The Al-powered Document Processing Platform that transforms Complex and Unstructured Inputs into Masterpieces
Behind every masterpiece lies a variety of complex, disparate and unstructured elements. Similarly, with critical documents also where sensitive data needs to be processed from a diverse set of inputs.
This is where Doc2API Platform comes in:
Its AI-based platform works with just about any kind of input of any quality to efficiently produce digital documents that are accurate and integrated into any system at speed and with scale.
Doc2API leverages key technologies such as AI, ML, NLP and Computer Vision to further validate, verify and analyse processed data, for specific use cases and opportunities. Doc2API is industry agnostic and can help in digitising and automating any document-intensive process.
It hosts a library of pre-trained AI models for common use cases. With Doc2API, the integrations within your systems are tightly coupled through powerful APIs. These APIs augment Doc2API as modular and allow easy integration with downstream systems. Simply put, the Doc2API platform brings together the best of the world capabilities of processing, extraction, validation and verification and is format agnostic.
The platform derives its accuracy from the base models which are trained on the scores of documents of each format. This accuracy of the platform will compound through extensive pre-processing and enhanced input quality.
By operationalising Doc2API in the system, the throughput increases greatly as the operation overheads reduce by over 50%
Doc2API Platform is built on a cloud-native, multi-tenant architecture that offers speed and scale, while ensuring security and protection of customer data.
Doc2API provides seamless integration capabilities using powerful APIs which easily pair with upstream or downstream systems. Doc2API also offers to normalise the output according to the data schema required by the customer.
The Doc2API Platform transforms the Operational Team into a Smart Processing Team by enabling it to handle exceptions and thus significantly reduce the turnaround time and improve the productivity of the team.
Doc2API provides an AI model-based Classification, which means that it can identify various document types from the uploaded files, even if uploaded as part of a single PDF or TIFF file. It splits the uploaded file into different document types, thus overcoming the practical challenges of ordering and sorting pages in the scanned file, prior to any further processing.
The platform then contextually Extracts data at the field, entity, checkbox, table and section levels and easily responds to variations from incoming documents. This sets it apart from traditional OCR tools in the market that are more position-based.
Doc2API provides post-extraction capabilities to normalise the output of client-specific schema requirements, so all the extracted data can be translated into a standard set of fields. Doc2API can Validate against configurable business rules to identify discrepancies or exceptions from the extracted data within or across documents. It provides Enrichment opportunities to integrate with third-party data sources, while addressing data coverage and data quality issues from the submitted documents.
An operational user can be brought into the loop with an intuitive point and click user interface to handle exceptions where the extraction falls off the STP flow. The user validation also serves as a feedback loop, and the machine learning model is retrained based on user inputs, to improve accuracy over time.
Doc2API is modular and its API-driven design allows seamless pluggability to upstream and downstream business processes. The transformed data can be consumed in various formats like JSON or a CSV file.
Doc2API Platform's Process Stages
Automatic document classification based on text, images and rules
Extract contextually relevant information from unstructured and semi-structured data, by leveraging purpose-built AI models
Normalise the output to client-specific schema requirements
Input data is validated leveraging configurable business rules to identify discrepancies and exceptions
Append valid data by integrating with external data sources, thus addressing data quality issues
Human in loop to triage and make decisions on exceptions, errors and approvals
Integrate structured output to a target system effortlessly