Tired of manual data entry? AI-enabled data extraction is here
Are you tired of spending your time manually sifting through emails and filling in forms for employee onboarding, invoice processing, insurance policies, and other critical service tasks?
Well, your prayers have been answered because we’ve built an AI-enabled solution that can automatically extract data from emails and auto-populate forms. Our progressive solution slashes the time spent on monotonous tasks such as data entry, and gives you more bandwidth to spend on value-added work.
How does AI data extraction actually work?
AI data extraction or entity recognition works by using Natural Language Understanding patterns to recognise key ‘features’ or entities within a block of text. For example, an email. Typically data extraction is conducted with the use of a large trained model and has been great for finding things like: proper names, locations, dates and times etc, but has been less accurate when locating more domain specific terms and entities. For instance, within a medical or legal email there may be certain domain specific entities you want to understand, but the implemented model is too general. This is where EnateAI steps in, implementing LLMs (Large Language Models) alongside our workflow orchestration platforms means that we are able to quickly map entities within your data to the Enate platform, saving critical human time reading and processing email.
Example use case: Processing claims in the insurance industry and being able to extract the claimants name, their claim number and the content of the claim directly from a submission enables their claim to be processed faster.
The benefits of AI-enabled email data extraction
Any business dealing with large volumes of emails and service requests stands to benefit from EnateAI. Our AI-enabled solution intelligently scans your emails, extracting relevant data, and populating the required service forms seamlessly.
- Reduction in manual work
- Heightened efficiency
- Improved accuracy
- Reduced risk of human error
- Streamlined workflows
- More time to focus on customer success
Historically, entity and feature extraction hasn’t cut the mustard
To date, entity or feature recognition has been constrained by the data available for training the underlying model. Typically, these models have been owned by large cloud platform providers and offered as a general service to consumers. Whilst this meant that the traditional cost of a lengthy machine learning (MLOps) pipeline had been removed (collecting data, cleaning data, training, testing etc), the models have often been highly general. Previously, challenges arose in accurately identifying specific domain language and entities, resulting in variable accuracy. This often prolonged development cycles for consuming services or lengthy projects that might not lead to entity recognition adoption.
EnateAI changes that
With EnateAI's integration with LLMs, powered by extensive data trained across diverse domains and industry-specific language, the need to repeatedly enhance or expand services for new feature extraction is eliminated. Trained on extensive datasets across diverse domains such as legal, medical, and insurance, LLMs possess the capability to comprehend various natural expressions. EnateAI automates the process, seamlessly aligning with your defined data in the Enate platform.
EnateAI - 5 AI features in one
Email data extraction is just one of 5 fantastic EnateAI features. EnateAI has been specifically built to elevate operational efficiency and can effortlessly categorize emails, perform sentiment analysis, comprehend foreign languages and automate queries.
Embrace a smarter and more effective way of working, empowering your service team to achieve more in less time, and ultimately, enhancing overall customer satisfaction.
AI in Operations
Operational Soup is a term we use when work is being carried out, but businesses have little idea how much, by whom or exactly how it is processed.
Start orchestration in departments with strong use-cases to deliver value quickly. Often, good examples can be found in back/middle office process areas that have high variation and complexity such as finance or HR operations. Recent intelligence sourced through process mining suggests 80%+ of the work performed in a shared services organization is not performed in the ERP systems, but rather in Excel or Outlook. This is where Orchestration thrives.
Having orchestration implemented across our departments can be likened to having x-ray vision into your operations.
Global Head of Operations at TMF
Almost half of banking and investment CIOs (49%) and insurance CIOs (44%) indicated that they will increase their automation investments in 2021.
Source: Gartner, 2021