Achieve better than human extraction quality on very large number of documents at a fraction of the time

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We help the financial industry

Streamline Improve accuracy Deploy securely
Streamline Improve accuracy Deploy securely
Streamline, automate and scale existing manual processes to save efforts & money. Improve data quality and reduce errors in your downstream systems and databases. Leverage the only machine learning-driven extraction engine than can be deployed either on-prem or on the cloud.

How SuccessData works

SuccessData automates complex screening and analysis processes by extracting relevant data points and producing a predefined structured data output, replacing tasks that would otherwise necessitate tremendous human effort.

What makes SuccessData special?

SuccessData uses sophisticated machine learning to automatically turn “dark” data (unstructured data buried in text, tables or figures which by definition cannot be processed by existing software or analytics platforms) from documents such as legal and commercial contracts, regulatory filings, web pages, news articles or annual reports into machine readable datasets.

Get actionable results quicker

With no hand-labelling needed and models that train on much smaller datasets than traditional techniques, you can achieve operational excellence within days or weeks instead of months.

Get better precision

SuccessData achieves very high levels of accuracy even for difficult or ambiguous extractions with crucially low levels of false positives.

Get more than data

SuccessData’s unique model retrieves not only simple fields but also complex relationships within documents whether from text, tables or images and provides a confidence level for each data point extracted.

Plug and play

SuccessData exposes a set of REST APIs to facilitate the deployment within complex workflows.

Why your AI project with SuccessData won't fail

It is widely recognised that 80% of the work with an AI project is collecting and preparing data. Most companies don't have either the budget or the resources associated with that going in. Good news: we don't need any hand-label training data!