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Alkymi launches with $5M seed to automate email data extraction

Alkymi, an early stage startup that wants to bring intelligence to highly manual business processes like copying and pasting financial data from emails and attachments, launched today with a $5 million seed investment. Canaan Partners led the round with participation from previous investor Work-Bench. SimpCorp also contributed as a strategic investor. Under the terms of…

Alkymi, an early stage startup that wants to bring intelligence to highly manual business processes like copying and pasting financial data from emails and attachments, launched today with a $5 million seed investment.

Canaan Partners led the round with participation from previous investor Work-Bench. SimpCorp also contributed as a strategic investor. Under the terms of the investment agreement, Joydeep Bhattacharyya from Canaan will become a member of the Alkymi board.

Company founder and CEO Harald Collet says the startup is bringing machine learning to the business analyst’s in-box with the goal of automating many of the tedious manual parts of the job. The company has created a solution that extracts data automatically that these analysts previously had to copy and paste into applications, spreadsheets or databases.

“What we do is we focus on automating tasks in emails and documents and really focusing on helping business analysts in [automating] those tasks where they have been taking and picking out of business data customer and financial data that’s being fed into business processes,” Collet told TechCrunch.

For today, that strictly involves financial services, which is an industry Collet has worked in for two decades, and which could benefit greatly from this approach. He uses an investment asset manager as an example. This person would receive emails with data in them about investments, copy and paste the data into an application or database, and repeat this many times to report on overall investment performance. Alkymi would automatically extract some amount of this data, reducing the overall manual copying and pasting required.

It takes some time to train the underlying machine model, from hours to days, depending on the size and complexity of the operation, but once that’s done, Collet says the software can deal with what it knows, setting aside what it can’t figure out for a human to intervene, and then learn from that in a typical machine learning loop. Over time, it should allow business analysts to do more analysis, instead of spending time on data entry to get to the analysis part. For now, they are looking at rates of 40-50% understanding, or more for less complex data sets.

While the company is concentrating on financial services today, the long-term plan is to expand into other verticals over time. For now, it is growing quickly with paying financial services customers. It has also partnered with investor SimpCorp, which will offer the service on its platform aimed at financial services professionals.

The company launched in 2017, and Collet spent time talking to potential customers before building the product. It offers an on-prem and cloud version, and bills by the workflow. Today, it has 7 employees based in New York City with plans to double that this year.

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