Indico Data

The Customer

The customer transforms unstructured data into actionable insights. Our customer has been on the forefront of innovation in unstructured data and intelligent document processing. Customer deploys enterprise large language model approach to process 99% of all US titles and deeds across 150,000 document variants and 50 million annual transactions with just 12 custom models.

The Ask

Data labelling and classification for a real estate data provider, intake and process tens of thousands of complex, unstructured documents, and look for ways to automate the workload. Key asks were

The speed at which the data becomes available in an actionable format and the accuracy of that data. As currently the intake problem was addressed with brute-force manual data entry
Reliably automate the intake process, identifying and extracting relevant data from unstructured title and deed documents published across the US by local municipalities – up to 40 million documents each year.

35

Faster Product Development

40

Specialists Recruited in 60 Days

30

Cost Savings

The Implementation

Digital Convergence Technologies deployed a team of
Data Acquisition Specialist whose role is to perform data labelling operations and model training efforts. Team’s goal is to conduct a thorough documentation review, execute the data labelling process and provide human in loop service for production workflows. The Data Acquisition specialist team is also supported by

Site Manager
Business Analyst
Technical Consultants
Platform Support
Engineer
QA team

The Solution

Our customer has an Intelligent Intake solution is based on its proprietary Transfer Learning approach, allowing non-technical users to quickly build custom machine learning models tailored precisely to the end customer’s needs. The cloud-native microservices architecture enables continuous optimizations, custom attributes for extraction, classification, etc., and quickly build models with proprietary AI-assisted labeling. The solution had the enterprise-scale capabilities. The cloud-native microservices architecture enables continuous optimization of the compute footprint and can scale up to any throughput or processing volumes.

Milestones and Growth

Captive Center to Hybrid Model Transition: After two years, DCT successfully transitioned the op- erations to the newly created Abacus India entity, transferring all the recruited employees. Post-tran- sition, DCT continues to support Abacus on a hy- brid model, offering flexible burst capacities and additional support as needed. This included orga- nizing bootcamps and providing UI/UX support to maintain service quality and enhance the product development lifecycle.
Continued Partnership: The relationship with Abacus has evolved beyond a traditional GCC model. DCT now engages in diverse collaborative and support activities, enabling Abacus to flexibly manage workload peaks and continue innovating without the operational overhead of maintaining a full-scale continuous team.

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