ElinarAI™ for automate manual processes
Elinar has been heavily involved with advanced topics in text analytics for over a decade. We have also extensive expertise in text analytics and inherent problems associated with linguistics, dictionaries, and regular expressions. These things, to name only a few, have enabled us to take unstructured data analytics to the next level. In summary, we automate manual processes to help you to get better results in your business.
ElinarAI™ helps you to get better results
We can automate manual processes in many different fields and in several different cases. Here are some examples:
- Financial Documents (for example, Orders, Invoices, Cash Allocation/Remittance Advice)
- Medical Records Read more…
- Privacy (GDPR) Read more…
- Legal Read more…
- Investigators Read more…
Elinar has a solid background in augmenting IBM Watson NLU®, IBM Watson Explorer®, IBM StoredIQ®, and IBM Datacap®. These utilize AI to provide AI-augmented enterprise content analytics and management solutions. Our own R&D on unstructured data AI started 5 years ago. This work ultimately resulted in significant Elinar IP: ElinarAI™
ElinarAI™’s core capabilities are:
Complex feature extraction from unstructured data. For example, ElinarAI™ can:
- Extract order or invoice lines.
- Identify damaged parts and the reason for damage from field service men reports.
- Identify the reason for a customer’s anger based on a customer complaint.
- Create a structure in a medical record. (For example, identify the reason for a doctor’s visit and identify which medication was ordered.)
- Extract complex personal data from a document.
- Help with redaction tasks. ElinarAI™ can discover what needs to be redacted from a record.
Advanced classification of a document or business record.
As such, ElinarAI™ is able to:
- Learn and create virtually any type of classification, for example, security classification.
- Use multi-valued complex classifications, such as which body part was injured.
- Mix complex classification systems together. For example, there can be dozens of classification categories (e.g. MRI, X-ray, specialist appointment, GP appointment, nurse appointment, injured body part, and so on).
ElinarAI™ uses supervised learning, so it learns from samples. These can either be obtained automatically from an existing business process/system or humans can generate them manually.
ElinarAI™ contains all the necessary tools for training data preparation, AI training, and scalable inferencing. Our solution is not a framework that requires deep involvement from corporate IT developers. Instead, it allows the business to create AI training data in an intuitive environment. And once the training data has been created (or provided), ElinarAI™ provides inferencing using the simple but secure REST interface. It fits with any corporate process.
What is ElinarAI™?
ElinarAI™ is an AI framework that combines Deep Learning with Advanced Text Analytics using ElinarNER or IBM Watson NLU®. Elinar has specialized industry modules available for several industries and use-cases.
We can automate manual processes in many different fields and in several different cases.
AI automation for Big Data and Analytics
One major challenge of unstructured data is that it is very difficult to use as part of corporate analytics due to the unstructured nature.
Unfortunately, nowadays 70% of corporate data is unstructured and this leaves huge gaps into corporate analytics capabilities. For some (simple) use cases traditional Text Analytics offers excellent approaches. Anyhow, in the long run, text analytics is very simple and requires constant maintenance for even mediocre results. When the data is “not apparent” in the text it becomes very expensive or impossible to create understanding using traditional text analytics.
This is why ElinarAI™ combines text analytics with Deep Learning: to create human-like cognition on unstructured data. This allows corporate analytics to tap into a vast, near-endless vat of knowledge called unstructured data. Business documents, records, consumer complaints – nearly anything can be part of corporate analytics!