With the right tools, predicting startup revenue is possible

For a long time, “revenue” seemed to be a taboo word in the startup world. Fortunately, things have changed with the rise of SaaS and alternative funding sources such as revenue-based investing VCs. Still, revenue modeling remains a challenge for founders. How do you predict earnings when you are still figuring it out?

The answer is twofold: You need to make your revenue predictable, repeatable and scalable in the first place, plus make use of tools that will help you create projections based on your data. Here, we’ll suggest some ways you can get more visibility into your revenue, find the data that really matter and figure out how to put a process in place to make forecasts about it.

You need to make your revenue predictable, repeatable and scalable in the first place, plus make use of tools that will help you create projections based on your data.

Base projections on repeatable, scalable results

Aaron Ross is a co-author of “Predictable Revenue,” a book based on his experience of creating a process and team that helped grow Salesforce’s revenue by more than $100 million. “Predictable” is the key word here: “You want growth that doesn’t require guessing, hope and frantic last-minute deal-hustling every quarter- and year-end,” he says.

This makes recurring revenue particularly desirable, though it is by no means the be-all-end-all of predictable revenue. On one hand, there is always the risk that recurring revenue won’t last, as customers may churn and organic growth runs out of gas. On the other, there is a broader picture for predictable revenue that goes beyond subscription-based models.

Ross and his co-author, Marylou Tyler, outline three steps to predictable revenue: predictable lead generation, a dedicated sales development team and consistent sales systems. They wrote an entire book about it, so it would be hard to sum it up here. So what’s the takeaway? You shouldn’t base your projections on processes and results that aren’t repeatable and scalable.

Cross the hot coals

In their early days, startups usually grow via word of mouth, luck and sheer hustle. The problem is that it likely won’t lead to sustainable growth; as the saying goes, what got you here won’t get you there. In between, there is typically a phase of uncertainty and missed results that Ross refers to as “the hot coals.”

Before the hot coals, predicting revenue is vain at best, and oftentimes impossible. I, for one, remember being at a loss when an old-school investor asked me for five-year profit-and-loss projections when my now-defunct startup was nowhere near a stable money-making path. Not all seed investors expect this, so there was obviously a mismatch here, but the challenge is still the same for most founders: How do you bridge the gap between traditional projections and the reality of a startup?

Docugami’s new model for understanding documents cuts its teeth on NASA archives

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.

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.

Illustration showing a document corresponding to pieces of another document.

Image Credits: Docugami

“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

Illustration showing a person interacting with a digital document.

Image Credits: Docugami

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.”

Illustration showing a document being turned into a report and a spreadsheet.

Image Credits: Docugami

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

Image Credits: NASA

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.”

Immersion cooling to offset data centers’ massive power demands gains a big booster in Microsoft

LiquidStack does it. So does Submer. They’re both dropping servers carrying sensitive data into goop in an effort to save the planet. Now they’re joined by one of the biggest tech companies in the world in their efforts to improve the energy efficiency of data centers, because Microsoft is getting into the liquid-immersion cooling market.

Microsoft is using a liquid it developed in-house that’s engineered to boil at 122 degrees Fahrenheit (lower than the boiling point of water) to act as a heat sink, reducing the temperature inside the servers so they can operate at full power without any risks from overheating.

The vapor from the boiling fluid is converted back into a liquid through contact with a cooled condenser in the lid of the tank that stores the servers.

“We are the first cloud provider that is running two-phase immersion cooling in a production environment,” said Husam Alissa, a principal hardware engineer on Microsoft’s team for datacenter advanced development in Redmond, Washington, in a statement on the company’s internal blog. 

While that claim may be true, liquid cooling is a well-known approach to dealing with moving heat around to keep systems working. Cars use liquid cooling to keep their motors humming as they head out on the highway.

As technology companies confront the physical limits of Moore’s Law, the demand for faster, higher performance processors mean designing new architectures that can handle more power, the company wrote in a blog post. Power flowing through central processing units has increased from 150 watts to more than 300 watts per chip and the GPUs responsible for much of Bitcoin mining, artificial intelligence applications and high end graphics each consume more than 700 watts per chip.

It’s worth noting that Microsoft isn’t the first tech company to apply liquid cooling to data centers and the distinction that the company uses of being the first “cloud provider” is doing a lot of work. That’s because bitcoin mining operations have been using the tech for years. Indeed, LiquidStack was spun out from a bitcoin miner to commercialize its liquid immersion cooling tech and bring it to the masses.

“Air cooling is not enough”

More power flowing through the processors means hotter chips, which means the need for better cooling or the chips will malfunction.

“Air cooling is not enough,” said Christian Belady, vice president of Microsoft’s datacenter advanced development group in Redmond, in an interview for the company’s internal blog. “That’s what’s driving us to immersion cooling, where we can directly boil off the surfaces of the chip.”

For Belady, the use of liquid cooling technology brings the density and compression of Moore’s Law up to the datacenter level

The results, from an energy consumption perspective, are impressive. The company found that using two-phase immersion cooling reduced power consumption for a server by anywhere from 5 percent to 15 percent (every little bit helps).

Microsoft investigated liquid immersion as a cooling solution for high performance computing applications such as AI. Among other things, the investigation revealed that two-phase immersion cooling reduced power consumption for any given server by 5% to 15%. 

Meanwhile, companies like Submer claim they reduce energy consumption by 50%, water use by 99%, and take up 85% less space.

For cloud computing companies, the ability to keep these servers up and running even during spikes in demand, when they’d consume even more power, adds flexibility and ensures uptime even when servers are overtaxed, according to Microsoft.

“[We] know that with Teams when you get to 1 o’clock or 2 o’clock, there is a huge spike because people are joining meetings at the same time,” Marcus Fontoura, a vice president on Microsoft’s Azure team, said on the company’s internal blog. “Immersion cooling gives us more flexibility to deal with these burst-y workloads.”

At this point, data centers are a critical component of the internet infrastructure that much of the world relies on for… well… pretty much every tech-enabled service. That reliance however has come at a significant environmental cost.

“Data centers power human advancement. Their role as a core infrastructure has become more apparent than ever and emerging technologies such as AI and IoT will continue to drive computing needs. However, the environmental footprint of the industry is growing at an alarming rate,” Alexander Danielsson, an investment manager at Norrsken VC noted last year when discussing that firm’s investment in Submer.

Solutions under the sea

If submerging servers in experimental liquids offers one potential solution to the problem — then sinking them in the ocean is another way that companies are trying to cool data centers without expending too much power.

Microsoft has already been operating an undersea data center for the past two years. The company actually trotted out the tech as part of a push from the tech company to aid in the search for a COVID-19 vaccine last year.

These pre-packed, shipping container-sized data centers can be spun up on demand and run deep under the ocean’s surface for sustainable, high-efficiency and powerful compute operations, the company said.

The liquid cooling project shares most similarity with Microsoft’s Project Natick, which is exploring the potential of underwater datacenters that are quick to deploy and can operate for years on the seabed sealed inside submarine-like tubes without any onsite maintenance by people. 

In those data centers nitrogen air replaces an engineered fluid and the servers are cooled with fans and a heat exchanger that pumps seawater through a sealed tube.

Startups are also staking claims to cool data centers out on the ocean (the seaweed is always greener in somebody else’s lake).

Nautilus Data Technologies, for instance, has raised over $100 million (according to Crunchbase) to develop data centers dotting the surface of Davey Jones’ Locker. The company is currently developing a data center project co-located with a sustainable energy project off the coast of Stockton, Calif.

With the double-immersion cooling tech Microsoft is hoping to bring the benefits of ocean-cooling tech onto the shore. “We brought the sea to the servers rather than put the datacenter under the sea,” Microsoft’s Alissa said in a company statement.

Ioannis Manousakis, a principal software engineer with Azure (left), and Husam Alissa, a principal hardware engineer on Microsoft’s team for datacenter advanced development (right), walk past a container at a Microsoft datacenter where computer servers in a two-phase immersion cooling tank are processing workloads. Photo by Gene Twedt for Microsoft.

YC-backed Abacum nets $7M to empower finance teams with real-time data and collaboration tools

SaaS to support mid-sized companies’ financial planning with real-time data and native collaboration isn’t the sexiest startup pitch under the sun but it’s one that’s swiftly netted Abacum a bunch of notable backers — including Creandum, which is leading a $7M seed round that’s being announced today.

The rosters of existing investors also participating in the round are Y Combinator (Abacum was part of its latest batch), PROFounders, and K-Fund, along with angel investors such as Justin Kan (Atrium and Twitch co-founder and CEO); Maximilian Tayenthal (N26 co-founder and co-CEO & CFO); Thomas Lehrman (GLG co-founder and ex-CEO), Avi Meir (TravelPerk co-founder and CEO); plus Jenny Bloom (Zapier CFO and Mailchimp ex-CFO) and Mike Asher (CFO at Neo4j).

Abacum was founded last year in the middle of the COVID-19 global lockdown, after what it says was around a year of “deep research” to feed its product development. They launched their SaaS in June 2020. And while they’re not disclosing customer numbers at this early stage their first clients include a range of scale-up companies in the US and in Europe, including the likes of Typeform, Cabify, Ebury, Garten, Jeff and Talkable.

The startup’s Spanish co-founders — Julio Martinez, a fintech entrepreneur with an investment banking background, and Jorge Lluch, a European Space Agency engineer turned CFO/COO — spotted an opportunity to build dedicated software for mid-market finance teams to provide real-time access to data via native collaborative that plugs into key software platforms used by other business units, having felt the pain of a lack of access to real-time data and barriers to collaboration in their own professional experience with the finance function.

The idea with Abacum is to replace the need for finance teams to manually update their models. The SaaS automatically does the updates, fed with real-time data through direct integrations with software used by teams dealing with functions like HR, CRM, ERP (and so on) — empowering the finance function to collaborate more easily across the business and bolster its strategic decision-making capabilities.

The startup’s sales pitch to the target mid-sized companies is multi-layered. Abacum says its SaaS both saves finance teams time and enables faster-decision making.

“Prior to using Abacum, finance analysts in our clients were easily spending 50% to 70% of their time in manual tasks like downloading files from different systems, copy&pasting them in massive spreadsheets (that crash frequently), formatting the data by manually adding and removing rows, columns and formats, connecting the data in a model prone to manual error (e.g. vlookups & sumifs),” Martinez tells TechCrunch. “With Abacum, this entire manual part is automatically done and the finance professionals can spend their time analyzing and adding real value to the business.”

“We enable faster decisions that were not possible prior to Abacum. For instance, some of our clients were updating their cohort analysis on a quarterly basis only because the associated manual tasks were too painful. With us, they’re able to update the analysis weekly and take better decisions as a result.”

The SaaS also supports decisions in another way — by applying machine learning to business data to generate estimates on future performance, providing an AI-based reference point based on historical data that finance teams can use to inform their assumptions.

And it aids cross-business collaboration — allowing users to share and gather information “easily through workflows and permissions”. “We see that this results in faster and richer decisions as more stakeholders are brought into the process,” he adds.

Martinez says Abacum chose to focus on mid-market finance teams because they face “more challenges and inefficiencies” vs the smaller (and larger) ends of the market. “In that segment, the finance function is underinvested — they face the acute complexities of scaling companies that become very pressing but at the same time they are still considered a support function, a back-office,” he argues.

“Abacum makes finance a strategic function — we deliver native collaboration to finance teams so that they become the trusted business partner they want to be. We also see that the pandemic has accelerated the need for finance teams to collaborate effectively and work remotely,” he adds.

He also describes the mid market segment as “fairly unpenetrated” — claiming many companies do not yet having a solution in place.

While competitors he points to when asked about other players in the space are long in the tooth in digital terms: Adaptive Insights (2003); Host Analytics (2001); and Anaplan (2008).

Commenting on the seed round in a statement, Peter Specht, principal at Creandum, added: “The financial planning processes in many companies are ripe for disruption and demand more automation. Abacum’s slick solution empowers finance teams to be more collaborative, efficient and better informed with access to real-time data. We were impressed by their user-friendly product, the initial hiring of top talent, and crucially the strong founders and their extensive operational experience — including as CFOs and entrepreneurs who have experienced the problem first-hand. We are delighted to be part of Abacum’s journey to empower global SMEs to bring their financial operations to new levels.”

Abacum’s seed financing will be ploughed into product development and growth, per Martinez, who says it’s focused on wooing finance teams in the US and Europe for now.

Former Amazon exec gives Chinese firms a tool to fight cyber threats

China is pushing forward an internet society where economic and public activities increasingly take place online. In the process, troves of citizen and government data get transferred to cloud servers, raising concerns over information security. One startup called ThreatBook sees an opportunity in this revolution and pledges to protect corporations and bureaucracies against malicious cyberattacks.

Antivirus and security software has been around in China for several decades, but until recently, enterprises were procuring them simply to meet compliance requests, Xue Feng, founder and CEO of six-year-old ThreatBook, told TechCrunch in an interview.

Starting around 2014, internet accessibility began to expand rapidly in China, ushering in an explosion of data. Information previously stored in physical servers was moving to the cloud. Companies realized that a cyber attack could result in a substantial financial loss and started to pay serious attention to security solutions.

In the meantime, cyberspace is emerging as a battlefield where competition between states plays out. Malicious actors may target a country’s critical digital infrastructure or steal key research from a university database.

“The amount of cyberattacks between countries is reflective of their geopolitical relationships,” observed Xue, who oversaw information security at Amazon China before founding ThreatBook. Previously, he was the director of internet security at Microsoft in China.

“If two countries are allies, they are less likely to attack one another. China has a very special position in geopolitics. Besides its tensions with the other superpowers, cyberattacks from smaller, nearby countries are also common.”

Like other emerging SaaS companies, ThreatBook sells software and charges a subscription fee for annual services. More than 80% of its current customers are big corporations in finance, energy, the internet industry, and manufacturing. Government contracts make up a smaller slice. With its Series E funding round that closed 500 million yuan ($76 million) in March, ThreatBook boosted its total capital raised to over 1 billion yuan from investors including Hillhouse Capital.

Xue declined to disclose the company’s revenues or valuation but said 95% of the firm’s customers have chosen to renew their annual subscriptions. He added that the company has met the “preliminary requirements” of the Shanghai Exchange’s STAR board, China’s equivalent to NASDAQ, and will go public when the conditions are ripe.

“It takes our peers 7-10 years to go public,” said Xue.

ThreatBook compares itself to CrowdStrike from Silicon Valley, which filed to go public in 2019 and detect threats by monitoring a company’s “endpoints”, which could be an employee’s laptops and mobile devices that connect to the internal network from outside the corporate firewall.

ThreatBook similarly has a suite of software that goes onto the devices of a company’s employees, automatically detects threats and comes up with a list of solutions.

“It’s like installing a lot of security cameras inside a company,” said Xue. “But the thing that matters is what we tell customers after we capture issues.”

SaaS providers in China are still in the phase of educating the market and lobbying enterprises to pay. Of the 3,000 companies that ThreatBook serves, only 300 are paying so there is plentiful room for monetization. Willingness to spend also differs across sectors, with financial institutions happy to shell out several million yuan ($1 = 6.54 yuan) a year while a tech startup may only want to pay a fraction of that.

Xue’s vision is to take ThreatBook global. The company had plans to expand overseas last year but was held back by the COVID-19 pandemic.

“We’ve had a handful of inquiries from companies in Southeast Asia and the Middle East. There may even be room for us in markets with mature [cybersecurity companies] like Europe and North America,” said Xue. “As long as we are able to offer differentiation, a customer may still consider us even if it has an existing security solution.”

ConductorOne raises $5M in seed round led by Accel to automate your access requests

Over the course of their careers, Alex Bovee and Paul Querna realized that while the use of SaaS apps and cloud infrastructure was exploding, the process to give employees permission to use them was not keeping up.

The pair led Zero Trust strategies and products at Okta, and could see the problem firsthand. For the unacquainted, Zero Trust is a security concept based on the premise that organizations should not automatically trust anything inside or outside its perimeters and, instead must verify anything and everything trying to connect to its systems before granting access.

Bovee and Querna realized that while more organizations were adopting Zero Trust strategies, they were not enacting privilege controls. This was resulting in delayed employee access to apps, or to the over-permissioning employees from day one.

Last summer, Bovee left Okta to be the first virtual entrepreneur-in-residence at VC firm Accel. There, he and Accel partner Ping Li got to talking and realized they both had an interest in addressing the challenge of granting permissions to users of cloud apps quicker and more securely.

Recalls Li: “It was actually kind of fortuitous. We were looking at this problem and I was like ‘Who can we talk to about the space? And we realized we had an expert in Alex.”

At that point, Bovee told Li he was actually thinking of starting a company to solve the problem. And so he did. Months later, Querna left Okta to join him in getting the startup off the ground. And today, ConductorOne announced that it raised $5 million in seed funding in a round led by Accel, with participation from Fuel Capital, Fathom Capital and Active Capital. 

ConductorOne plans to use its new capital to build what the company describes as “the first-ever identity orchestration and automation platform.” Its goal is to give IT and identity admins the ability to automate and delegate employee access to cloud apps and infrastructure, while preserving least privilege permissions. 

“The crux of the problem is that you’ve got these identities — you’ve got employees and contractors on one side and then on the other side you’ve got all this SaaS infrastructure and they all have sort of infinite permutations of roles and permissions and what people can do within the context of those infrastructure environments,” Bovee said.

Companies of all sizes often have hundreds of apps and infrastructure providers they’re managing. It’s not unusual for an IT helpdesk queue to be more than 20% access requests, with people needing urgent access to resources like Salesforce, AWS, or GitHub, according to Bovee. Yet each request is manually reviewed to make sure people get the right level of permissions. 

“But that access is never revoked, even if it’s unused,” Bovee said. “Without a central layer to orchestrate and automate authorization, it’s impossible to handle all the permissions, entitlements, and on- and off-boarding, not to mention auditing and analytics.”

ConductorOne aims to build “the world’s best access request experience,” with automation at its core.

“Automation that solves privilege management and governance is the next major pillar of cloud identity,” Accel’s Li said.

Bovee and Querna have deep expertise in the space. Prior to Okta, Bovee led enterprise mobile security product development at Lookout. Querna was the co-founder and CTO of ScaleFT, which was acquired by Okta in 2018. He also led technology and strategy teams at Rackspace and Cloudkick, and is a vocal and active open source software advocate.   

While the company’s headquarters are in Portland, Oregon, ConductorOne is a remote-first company with 10 employees.

“We’re deep in building the product right now, and just doing a lot of customer development to understand the problems deeply,” Bovee said. “Then we’ll focus on getting early customers.”

Collabio lets you co-edit documents without the cloud

Meet Collabio Spaces: An office suite app with a cloudless co-authoring twist that looks helpful if you need to collaborate on documents without having to worry about losing control of your data or the thread of changes.

The p2p software lets multiple people co-edit a document locally — from a mobile device or desktop computer — without A) the risk of uploading sensitive information to the cloud (i.e. as you must if you’re using a shared document function of a service like Google Docs); or B) the tedium of emailing a text to multiple recipients and then having to collate and resolve changes manually, once all the contributions trickle back.  

There’s more coming down Collabio’s pipe too. Document collaborating will be possible from anywhere in the future, not only (as now) via a local network: A major release slated for next month will add p2p collaboration that works via the Internet — but still without the privacy risk of having a remote server in the loop.

Collabio’s app is MacOS and iOS only for now — but Android and Windows versions are in the works, slated for release this year.

Current supported text formats are DOCX, ODT, XLSX and ODS. Other features of Collabio’s office suite include the ability to scan and recognise texts and images using a camera; annotate and comment on PDFs (including via audio); e-sign text documents and PDFs; and view presentations.

Image credit: XCDS/Collabio

Its maker XCDS (aka “eXtended Collaboration Document Systems”), which is headquartered in London, UK with an R&D hub in Prague in the Czech Republic, has been in business for around a decade at this point — but working on office tools for some seven years, per CTO Egor Goroshko, who says they see Collabio as a startup in its own right.

The app is being funded by (an undisclosed amount of funding from undisclosed) private investors, with the team planning to take in further funding to continue development in the near future as they build momentum for the product.

With the coronavirus supercharging remote working over the past 12 months there is certainly opportunity to improve on the current crop of collaboration and productivity tools — and help to safely break down any unwelcome workflow barriers which have been erected as a result of scores of office workers no longer being co-located. Although the current version of Collabio is designed for nearby, rather than remote collaboration — so its next major release looks the most interesting from that perspective.

The early team behind Collabio included some devs who worked on Quickoffice but didn’t go to Google as part of that 2012 acquisition. Instead they focused on thinking about how to improve the user experience around documents — finally bringing their long-developed p2p document collaboration product to market last fall.

“When we started with Collabio we were ready for the long game,” Goroshko tells TechCrunch. “We knew that we would need to implement most of the features [office suite software] users were familiar with, before we could start developing our own ideas.”

“Long story short, our cloudless collaboration works exactly the same way as a cloud one. Of course there is some difference in the way you connect to the document but after that, you have exactly the same experience as if you work in the cloud,” he continues.

“We started with an iOS app in September 2020 and introduced a macOS version in October. With our early releases, we mainly concentrate on testing the app with real users and prove our ideas. Starting from our launch, we’ve got almost 15K of installs and valuable feedback on what users need and what can be improved. We pushed intensively on the market starting in February 2021 this year and got more than one thousand users during this month.”

There are some key differences between Collabio’s p2p cloudless collaboration and the (more typical) upload-to-a-server flavor that are worth flagging.

Notably, the lack of constant access to the document that you’re co-authoring/co-editing. Although that limitation may also be desirable if you want to tightly manage collaborative access to your data.

“In Collabio we call cloudless collaborative editing ‘Ad-Hoc collaboration’, because without a cloud your peers have no constant access to the document, so this thing is essential for occasional document discussion and updates,” Goroshko notes.

Another important difference he points to is that a shared document remains on the owner host devices only — and a copy can only be saved by the owner (at least for now).

“Other peers have session document access but the application does not upload/transfer files to collaborators’ devices,” he explains. “[The] session lasts til the host keeps the document open. As soon as you close the document, peers lose their access and can’t save the document locally. This is made for reasons of privacy but we are now considering giving users the option to allow connected peers to save a copy of the document.”

Given that all document work is done on devices on a local network there’s no need for an Internet connection to be able to collaborate via Collabio — which the team argues can itself be pretty useful, such as in situations like business travel (remember it?) when a stable Internet connection may not be readily available.

For this local p2p connectivity Goroshko says Collabio uses both wi-fi and Bluetooth — “to achieve better discovering quality”. “This is a common approach used, for example, in AirDrop technology. When peers’ addresses are identified, the application establishes connection via WiFi to achieve better speed and the quality of data exchange,” he says.

“All work is done only on devices in the local network so our Ad-Hoc collaboration does not need the Internet, the same way as you do not need the Internet to exchange files via AirDrop,” he goes on. “Just like with AirDrop, you do not need any specific configuration for Collabio Spaces, everything is done automatically. You start a session and peers see it on their devices, they simply connect to a selected document, and if they know the code, they can edit the document.”

Goroshko says Collabio’s team has been inspired by Apple’s technology — and the tech giant’s ‘it just works’ philosophy. But are committed to bringing the product to non-Apple platforms, aiming for a release later this year.

“It is a large, complex and ambitious project but we believe we can introduce game-changing approaches,” he continues. “The Office software market is quite conservative and market expectations from new software are really high. This is the reason why it has taken so much time to get to a public release stage. But with such a high entrance threshold and with slow innovations in the area of office document management and editing, this creates great opportunities.”

He argues that Collabio has been able to get efficiency gains vs office suites that had to bolt collaboration onto a legacy product exactly because it was being developed from scratch — with “collaborative editing in mind from the first step of proof of concept”. Hence its implementation of collaborative editing algorithms can work “with minimal resources consumption even on mobile phones”.

Goroshko says a Collabio user can have up to five peers simultaneously connected if they launch a collaboration session via a mobile device — with all participants able to edit the document. (Desktops support more connections.)

“You launch a collaboration session with a honeycomb icon, and any nearby devices with [the] Collabio Spaces app show shared documents,” he explains. “Under the hood, it works the similar way as sharing files through AirDrop or streaming audio/video through AirPlay. People nearby can join editing, if they know the security code assigned to the session.”

These p2p connections are encrypted with “standard end-to-end encryption”, according to Goroshko — who admits to “some tricks to allow trusted connections in the local network without access to the Internet”, adding: “We believe that this is enough for the start but in the future we will probably improve this approach.”

So — as with any nascent and non-independently security-tested product — prospective users should approach with caution, weighing up the sensitivity of any data they might wish to share for co-editing purposes before trusting it to Collabio’s novel implementation.

The startup, meanwhile, sees plenty of potential growth coming from frustrated office workers trying to find smarter ways to work remotely.

“Our goal is to create an editor specifically for team work, to help people get the most from collaboration,” says Goroshko. “Working together with others gives you a lot of advantages but requires more effort to sync with others. Planning, tracking, discussions, reviews — currently most of this work is performed separately from the document or locked inside the document. We want to cover this gap and give our users the most from collaboration with each other.”

“We consider two main types of competitors on the market,” he adds. “Classical office document editing suites like MS Office, Google Docs and Libre Office. We do not consider direct competition with them because their features set is enormous. However, many people simply do not use most of these features!

“And now a few newcomers have appeared on the market like Notion or Airtable, introducing smart ways how the document editing process can be integrated into your business. We see ourselves somewhere in between these products and classical office suites.”

A subscription payment is required to use Collabio Suites but a free trial version is available for up to a week.

We’re also told there’s an option for free of charge usage where the user is able to view and edit documents as a peer but can’t be the host of a collaboration session.

The major release that’s coming in May looks set to expand Collabio’s utility greatly — enabling it to tap into the remote work boom — by adding the ability to do p2p collaboration from anywhere via the Internet, also without the need for a remote server sitting in the loop.

How will that forthcoming functionality work? In a word: Math. Goroshko says the implementation will rely on an Operations Transformation algorithm keeping the document consistent “at any moment” during co-editing — avoiding the need for true real-time operations.

“It does not matter what co-editors type for in the end they all have absolutely the same content,” he says. “The algorithm does not guarantee that the result will be meaningful. If several people type in the same place, they will get an abracadabra. But this will be exactly the same abracadabra after all changes have been synced between all participants. This is the point. Operations Transformation does not require true real-time operations, changes can come early or later, even after sufficient delays. In either case they will be transformed to become inline with other changes. So regardless of cloud or cloudless collaboration mode, you do not need specific infrastructure or high speed processing to support collaborative editing.”

Pipe, which aims to be the ‘Nasdaq for revenue,’ raises more money at a $2B valuation

Fast-growing fintech Pipe has raised another round of funding at a $2 billion valuation, just weeks after raising $50M in growth funding, according to sources familiar with the deal.

Although the round is still ongoing, Pipe has reportedly raised $150 million in a “massively oversubscribed” round led by Baltimore, Md.-based Greenspring Associates. While the company has signed a term sheet, more money could still come in, according to the source. Both new and existing investors have participated in the fundraise.

The increase in valuation is “a significant step up” from the company’s last raise. Pipe has declined to comment on the deal.

A little over one year ago, Pipe raised a $6 million seed round led by Craft Ventures to help it pursue its mission of giving SaaS companies a funding alternative outside of equity or venture debt.

The buzzy startup’s goal with the money was to give SaaS companies a way to get their revenue upfront, by pairing them with investors on a marketplace that pays a discounted rate for the annual value of those contracts. (Pipe describes its buy-side participants as “a vetted group of financial institutions and banks.”)

Just a few weeks ago, Miami-based Pipe announced a new raise — $50 million in “strategic equity funding” from a slew of high-profile investors. Siemens’ Next47 and Jim Pallotta’s Raptor Group co-led the round, which also included participation from Shopify, Slack, HubSpot, Okta, Social Capital’s Chamath Palihapitiya, Marc Benioff, Michael Dell’s MSD Capital, Republic, Alexis Ohanian’s Seven Seven Six and Joe Lonsdale.

At that time, Pipe co-CEO and co-founder Harry Hurst said the company was also broadening the scope of its platform beyond strictly SaaS companies to “any company with a recurring revenue stream.” This could include D2C subscription companies, ISP, streaming services or a telecommunications companies. Even VC fund admin and management are being piped on its platform, for example, according to Hurst.

“When we first went to market, we were very focused on SaaS, our first vertical,” he told TC at the time. “Since then, over 3,000 companies have signed up to use our platform.” Those companies range from early-stage and bootstrapped with $200,000 in revenue, to publicly-traded companies.

Pipe’s platform assesses a customer’s key metrics by integrating with its accounting, payment processing and banking systems. It then instantly rates the performance of the business and qualifies them for a trading limit. Trading limits currently range from $50,000 for smaller early-stage and bootstrapped companies, to over $100 million for late-stage and publicly traded companies, although there is no cap on how large a trading limit can be.

In the first quarter of 2021, tens of millions of dollars were traded across the Pipe platform. Between its launch in late June 2020 through year’s end, the company also saw “tens of millions” in trades take place via its marketplace. Tradable ARR on the platform is currently in excess of $1 billion.

Living Security raises $14M for gamified cybersecurity training

Cybersecurity training is one of those things that everyone has to do but not something everyone necessarily looks forward to.

Living Security is an Austin-based startup out to change cybersecurity training something you look forward to, not dread. And the company has just closed on a $14 million Series B to continue its expansion beyond cybersecurity awareness training into human risk management.

Washington, D.C. based-Updata Partners led the financing, which also included participation from existing backers previous investors Silverton Partners, Active Capital, Rain Capital and SaaS Venture Partners. The investment comes after $5 million series A, led by Austin-based Silverton, raised last April.

Husband and wife Drew and Ashley Rose founded Living Security in June 2017 with the mission of making cybersecurity training less boring and more effective via gamified learning with live action immersive storylines, role-based micro modules and reporting.

Living Security launched with its flagship product — Cyber Escape Room. When the pandemic hit, the startup brought its in-person training sessions online through the launch of CyberEscape Online.

With more people working remotely, the need for the type of offering Living Security provides has become even more paramount, considering how many people use personal devices for professional reasons, among other things. Employees are more vulnerable than ever to inadvertently providing entry points into the networks of the enterprises where they work — whether through social engineering, phishing or other methods.

Today, Living Security works with over 100 large enterprises to train their global workforces to better protect sensitive data and secure their organizations. The startup’s customer list is impressive, and includes large enterprises such as CVS Health, Mastercard, Verizon, MassMutual, Biogen, AmerisourceBergen, Hewlett Packard, JPMorgan and Target.

So it’s not a big surprise that in 2020, Living Security tripled its revenue and employee headcount and more than doubled its customer count. The company declined to provide hard revenue figures, saying only that ARR grew nearly 200% last year.

“We have seen a significant increase in account growth and expansion in existing accounts..largely in part due to the scalability of our digital solution,” CEO Ashley Rose said.

With the success of its escape rooms and gamified training, Living Security’s team then asked themselves how they could make their efforts “more predictable.”

“We added risk management and scoring so program and security owners could become more targeted and focused on the delivery of their training,” Rose said.

So now Living Security aims to use behavioral data and analytics to measure and manage human risk. It plans to take that data and provide “predictive interventions” to employees. 

“We’re focused on ‘How do we turn people from our greatest risk, to our greatest assets in cybersecurity?” Rose said. “That’s our big vision for the company.”

Image Credits: Living Security

With its “Unify” human risk management platform, Living Security wants to provide an even more scalable solution. The company also plans to use its new capital toward expanding its geographic reach and scaling both direct and channel sales efforts.

Currently, Living Security has 55 employees with the goal of having 90 by year’s end.

Deb Walter, director of information security training and awareness at AmerisourceBergen, said she first engaged with Living Security in 2017 when she requested its CyberSecurity Card game. 

“I wanted to gamify how I presented training,” she recalls.

Introducing episodic gamification and its “bingeable” content into her training program was a big hit with employees, according to Walter.

“Their new platform is enabling us to deploy an ‘Information security academy’ to encourage associates and contractors to use several modes of training to earn points and track themselves on a leaderboard,” she said.

Updata General Partner Jon Seeber, who is taking a seat on Living Security’s board with the funding, said his firm saw “breakout potential” in the startup’s platform.

“It comes as close as you can to closing the loop between people and the systems on which they’re operating,” he said. 

Plus, he said, it does it in a way that avoids the compliance-focused, “check-the-box” mindset that so often dominates employee-focused cybersecurity solutions.

Jeff Bezos’ investment fund is backing a startup hoping to be the AWS for SMB accounting

One of the biggest pain points for startups and small businesses is keeping up with back office tasks such as bookkeeping and managing taxes.

QuickBooks, it seems, just doesn’t always cut it.

Three-time co-founders Waseem Daher, Jeff Arnold, and Jessica McKellar formed Pilot with the mission of affordably providing back office services to startups and SMBs. With over 1,000 customers, it has gained serious traction over the years. And Pilot has now also received validation from some big-name investors. On Friday, the company announced a $100 million Series C that doubles the company’s valuation to $1.2 billion.

Bezos Expeditions — Amazon founder Jeff Bezos’ personal investment fund — and Whale Rock Capital (a $10 billion hedge fund) co-led the round, which also included participation from Sequoia Capital, Index Ventures, Authentic Ventures and others. 

Stripe and Index Ventures co-led Pilot’s $40 million Series B in April 2019. The latest financing brings the company’s total funding raised to over $158 million since its 2017 inception.

The founding team certainly has an impressive track record, having founded and sold two previous companies: Ksplice  (to Oracle) and Zupli (to Dropbox).

Pilot’s pitch is about more than just software. The company combines its software with accountants to do things such as provide “CFO Services” to SMBs without a full-stack finance team. It also provides monthly variance analysis for all its bookkeeping customers, essentially serving as a controller for those companies, so they can make better budgeting and spending decisions.

It also helps companies access small business tax credits they may not have otherwise known about. 

Last year, Pilot completed more than $3 billion in bookkeeping transactions for its customers, which range from pre-revenue startups to larger companies with more than $30M of revenue a year. Customers include Bolt, r2c and Pathrise, among others.

Pilot has also inked a number of co-marketing partnerships with companies such as American Express, Bill.com, Brex, Carta, Gusto, Rippling, Stripe, SVB, and Techstars.

Ironically, Pilot says it aspires to the “AWS of SMB backoffice.” (In fact, co-founder Waseem Daher started his career as an intern at Amazon). Put simply, Pilot wants to take care of all those back office tasks so companies can focus more on growth and winning business.

Pilot strives to offer an “exceptional customer experience,” which is reflected in the fact that over 80% of the company’s business is driven by customer referrals and organic interest, according to Daher.

Whale Rock Partner Kristov Paulus said that white-glove customer service experience and Pilot’s “carefully-engineered” software make a powerful combination.

“We look forward to supporting Pilot in their vision to make back office services as easy-to-use, scalable, and ubiquitous as AWS has with the cloud,” he said.

Pilot’s model reminds me a lot of that of ScaleFactor’s, an Austin-based startup that raised $100 million in a year before it crashed and burned. But the difference in this case is that Pilot seems to have satisfied customers.