Selections #29 – Adarga, Avora, Pecan, Plotly

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Adarga – Enhancing human ingenuity

UK-based, 2016 founded, £7.5 raised, 50 – 100 employees, working with BAE Systems, Lockheed Martin, UK Government

Where’s the good stuff?

Founder and CEO, Robert Basset Cross, is a former army officer having served tours of duty in both Iraq and Afghanistan, where he was awarded the military cross. Adarga raised their £7.5 million series A round in July of 2019, lead by Allectus Capital with follow on investment from Moore Strategic Ventures.

What do they do?

Decisions within any modern company are largely dependent on data. The problem that many companies face is the ability to process the huge amounts of data that they receive. Using Adarga’s software, companies can increase their data ingestion and analysation to speeds humans are unable to achieve. 

Adarga offers two products; adarga_engine which is designed for corporate clients who need an AI analytics product to work alongside existing technology, and adarga_bench a workflow tool used by analysts to look deeper into the insights given by adarga_engine. Both of Adarga’s products are used to understand vast amounts of complex data to highlight threats, relationships, and opportunities. 

Avora – Explain why in seconds, not days

US-based, 2014 founded, $9.7m raised, 50 – 100 employees, working with 02, NPR, Vevo

Where’s the good stuff?

Avora’s Founder and CEO, Ricky Thomas, was named as 2019’s Deep Tech Entrepreneur by the Enterprise Awards. They’re success is further highlighted by Deloitte naming Avora as the 42nd fastest-growing technology company in the UK in 2019. 

What do they do?

Avora aggregates and analyses all of a company’s data sources to uncover the factors changing business metrics. They help their clients discover the unknown variable within their data to enhance customer journey, conversion rates, lower operational costs and discover previously overlooked profitability. 

Avora’s automated analysis and visualisations allow its users to monitor trends, unusual behaviours and creates a 30 day forecast based on predefined metrics. An important feature of Avora is their use of Explainable AI within its root cause analysis algorithm. Their use of Explainable AI gives context to the outcome allowing decisions to be made faster. 

Plotly – The front-end for ML and data science models

Canada-based, 2015 founded, $13.3m raised, 50 – 100 employees, working with BASF, Cisco, Invesco

Where’s the good stuff?

Plotly was awarded the top place to work at the Canadian SME Business Awards and even nominated as the Business of the Year. They’ve also been recently awarded a $1.7 million grant from Scale AI to help develop the supply chain side of their product offerings. 

What do they do?

Plotly helps customers create and deploy interactive graphs, web apps and visualisations in any programming language. Plotly enables its clients to run advanced analytics on Machine Learning, Natural Language Processing, forecasting and computer vision – whilst giving data science teams the autonomy to deploy applications at will. 

To do all of this, Plotly created Dash, giving everyone in an organisation direct access to interact with models and data without the need to understand the underlying code. Dash focusses on easy sharing and deployability to remove data silos across different business units. Further, when building the front UI of the interactive apps, data science teams can choose to start from scratch or from many pre-built templates. 

Pecan – The fastest way from raw data to AI, risk-free

Israel-based, 2016 founded, $15.0m raised, 25 – 50 employees, used by Publicis Groupe, Volkswagen Group, Siemens

Where’s the good stuff?

Closed out their Series A Round in January of this year to the tune of $11 million, led by CVC Dell Capital with follow on investment by S Capital. The success of this round is highlighted by being selected to join Microsofts AI for Good Accelerator in Israel. 

What do they do?

Pecan automates the entire predictive analytics process, dramatically reducing ‘time-to-model’ from months to days. Their platform simplifies and speeds up the process of building and deploying predictive models in various use-cases (e.g. churn, lifetime value, lead scoring, next-best-offer and fraud) by connecting directly to raw data, and using neural networks to automate the entire predictive process. 

Using Pecan, one private bank was able to block, in real-time, 4,511 out of a total weekly 7,359 fraud attempts. The impact on the bank’s bottom line from this project was estimated at $2.75 million for the test period alone. Compared with the previous fraud detection systems employed by the bank, Pecan increased the fraud detection rate by 50% and minimised both the cost and time required.

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