Digital transformation has been one of the biggest catchphrases of the past year, with many an organization forced to reckon with aging IT, a lack of digital strategy, or simply the challenges of growth after being faced with newly-remote workforces, customers doing everything online and other tech demands.

Now, a startup called Upstack that has built a platform to help those businesses evaluate how to grapple with those next steps — including planning and costing out different options and scenarios, and then ultimately buying solutions — is announcing financing to do some growth of its own.

The New York startup has picked up funding of $50 million, money that it will be using to continue building out its platform and expanding its services business.

The funding is coming from Berkshire Partners, and it’s being described as an “initial investment”. The firm, which makes private equity and late-stage growth investments, typically puts between $100 million and $1 billion in its portfolio companies so this could end up as a bigger number, especially when you consider the size of the market that Upstack is tackling: the cloud and internet infrastructure brokerage industry generates annual revenues “in excess of $70 billion,” the company estimates.

We’re asking about the valuation, but PitchBook notes that the median valuation in its deals is around $211 million. Upstack had previously raised around $35 million.

Upstack today already provides tools to large enterprises, government organizations, and smaller businesses to compare offerings and plan out pricing for different scenarios covering a range of IT areas, including private, public and hybrid cloud deployments; data center investments; network connectivity; business continuity and mobile services, and the plan is to bring in more categories to the mix, including unified communications and security.

Notably, Upstack itself is profitable and names a lot of customers that themselves are tech companies — they include Cisco, Accenture, cloud storage company Backblaze, Riverbed and Lumen — a mark of how digital transformation and planning for it are not necessarily a core competency even of digital businesses, but especially those that are not technology companies. It says it has helped complete over 3,700 IT projects across 1,000 engagements to date.

“Upstack was founded to bring enterprise-grade advisory services to businesses of all sizes,” said Christopher Trapp, founder and CEO, in a statement. “Berkshire’s expertise in the data center, connectivity and managed services sectors aligns well with our commitment to enabling and empowering a world-class ecosystem of technology solutions advisors with a platform that delivers higher value to their customers.”

The core of the Upstack’s proposition is a platform that system integrators, or advisors, plus end users themselves, can use to design and compare pricing for different services and solutions. This is an unsung but critical aspect of the ecosystem: We love to hear and write about all the interesting enterprise technology that is being developed, but the truth of the matter is that buying and using that tech is never just a simple click on a “buy” button.

Even for smaller organizations, buying tech can be a hugely time-consuming task. It involves evaluating different companies and what they have to offer — which can differ widely in the same category, and gets more complex when you start to compare different technological approaches to the same problem.

It also includes the task of designing solutions to fit one’s particular network. And finally, there are the calculations that need to be made to determine the real cost of services once implemented in an organization. It also gives users the ability to present their work, which also forms a critical part of the evaluating and decision-making process. When you think about all of this, it’s no wonder that so many organizations have opted to follow the “if it ain’t broke, don’t fix it” school of digital strategy.

As technology has evolved, the concept of digital transformation itself has become more complicated, making tools like Upstack’s more in demand both by companies and the people they hire to do this work for them. Upstack also employs a group of about 15 advisors — consultants — who also provide insight and guidance in the procurement process, and it seems some of the funding will also be used to invest in expanding that team.

(Incidentally, the model of balancing technology with human experts is one used by other enterprise startups that are built around the premise of helping businesses procure technology: BlueVoyant, a security startup that has built a platform to help businesses manage and use different security services, also retains advisors who are experts in that field.)

The advisors are part of the business model: Upstack’s customers can either pay Upstack a consulting fee to work with its advisors, or Upstack receives a commission from suppliers that a company ends up using, having evaluated and selected them via the Upstack platform.

The company competes with traditional systems integrators and consultants, but it seems that the fact that it has built a tech platform that some of its competitors also use is one reason why it’s caught the eye of investors, and also seen strong growth.

Indeed, when you consider the breadth of services that a company might use within their infrastructure — whether it’s software to run sales or marketing, or AI to run a recommendation for products on a site, or business intelligence or RPA — it will be interesting to see how and if Upstack considers deeper moves into these areas.

“Upstack has quickly become a leader in a large, rapidly growing and highly fragmented market,” said Josh Johnson, principal at Berkshire Partners, in a statement. “Our experience has reinforced the importance of the agent channel to enterprises designing and procuring digital infrastructure. Upstack’s platform accelerates this digital transformation by helping its advisors better serve their enterprise customers. We look forward to supporting Upstack’s continued growth through M&A and further investment in the platform.”

You hear so much about data these days that you might forget that a huge amount of the world runs on documents: a veritable menagerie of heterogeneous files and formats holding enormous value yet incompatible with the new era of clean, structured databases. Docugami plans to change that with a system that intuitively understands any set of documents and intelligently indexes their contents — and NASA is already on board.

If Docugami’s product works as planned, anyone will be able to take piles of documents accumulated over the years and near-instantly convert them to the kind of data that’s actually useful to people.

Because it turns out that running just about any business ends up producing a ton of documents. Contracts and briefs in legal work, leases and agreements in real estate, proposals and releases in marketing, medical charts, etc, etc. Not to mention the various formats: Word docs, PDFs, scans of paper printouts of PDFs exported from Word docs, and so on.

Over the last decade there’s been an effort to corral this problem, but movement has largely been on the organizational side: put all your documents in one place, share and edit them collaboratively. Understanding the document itself has pretty much been left to the people who handle them, and for good reason — understanding documents is hard!

Think of a rental contract. We humans understand when the renter is named as Jill Jackson, that later on, “the renter” also refers to that person. Furthermore, in any of a hundred other contracts, we understand that the renters in those documents are the same type of person or concept in the context of the document, but not the same actual person. These are surprisingly difficult concepts for machine learning and natural language understanding systems to grasp and apply. Yet if they could be mastered, an enormous amount of useful information could be extracted from the millions of documents squirreled away around the world.

What’s up, .docx?

Docugami founder Jean Paoli says they’ve cracked the problem wide open, and while it’s a major claim, he’s one of few people who could credibly make it. Paoli was a major figure at Microsoft for decades, and among other things helped create the XML format — you know all those files that end in x, like .docx and .xlsx? Paoli is at least partly to thank for them.

“Data and documents aren’t the same thing,” he told me. “There’s a thing you understand, called documents, and there’s something that computers understand, called data. Why are they not the same thing? So my first job [at Microsoft] was to create a format that can represent documents as data. I created XML with friends in the industry, and Bill accepted it.” (Yes, that Bill.)

The formats became ubiquitous, yet 20 years later the same problem persists, having grown in scale with the digitization of industry after industry. But for Paoli the solution is the same. At the core of XML was the idea that a document should be structured almost like a webpage: boxes within boxes, each clearly defined by metadata — a hierarchical model more easily understood by computers.

“A few years ago I drank the AI kool-aid, got the idea to transform documents into data. I needed an algorithm that navigates the hierarchical model, and they told me that the algorithm you want does not exist,” he explained. “The XML model, where every piece is inside another, and each has a different name to represent the data it contains — that has not been married to the AI model we have today. That’s just a fact. I hoped the AI people would go and jump on it, but it didn’t happen.” (“I was busy doing something else,” he added, to excuse himself.)

The lack of compatibility with this new model of computing shouldn’t come as a surprise — every emerging technology carries with it certain assumptions and limitations, and AI has focused on a few other, equally crucial areas like speech understanding and computer vision. The approach taken there doesn’t match the needs of systematically understanding a document.

“Many people think that documents are like cats. You train the AI to look for their eyes, for their tails … documents are not like cats,” he said.

It sounds obvious, but it’s a real limitation. Advanced AI methods like segmentation, scene understanding, multimodal context, and such are all a sort of hyperadvanced cat detection that has moved beyond cats to detect dogs, car types, facial expressions, locations, etc. Documents are too different from one another, or in other ways too similar, for these approaches to do much more than roughly categorize them.

As for language understanding, it’s good in some ways but not in the ways Paoli needed. “They’re working sort of at the English language level,” he said. “They look at the text but they disconnect it from the document where they found it. I love NLP people, half my team is NLP people — but NLP people don’t think about business processes. You need to mix them with XML people, people who understand computer vision, then you start looking at the document at a different level.”

Docugami in action

Paoli’s goal couldn’t be reached by adapting existing tools (beyond mature primitives like optical character recognition), so he assembled his own private AI lab, where a multidisciplinary team has been tinkering away for about two years.

“We did core science, self-funded, in stealth mode, and we sent a bunch of patents to the patent office,” he said. “Then we went to see the VCs, and SignalFire basically volunteered to lead the seed round at $10 million.”

Coverage of the round didn’t really get into the actual experience of using Docugami, but Paoli walked me through the platform with some live documents. I wasn’t given access myself and the company wouldn’t provide screenshots or video, saying it is still working on the integrations and UI, so you’ll have to use your imagination … but if you picture pretty much any enterprise SaaS service, you’re 90% of the way there.

As the user, you upload any number of documents to Docugami, from a couple dozen to hundreds or thousands. These enter a machine understanding workflow that parses the documents, whether they’re scanned PDFs, Word files, or something else, into an XML-esque hierarchical organization unique to the contents.

“Say you’ve got 500 documents, we try to categorize it in document sets, these 30 look the same, those 20 look the same, those five together. We group them with a mix of hints coming from how the document looked, what it’s talking about, what we think people are using it for, etc.,” said Paoli. Other services might be able to tell the difference between a lease and an NDA, but documents are too diverse to slot into pre-trained ideas of categories and expect it to work out. Every set of documents is potentially unique, and so Docugami trains itself anew every time, even for a set of one. “Once we group them, we understand the overall structure and hierarchy of that particular set of documents, because that’s how documents become useful: together.”

That doesn’t just mean it picks up on header text and creates an index, or lets you search for words. The data that is in the document, for example who is paying whom, how much and when, and under what conditions, all that becomes structured and editable within the context of similar documents. (It asks for a little input to double check what it has deduced.)

It can be a little hard to picture, but now just imagine that you want to put together a report on your company’s active loans. All you need to do is highlight the information that’s important to you in an example document — literally, you just click “Jane Roe” and “$20,000” and “five years” anywhere they occur — and then select the other documents you want to pull corresponding information from. A few seconds later you have an ordered spreadsheet with names, amounts, dates, anything you wanted out of that set of documents.

All this data is meant to be portable too, of course — there are integrations planned with various other common pipes and services in business, allowing for automatic reports, alerts if certain conditions are reached, automated creation of templates and standard documents (no more keeping an old one around with underscores where the principals go).

Remember, this is all half an hour after you uploaded them in the first place, no labeling or pre-processing or cleaning required. And the AI isn’t working from some preconceived notion or format of what a lease document looks like. It’s learned all it needs to know from the actual docs you uploaded — how they’re structured, where things like names and dates figure relative to one another, and so on. And it works across verticals and uses an interface anyone can figure out in a few minutes. Whether you’re in healthcare data entry or construction contract management, the tool should make sense.

The web interface where you ingest and create new documents is one of the main tools, while the other lives inside Word. There Docugami acts as a sort of assistant that’s fully aware of every other document of whatever type you’re in, so you can create new ones, fill in standard information, comply with regulations and so on.

Okay, so processing legal documents isn’t exactly the most exciting application of machine learning in the world. But I wouldn’t be writing this (at all, let alone at this length) if I didn’t think this was a big deal. This sort of deep understanding of document types can be found here and there among established industries with standard document types (such as police or medical reports), but have fun waiting until someone trains a bespoke model for your kayak rental service. But small businesses have just as much value locked up in documents as large enterprises — and they can’t afford to hire a team of data scientists. And even the big organizations can’t do it all manually.

NASA’s treasure trove

The problem is extremely difficult, yet to humans seems almost trivial. You or I could glance through 20 similar documents and a list of names and amounts easily, perhaps even in less time than it takes for Docugami to crawl them and train itself.

But AI, after all, is meant to imitate and transcend human capacity, and it’s one thing for an account manager to do monthly reports on 20 contracts — quite another to do a daily report on a thousand. Yet Docugami accomplishes the latter and former equally easily — which is where it fits into both the enterprise system, where scaling this kind of operation is crucial, and to NASA, which is buried under a backlog of documentation from which it hopes to glean clean data and insights.

If there’s one thing NASA’s got a lot of, it’s documents. Its reasonably well-maintained archives go back to its founding, and many important ones are available by various means — I’ve spent many a pleasant hour perusing its cache of historical documents.

But NASA isn’t looking for new insights into Apollo 11. Through its many past and present programs, solicitations, grant programs, budgets, and of course engineering projects, it generates a huge amount of documents — being, after all, very much a part of the federal bureaucracy. And as with any large organization with its paperwork spread over decades, NASA’s document stash represents untapped potential.

Expert opinions, research precursors, engineering solutions, and a dozen more categories of important information are sitting in files searchable perhaps by basic word matching but otherwise unstructured. Wouldn’t it be nice for someone at JPL to get it in their head to look at the evolution of nozzle design, and within a few minutes have a complete and current list of documents on that topic, organized by type, date, author and status? What about the patent advisor who needs to provide a NIAC grant recipient information on prior art — shouldn’t they be able to pull those old patents and applications up with more specificity than any with a given keyword?

The NASA SBIR grant, awarded last summer, isn’t for any specific work, like collecting all the documents of such and such a type from Johnson Space Center or something. It’s an exploratory or investigative agreement, as many of these grants are, and Docugami is working with NASA scientists on the best ways to apply the technology to their archives. (One of the best applications may be to the SBIR and other small business funding programs themselves.)

Another SBIR grant with the NSF differs in that, while at NASA the team is looking into better organizing tons of disparate types of documents with some overlapping information, at NSF they’re aiming to better identify “small data.” “We are looking at the tiny things, the tiny details,” said Paoli. “For instance, if you have a name, is it the lender or the borrower? The doctor or the patient name? When you read a patient record, penicillin is mentioned, is it prescribed or prohibited? If there’s a section called allergies and another called prescriptions, we can make that connection.”

“Maybe it’s because I’m French”

When I pointed out the rather small budgets involved with SBIR grants and how his company couldn’t possibly survive on these, he laughed.

“Oh, we’re not running on grants! This isn’t our business. For me, this is a way to work with scientists, with the best labs in the world,” he said, while noting many more grant projects were in the offing. “Science for me is a fuel. The business model is very simple — a service that you subscribe to, like Docusign or Dropbox.”

The company is only just now beginning its real business operations, having made a few connections with integration partners and testers. But over the next year it will expand its private beta and eventually open it up — though there’s no timeline on that just yet.

“We’re very young. A year ago we were like five, six people, now we went and got this $10 million seed round and boom,” said Paoli. But he’s certain that this is a business that will be not just lucrative but will represent an important change in how companies work.

“People love documents. Maybe it’s because I’m French,” he said, “but I think text and books and writing are critical — that’s just how humans work. We really think people can help machines think better, and machines can help people think better.”

Meroxa, a startup that makes it easier for businesses to build the data pipelines to power both their analytics and operational workflows, today announced that it has raised a $15 million Series A funding round led by Drive Capital. Existing investors Root, Amplify and Hustle Fund also participated in this round, which together with the company’s previously undisclosed $4.2 million seed round now brings total funding in the company to $19.2 million.

The promise of Meroxa is that can use a single platform for their various data needs and won’t need a team of experts to build their infrastructure and then manage it. At its core, Meroxa provides a single Software-as-a-Service solution that connects relational databases to data warehouses and then helps businesses operationalize that data.

“The interesting thing is that we are focusing squarely on relational and NoSQL databases into data warehouse,” Meroxa co-founder and CEO DeVaris Brown told me. “Honestly, people come to us as a real-time FiveTran or real-time data warehouse sink. Because, you know, the industry has moved to this [extract, load, transform] format. But the beautiful part about us is, because we do change data capture, we get that granular data as it happens.” And businesses want this very granular data to be reflected inside of their data warehouses, Brown noted, but he also stressed that Meroxa can expose this stream of data as an API endpoint or point it to a Webhook.

The company is able to do this because its core architecture is somewhat different from other data pipeline and integration services that, at first glance, seem to offer a similar solution. Because of this, users can use the service to connect different tools to their data warehouse but also build real-time tools on top of these data streams.

“We aren’t a point-to-point solution,” Meroxa co-founder and CTO Ali Hamidi explained. “When you set up the connection, you aren’t taking data from Postgres and only putting it into Snowflake. What’s really happening is that it’s going into our intermediate stream. Once it’s in that stream, you can then start hanging off connectors and say, ‘Okay, well, I also want to peek into the stream, I want to transfer my data, I want to filter out some things, I want to put it into S3.”

Because of this, users can use the service to connect different tools to their data warehouse but also build real-time tools to utilize the real-time data stream. With this flexibility, Hamidi noted, a lot of the company’s customers start with a pretty standard use case and then quickly expand into other areas as well.

Brown and Hamidi met during their time at Heroku, where Brown was a director of product management and Hamidi a lead software engineer. But while Heroku made it very easy for developers to publish their web apps, there wasn’t anything comparable in the highly fragmented database space. The team acknowledges that there are a lot of tools that aim to solve these data problems, but few of them focus on the user experience.

“When we talk to customers now, it’s still very much an unsolved problem,” Hamidi said. “It seems kind of insane to me that this is such a common thing and there is no ‘oh, of course you use this tool because it addresses all my problems.’ And so the angle that we’re taking is that we see user experience not as a nice-to-have, it’s really an enabler, it is something that enables a software engineer or someone who isn’t a data engineer with 10 years of experience in wrangling Kafka and Postgres and all these things. […] That’s a transformative kind of change.”

It’s worth noting that Meroxa uses a lot of open-source tools but the company has also committed to open-sourcing everything in its data plane as well. “This has multiple wins for us, but one of the biggest incentives is in terms of the customer, we’re really committed to having our agenda aligned. Because if we don’t do well, we don’t serve the customer. If we do a crappy job, they can just keep all of those components and run it themselves,” Hamidi explained.

Today, Meroxa, which the team founded in early 2020, has over 24 employees (and is 100% remote). “I really think we’re building one of the most talented and most inclusive teams possible,” Brown told me. “Inclusion and diversity are very, very high on our radar. Our team is 50% black and brown. Over 40% are women. Our management team is 90% underrepresented. So not only are we building a great product, we’re building a great company, we’re building a great business.”  

Microsoft agreed today to acquire Nuance Communications, a leader in speech to text software, for $19.7 billion. Bloomberg broke the story over the weekend that the two companies were in talks.

In a post announcing the deal, the company said this was about increasing its presence in the healthcare vertical, a place where Nuance has done well in recent years. In fact, the company announced the Microsoft Cloud for Healthcare last year, and this deal is about accelerating its presence there. Nuance’s products in this area include Dragon Ambient eXperience, Dragon Medical One and PowerScribe One for radiology reporting.

“Today’s acquisition announcement represents the latest step in Microsoft’s industry-specific cloud strategy,” the company wrote. The acquisition also builds on several integrations and partnerships the two companies have made in the last couple of years.

The company boasts 10,000 healthcare customers, according to information on the website. Those include AthenaHealth, Johns Hopkins, Mass General Brigham and Cleveland Clinic to name but a few, and it was that customer base that attracted Microsoft to pay the price it did to bring Nuance into the fold.

Nuance CEO Mark Benjamin will remain with the company and report to Scott Guthrie, Microsoft’s EVP in charge of the cloud and AI group.

Nuance has a complex history. It went public in 2000 and began buying speech recognition products including Dragon Dictate from Lernout Hauspie in 2001. It merged with a company called ScanSoft in 2005. That company began life as Visioneer, a scanning company in 1992.

Today, the company has a number of products including Dragon Dictate, a consumer and business text to speech product that dates back to the early 1990s. It’s also involved in speech recognition, chat bots and natural language processing particularly in healthcare and other verticals.

The company has 6,000 employees spread across 27 countries. In its most recent earnings report from November 2020, which was for Q42020, the company reported $352.9 million in revenue compared to $387.6 million in the same period a year prior. That’s not the direction a company wants to go in, but it is still a run rate of over $1.4 billion.

At the time of that earnings call, the company also announced it was selling its medical transcription and electronic health record (EHR) Go-Live services to Assured Healthcare Partners and Aeries Technology Group. Company CEO Benjamin said this was about helping the company concentrate on its core speech services.

“With this sale, we will reach an important milestone in our journey towards a more focused strategy of advancing our Conversational AI, natural language understanding and ambient clinical intelligence solutions,” Benjamin said in a statement at the time.

It’s worth noting that Microsoft already has a number speech recognition and chat bot products of its own including desktop speech to text services in Windows and on Azure, but it took a chance to buy a market leader and go deeper into the healthcare vertical.

The transaction has already been approved by both company boards and Microsoft reports it expects the deal to close by the end of this year, subject to standard regulatory oversight and approval by Nuance shareholders.

This would mark the second largest purchase by Microsoft ever, only surpassed by the $26.2 billion the company paid for LinkedIn in 2016.