Data extraction automation using Machine Learning

SuccessData uses the latest advances in machine learning to automatically turn unstructured data buried in text, tables or figures which by definition cannot be processed by existing software or analytics platforms from documents such as invoices or legal and commercial contracts into machine readable datasets. SuccessData's meticulously designed APIs help you automate complex document-processing workflows and achieve operational excellence.

Product

Intelligent Process Automation

Intelligent Process Automation (IPA) refers to the application of cognitive techniques based on Artificial Intelligence and Machine Learning in general to complement more traditional Robotic Process Automation (RPA). Those techniques provide automation capabilities that go beyond the more simple, routine and stable processes currently streamlined by RPA solutions today and create genuine additional business value for clients. SuccessData is at the forefront of innovation in the IPA space.

Extract complex relationships

In contrast to more traditional approaches focused on text only, SuccessData understands relations conveyed jointly via textual, structural, tabular, and even visual expressions by using new deep-learning techniques to automatically capture the representation (in other words the features) needed to learn how to extract those relationships from richly formatted data. We turn domain expertise and document understanding based on multiple modalities of information, first into meaningful signals of supervision, and then finally into predictive extraction results.

No hand-labelling

SuccessData uses data programming (also called code-as-supervision) to build training sets programmatically using heuristic functions – this completely mitigates the key pain point for most ML implementations. We therefore need up to 100x less training data than other traditional supervised machine learning solutions.

Get more than raw data

SuccessData’s unique model retrieves not only predefined data points but also contextual information on the data extracted such as where it was found in the original document and a confidence level for each data point extracted.

Integrate your reference data

SuccessData exposes a set of APIs to facilitate the integration of your own reference data so that the output data can be enriched, cross-referenced and/or reconciled.

How does SuccessData create a new extraction model?

Define the specific data points (name, date, entity, tables, etc.) that you need to retrieve

Train a Machine Learning model on a subset of the documents (text, PDFs, articles, web pages, etc.)

Once the ML is ready and deployed, send the documents to our hosted infrastructure or process the documents locally

Retrieve a JSON/XML result file containing the extracted data points in a structured form via an API call

Behind the scene

Using a traditional supervised learning approach of machine learning, input data fed to a machine learning system has to be hand-labeled by subject-matter experts. The human-crafted labels help the machine learn to interpret and classify data, however the cost of labeling training sets has become very significant if not prohibitive in some cases, the task is extremely time-consuming for humans meaning that weeks or months have to be spent working on this, and as applications and use cases shift, training sets relevance depreciates. SuccessData instead lets a team of subject matter experts write functions that automatically assign labels to datasets.

A generative neural network then compares which labels multiple functions generate for the same data, resulting in probabilities being assigned as to which labels may be true. That data and its probabilistic labels are then used to train a predictive model, instead of using hand-labeled data. The approach is known as “weak supervision” in contrast to more traditional supervised machine learning techniques.

Discover how SuccessData automates your manual processes and integrates seamlessly with your existing workflows

SuccessData abstracts away the complexity of the actual extraction process, offering a scalable infrastructure, providing speed and lower costs.