Dec
10
2020
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Boast.ai raises $23M to help businesses get their R&D tax credits

Nobody likes dealing with taxes — until the system works in your favor. In many countries, startups can receive tax credits for their R&D work and related employee cost, but as with all things bureaucracy, that’s often a slow and onerous task. Boast.ai aims to make this process far easier, by using a mix of AI and tax experts. The company, which currently has about 1,000 customers, today announced that it has raised a $23 million Series A round led by Radian Capital.

Launched in 2012 by co-founders Alex Popa (CEO) and Lloyed Lobo (president), Boast focuses on helping companies — and especially startups — in the U.S. and Canada claim their R&D tax credits.

“Globally, over $200 billion has been given in R&D incentives to fund businesses, not only in the U.S. and Canada, but the U.K., Australia, France, New Zealand, Ireland give out these incentives,” Lobo explained. “But there’s huge red tape. It’s a cumbersome process. You got to dive in and figure out work that qualifies and what doesn’t. Then you’ve got to file it with your taxes. Then if the government audits you, it’s like a long, laborious process.”

Image Credits: Boast.ai

After working on a few other startup ideas, the co-founders decided to go all-in on Boast. And in the process of working on other ideas, they also realized that AI wasn’t going to be able to do it all, but that it was getting good enough to augment humans to make a complex process like dealing with R&D tax credits scalable.

“The way I think to bootstrap a company is three things,” Lobo explained. “One, customers are looking for an outcome. Get them that outcome in the fastest, cheapest way possible. Two, when you’re doing that, you may have to do a lot of manual work. Figure out what those manual touch points are and then build the workflow to automate that. And once you have those two things, then you’ll have enough data to start working on artificial intelligence and machine learning. Those are the key learnings that we learned the hard way.”

So after doing some of that manual work, Boast can now automatically pull in data using tech tools like JIRA and GitHub and a company’s financial tools like QuickBooks, Gusto and (soon) ADP. It then uses its algorithms to cluster this data, figure out how much time employees spend on projects that would qualify for a tax credit and automate the tax filing process. Throughout the process — and to interact with the government if necessary — the company keeps humans in the loop.

“So all our [customer success] team is engineers,” Lobo noted. “Because if you don’t have engineers they can’t inform the decision-making process. They help figure out if there are any loose ends and then they deal with the audits, communicating with the government and whatnot. That’s how we’re able to effectively get SaaS-like margins or more.”

Ideally, a tool like Boast pays for itself and the company says it has secured more than $150 million in R&D tax credits since launch. Currently, it’s also doubling growth year over year, and that’s what made the founders decide to raise outside money for the first time. That funding will go toward increasing the sales team (which is currently only four people strong) and improving the platform, but Lobo was clear that he doesn’t want to be too aggressive. The goal, he said, is not to have to raise again until Boast can hit the $30 to $50 million revenue mark.

Once fully implemented, Boast also effectively becomes a system of record for all R&D and engineering data. And indeed, that’s the company’s overall vision, with the tax credits being somewhat of a Trojan horse to get to this point. By the middle of next year, the team plans to offer a new product around R&D-based financing, Lobo tells me.

Over the years, the Boast team also focused on not just growing its customer base but also the overall startup ecosystem in the markets in which it operates, with a special focus on Canada. The Boast team, for example, is also the team behind the popular annual Traction conference in Vancouver, Canada (Disclosure: I’ve moderated sessions at the event since its inception). A thriving startup ecosystem creates a larger client base for Boast, too, after all — and coincidently, the team met its investors at the event, too.

Dec
08
2020
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AWS expands on SageMaker capabilities with end-to-end features for machine learning

Nearly three years after it was first launched, Amazon Web Services’ SageMaker platform has gotten a significant upgrade in the form of new features, making it easier for developers to automate and scale each step of the process to build new automation and machine learning capabilities, the company said.

As machine learning moves into the mainstream, business units across organizations will find applications for automation, and AWS is trying to make the development of those bespoke applications easier for its customers.

“One of the best parts of having such a widely adopted service like SageMaker is that we get lots of customer suggestions which fuel our next set of deliverables,” said AWS vice president of machine learning, Swami Sivasubramanian. “Today, we are announcing a set of tools for Amazon SageMaker that makes it much easier for developers to build end-to-end machine learning pipelines to prepare, build, train, explain, inspect, monitor, debug and run custom machine learning models with greater visibility, explainability and automation at scale.”

Already companies like 3M, ADP, AstraZeneca, Avis, Bayer, Capital One, Cerner, Domino’s Pizza, Fidelity Investments, Lenovo, Lyft, T-Mobile and Thomson Reuters are using SageMaker tools in their own operations, according to AWS.

The company’s new products include Amazon SageMaker Data Wrangler, which the company said was providing a way to normalize data from disparate sources so the data is consistently easy to use. Data Wrangler can also ease the process of grouping disparate data sources into features to highlight certain types of data. The Data Wrangler tool contains more than 300 built-in data transformers that can help customers normalize, transform and combine features without having to write any code.

Amazon also unveiled the Feature Store, which allows customers to create repositories that make it easier to store, update, retrieve and share machine learning features for training and inference.

Another new tool that Amazon Web Services touted was Pipelines, its workflow management and automation toolkit. The Pipelines tech is designed to provide orchestration and automation features not dissimilar from traditional programming. Using pipelines, developers can define each step of an end-to-end machine learning workflow, the company said in a statement. Developers can use the tools to re-run an end-to-end workflow from SageMaker Studio using the same settings to get the same model every time, or they can re-run the workflow with new data to update their models.

To address the longstanding issues with data bias in artificial intelligence and machine learning models, Amazon launched SageMaker Clarify. First announced today, this tool allegedly provides bias detection across the machine learning workflow, so developers can build with an eye toward better transparency on how models were set up. There are open-source tools that can do these tests, Amazon acknowledged, but the tools are manual and require a lot of lifting from developers, according to the company.

Other products designed to simplify the machine learning application development process include SageMaker Debugger, which enables developers to train models faster by monitoring system resource utilization and alerting developers to potential bottlenecks; Distributed Training, which makes it possible to train large, complex, deep learning models faster than current approaches by automatically splitting data across multiple GPUs to accelerate training times; and SageMaker Edge Manager, a machine learning model management tool for edge devices, which allows developers to optimize, secure, monitor and manage models deployed on fleets of edge devices.

Last but not least, Amazon unveiled SageMaker JumpStart, which provides developers with a searchable interface to find algorithms and sample notebooks so they can get started on their machine learning journey. The company said it would give developers new to machine learning the option to select several pre-built machine learning solutions and deploy them into SageMaker environments.

Jan
22
2018
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ADP acquires workforce management software startup WorkMarket

 Payroll provider ADP said it is acquiring WorkMarket, a startup that specializes in workforce management software that operates across a wide range of employees and contractors, for an undisclosed sum. The software aims to create a kind of unified interface for managing an extended workforce that can include a variety of workers with different employment status, from contractors and… Read More

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