We take the complexity of business intelligence out of your daily workflow

As the world’s demand for data grows, we use more people and processing to QA, structure, and model that data, because traditional AI still requires human input and massive data sets to establish the basis on which machines learn.

Our AI precisely and accurately structures data in an unsupervised fashion, reducing our reliance on costly human resources to generate business intelligence from raw data, even as the demand for insights evolves and increases over time.

As the ecosystem of data expands, so will the risk for errors and gaps in that data. Traditional engineering practices “clean” the data to improve their results. But we live in an imperfect world filled with imperfect, open data.

Our technology automates the QA process and models the real impact of errors in data, rather than ignore that data altogether. This minimizes potential for human correction bias, allows us to use smaller data sets, and lowers overall costs.

As digital technologies enter every corner of our lives, the network of systems that talk to each other without human intervention will continue to grow. Raw data will need to be structured for computers, faster and with higher quality than ever before.

We use innovative models to structure data in a format that helps computers talk to computers, recognising and modeling errors that would otherwise need human intervention to correct, and processing data faster than current manual approaches.

As data volumes grow exponentially, the data lifecycle will become more fragmented and specialised, with an increasingly complex network of costly relationships to manage, and sophisticated decisions between intricate technologies to make.

We sit across the entire data pipeline, understand our clients’ data needs, engage the right partners for the right data, and deliver subscription-based data and insights that solve tangible business problems more cost-effectively and efficiently than clients would do on their own.