents, the same way a human subject matter expert would. Romulus is designed to capture, classify, enrich & extract documents of all size, types, source, content or a combination thereof.
Traditional RPA and OCR platforms can automate document processing, but they lack the intelligence to work with complex or fluid-format documents. A context-first approach means that Romulus continues to deliver in scenarios where traditional technologies fall short, consistently delivering quick & reliable results.
No Templates. Ever
Romulus is document layout- agnostic so you don't need to deal with templates or changes in templates
100% self-awareness of data quality
Romulus automatically discovers and flags any potential errors or inconsistencies in the data
Speaks Finance natively
Romulus is trained on a financial dictionary. Also, it can auto-generate documents in industry-standard formats like SWIFT or XBRL
Remodel Financial Documents into Actionable Data with Romulus
Categorize
Romulus reads and classifies documents based their content rather than format or structure using a template-free approach
Extract & Enrich
Romulus extracts relevant data from documents based on the type and enriches it with key reference data as necessary
Validate & Escalate
Romulus ensures that data is both complete & consistent using intra & inter document checks and escalates as required to a human-in-the-loop
Normalize & Persist
Romulus standardizes & exports data in the requisite format (e.g. SWIFT, CSV, etc.) to downstream systems & workflows for further processing
Unstructured data include photos, video and audio files, agreement PDFs, legal documents, open-ended survey responses, websites, phone transcripts/recordings, etc.
Semi-Structured Documents
Semi-structured data include digital photos, SWIFT messages, emails messages, etc.
Structured Documents
Structured data include accounting summary, holding statement, customers’ address and demographic details, star ratings by customers, machines logs, etc.
