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Tanso nabs $1.9M pre-seed to help industrial manufacturers do sustainability reporting

The climate crisis is creating massive demand for data capture as industries grapple with how to decarbonize. Put simply, you can’t cut your carbon emissions if don’t know what they are in the first place.

This need to gather data is a big opportunity for startups — and a wave of early companies have already been founded to try to plug the sustainability data gap, through things like APIs to assess emissions for carbon offsetting (which in turn has led to other startups trying to tackle the data gap around offsetting projects).

One thing is clear: Requirements for sustainability reporting are only going to get broader and deeper from here on in.

Munich-based Tanso is an early-stage startup (founded this year) that’s building software to support sustainability reporting for a particular sector (industrial manufacturers) — with the goal of creating a data management system that can automate data capture and sustainability reporting geared toward the specific needs of the sector.

The startup says it decided to focus on industrial manufacturing because it’s both an emissions-heavy sector and underserved with supportive digital tech versus many other industries.

The founders met during their studies at universities in Munich and Zurich — where they’d been researching the assessment of organizational climate impact. Their collective expertise crystalized into the realization of a business opportunity to build a data management system for a notoriously polluting sector that’s facing a mandate to change.

In the coming years, European regulations will expand sustainability reporting requirements — with the EU’s “Green Deal” plan setting an overarching goal of Europe becoming the first “climate-neutral” continent by 2050.

Specific (existing) reporting requirements within the bloc include the EU Corporate Sustainability Reporting Directive (CSRD), which will apply to more than 50,000 companies — requiring they report on their sustainability metrics, starting in 2023.

The U.K. (now outside the EU) already introduced some reporting requirements for domestic companies, under the Streamlined Energy and Carbon Reporting (SECR) regulation, which has applied since 2019 and applies to over 12,000 businesses in the U.K. in varying degrees of detail depending on the size of the company.

So there is a clear direction of travel in the region requiring businesses to gather and report sustainability data.

Tanso has just closed a $1.9 million pre-seed raise with the aim of getting its data management support software to market in time for an expected surge in demand as sustainability regulations like CSRD start to bite.

The raise is led by German early-stage B2B fund UVC Partners, with participation from Picus Capital, Possible Ventures and a number of business angels.

Tanso is still in the R&D/product development phase, with co-founder Gyri Reiersen telling TechCrunch it’s currently working with a number of manufacturers to “figure out the sweet spot” for automating data gathering so it can come to market with a scalable product offering. She says the team raised a relatively large pre-seed exactly to see it through until it’s got something fit to launch (it’s hoping to have something “solid, verified and scalable” by the end of 2022, per Reiersen).

The goal for the product is a single platform that gathers and holds all the customer’s sustainability data and can automate the generation of reports to meet regulatory requirements — including auditing.

From 2025, Reiersen points out that CSRD reporting needs to be “auditable,” meaning that you have to have “some form of transparency and traceability”; and also that the “correctness” of sustainability reporting will be a C-suite responsibility. So that must concentrate boardroom minds.

“Going beyond that it’s all about how can you use this data and the insights that the data gives you to make predictions and models going forward for how should we develop our products? What makes sense to do going forward to make?” she adds.

“What we’re prototyping currently is to streamline the workflow of information gathering,” Reiersen also tells us, discussing the product dev process. “Also to have really good, fundamental user flow for the users to use our product. And then doing the deep dives on integrations over time.”

She says the challenge is finding the trade-off between usability and “digging into the data.” “For us it’s very important to have a scalable product, especially having it fully scalable from 2023 when the CSRD are started because then there will be desperation on the market. Companies will need to have something,” she adds.

“We need to have these solutions … that take one step in the right direction for all companies and not just have a couple of carbon neutral companies … So for us it’s more about finding the productizable use cases in the beginning to make this a scalable product.”

But she also warns over a proliferation of overly “shallow” offerings in the space — driven by marketing-led ‘greenwashing’ (and bogus carbon offsetting) rather than a genuine desire to correctly identify the problem and course-correct, which is what’s actually needed for humanity to avert climate disaster.

Reiersen adds that she got really interested in this space through her university work researching the overestimation of carbon offsets through deep learning.

“There is such a need for accountability and making sure that the products that are being developed actually do their job correctly. Because it’s so easy to just have a black box and trust it. We can’t afford having systems that overestimate or underestimate. It needs to be accurate and it needs to be validated,” she says.

“Going forward, accuracy will mean more and more and then you need to access the ‘real data’ and not just ‘guestimations,’” she predicts. “And that’s where we see that of course we need to be very front-end/UX-friendly, and making it easy for people to enter the right data and have a very user-friendly, usable product and that people are guided through the process of gathering the right data … but also over time really focusing on how do you integrate and get access to the data at the database level?”

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Hypatos gets $11.8M for a deep learning approach to document processing

Process automation startup Hypatos has raised a €10 million (~$11.8 million) seed round of funding from investors including Blackfin Tech, Grazia Equity, UVC Partners and Plug & Play Ventures.

The Germany and Poland-based company was spun out of AI for accounting startup Smacc at the back end of 2018 to apply deep learning tech to power a wider range of back-office automation, with a focus on industries with heavy financial document processing needs, such as the financial and insurance sectors.

Hypatos is applying language processing AI and computer vision tech to speed up financial document processing for business use cases such as invoices, travel and expense management, loan application validation and insurance claims handling via — touting a training data set of more than 10 million annotated data entities.

It says the new seed funding will go on R&D to expand its portfolio of AI models so it can automate business processing for more types of documents, as well as for fueling growth in Europe, North American and Asia. Its customer base at this point includes Fortune 500 companies, major accounting firms and more than 300 software companies.

While there are plenty of business process automation plays, Hypatos says its use of deep learning tech supports an “in-depth understanding” of document content — which in turn allows it to offer customers a “soup to nuts” automation menu that covers document classification, information capturing, content validation and data enrichment.

It dubs its approach “cognitive process automation” (CPA) versus more basic applications of business process automation with software robots (RPA), which it argues aren’t so contextually savvy — thereby claiming an edge.

As well as document processing solutions, it has developed machine learning modules for enhancing customers’ existing systems (e.g. ECM, ERP, CRM, RPA); and offers APIs for software providers to draw on its machine learning tech for their own applications.

“All offerings include machine learning pipeline software for continuous model training in the cloud or in on-premise deployments,” it notes in a press release.

“We have deep knowledge of how financial documents are processed and millions of data entities in our training data,” says chief commercial officer Cem Dilmegani, discussing where Hypatos fits in the business process automation landscape. “We get compared to RPA companies like UiPath, enterprise content management (ECM) companies like Kofax Readsoft as well as generalist ML document automation companies like Hyperscience. However, we are quite different.

“We focus on end-to-end automation, we don’t only help companies capture data, we help them process it using our deep domain understanding, enabling higher rates of automation. For example, to automate incoming invoice processing (A/P automation) we apply our document understanding AI to capture all data, classify the document, identify the specific goods and services, validate for internal/external compliance and assign financial accounts, cost centers, cost categories etc. to automate all processing tasks.

“Finally, we offer this technology as components easily accessible via APIs. This allows RPA or ECM users to leverage our technology and increase their level of automation.”

Hypatos claims it’s seeing uplift as a result of the coronavirus pandemic — noting it’s providing a service to more than a dozen Fortune 500 companies to help with in-shoring efforts, which it says are accelerating as a result of COVID-19 putting pressure on the traditional business process outsourcing model as offshore workforce productivity in lower wage regions is affected by coronavirus lockdowns.

“We believe that we are in a pivotal moment of machine learning adoption in large organizations,” adds Andreas Unseld, partner at UVC Partners, in a supporting statement. “Hypatos’ technology provides ample opportunity to transform many core business processes. We’re impressed by the Hypatos machine learning technology and see the team in a perfect position to take a leading role in the machine learning revolution to come.”

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