Digital Transformation initiatives are transitioning from Robotic Process Automation (RPA) to Intelligent Automation (IA), making Intelligent Document Processing (IDP) one of the fastest-growing software categories because of its potential to dramatically lower costs and improve customer/vendor experience.
However, IDP solutions for use cases like invoice processing are limited to automating 50-80% of the processing. The remaining invoices require manual, human-in-the-loop (HILT) review. More complex Unstructured Data Processing (UDP) use cases involving images and videos (think employee safety monitoring and drone inspections) also need HITL support, limiting cost savings and scale.
Enterprises have typically turned to in-house experts or third-party resources for HITL, which requires significant training, enablement, and QA—further adding to the cost and complexity of scaling IA initiatives. To help overcome these limitations, and lessen the burden on teams building artificial intelligence (AI) applications, we built the super.AI Data Processing Crowd.
Data Processing Crowd is a group of crowd-sourced workers available to process unstructured data and documents on-demand. Think of them as Uber drivers for data processing that are:
Our crowd offers the following benefits compared to alternative in-house or third-party data processing resources:
The growth of the gig economy has laid the groundwork for leveraging crowd-sourced resources to meet Service Level Agreements (SLAs) and for continuously monitoring their work. With software performing the role of supervisors, crowd-sourced workers have the potential to provide higher quality data processing at a much lower cost than traditional in-house or Business Process Outsourcing (BPO) providers.
BPO companies hire and maintain their employees. Like taxi companies before Uber, they are saddled with a higher cost structure. Gig workers want flexibility in work. They are willing to forego the safety of full-time employment and associated benefits for freedom and flexibility. Moreover, BPO companies are not software companies. The only revenue they derive is from manual work. This creates an incentive for them to maintain the human-in-the-loop workload and to drag their feet on automation. The net result is a significantly higher cost for enterprises.
Super.AI has a large globally distributed crowd of data processing experts ready to begin working on your project. We are constantly sourcing and onboarding data processing experts from around the globe. We qualify new workers by having them complete predefined tasks. The platform uses 150+ quality assurance measures to curate our workforce. Here’s how we tap the right crowd for the job and guarantee results.
Super.AI platform requires clear and concise instructions for a given task to support the rapid onboarding of workers for a new AI app. You can create the instructions on your own or work closely with our team to create them. Written instructions are analyzed for clarity using AI, and feedback is provided to ensure seamless execution. The instructions are pre-defined for out-of-the-box applications available in our marketplace.
Workers must pass general skills tests that evaluate abilities such as reading comprehension, adherence to instructions, and more. Most importantly, tests that use your specific data are administered during qualification.
Tasks with known answers (ground truth) are sent to workers at random to ensure output quality is consistent. Inconsistent workers are checked more frequently, while consistent workers are checked less frequently.
Same tasks are sent to workers with a slight variation to identify temporary drops in performance due to fatigue or distraction.
If an anomaly, such as a much longer/shorter than usual processing time, is detected during task processing, an internal audit team is notified. Experts continuously investigate and resolve any issues.
Tasks below a user-defined accuracy threshold can be routed to experts (in-house or third-party) for review. Our platform is designed to minimize the use of an expert review panel as much as possible.
To create an incentive to resolve tasks accurately and develop their skills, workers are compensated based on the value they provide to you. High-performing workers receive more responsibility and compensation over time.
The limiting factor in scaling unstructured data processing and IDP is human-in-the-loop capability. Leveraging in-house resources or outsourcing to legacy BPO companies add cost and complexity. Modern UDP/IDP solutions providers like super.AI are leveraging the data processing crowd to provide AI + human + software solutions that can rapidly process 100% of the data at a much lower blended cost while strictly adhering to SLAs.
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