Coveo raises $227M at $1B+ valuation for AI-based enterprise search and personalization

Search and personalization services continue to be a major area of investment among enterprises, both to make their products and services more discoverable (and used) by customers, and to help their own workers get their jobs done, with the market estimated to be worth some $100 billion annually. Today, one of the big startups building services in this area raised a large round of growth funding to continue tapping that opportunity. Coveo, a Canadian company that builds search and personalisation services powered by artificial intelligence — used by its enterprise customers by way of clould-based, software-as-a-service — has closed a $227 million round, which CEO Louis Tetu tells me values the company at “well above” $1 billion, “Canadian or US dollars.”

The round is being led by Omers Capital Private Growth Equity Group, the investing arm of the Canadian pensions giant that makes large, later-stage bets (the company has been stepping up the pace of investments lately), with participation also from Evergreen Coast Capital, FSTQ, and IQ Ventures. Evergreen led the company’s last round of $100 million in April 2018, and in total the company has now raised just over $402 million with this round.

The $1 billion+ valuation appears to be a huge leap in the context of Coveo’s funding history: in that last round, it had a post-money valuation of about $370 million, according to PitchBook data.

Part of the reason for that is because of Coveo’s business trajectory, and part is due to the heat of the overall market.

Coveo’s round is coming about two weeks after another company that builds enterprise search solutions, Algolia, raised $110 million. The two aim at slightly different ends of the market, Tetu tells me, not directly competing in terms of target customers, and even services. “Algolia is in a different zip code,” he said. Good thing, too, if that’s the case: Salesforce — which is one of Coveo’s biggest partners and customers — was also a strategic investor in the Algolia round. Even if these two do not compete, there are plenty of others vying for the same end of the enterprise search and personalization continuum — they include Google, Microsoft, Elastic, IBM, Lucidworks, and many more. That, again, underscores the size of the market opportunity.

In terms of Coveo’s own business, the company works with some 500 customers today and says SaaS subscription revenues grew more than 55 percent year-over-year this year. Five hundred may sound like a small number, but it covers a lot of very large enterprises spanning web-facing businesses, commerce-based organizations, service-facing companies, and enterprise solutions.

In addition to Salesforce, it includes Visa, Tableau (also Salesforce now!), Honeywell, a Fortune 50 healthcare company (whose name is not getting disclosed), and what Tetu described to me as an Amazon competitor that does $21 billion in sales annually but doesn’t want to be named.

Coveo’s basic selling point is that the better discoverability and personalization that it provides helps its customers avoid as many call-center interactions (reducing operating expenditures), improving sales (boosting conversions and reducing cart abandonment), and help companies themselves just work faster.

“We believe that Coveo is the market leader in leveraging data and AI to personalize at scale,” said Mark Shulgan, Managing Director and Head of Growth Equity at Omers, in a statement. “Coveo fits our investment thesis precisely: an A-plus leadership team with deep expertise in enterprise SaaS, a Fortune 1000 customer base who deeply love the product, and a track record of high growth in a market worth over $100 billion. This makes Coveo a highly-coveted asset. We are glad to be partnering to scale this business.”

Alongside business development on its own steam, the company is going to be using this funding for acquisitions. Tetu notes that Coveo still has a lot of money in the bank from previous rounds.

“We are a real company with real positive economics,” he said. “This round is mostly to have dry powder to invest in a way that is commensurate in the AI space, and within commerce in particular.” To get the ball rolling on that, this past July, Coveo acquired Tooso, a specialist in AI-based digital commerce technology.


By Ingrid Lunden

Quilt Data launches from stealth with free portal to access petabytes of public data

Quilt Data‘s founders, Kevin Moore and Aneesh Karve, have been hard at work for the last four years building a platform to search for data quickly across vast repositories on AWS S3 storage. The idea is to give data scientists a way to find data in S3 buckets, and then package that data in forms that a business can use. Today, the company launched out of stealth with a free data search portal that not only proves what they can do, but also provides valuable access to 3.7 petabytes of public data across 23 S3 repositories.

The public data repository includes publicly available Amazon review data along with satellite images and other high-value public information. The product works like any search engine, where you enter a query, but instead of searching the web or an enterprise repository, it finds the results in S3 storage on AWS.

The results not only include the data you are looking for, it also includes all of the information around the data, such as Jupyter notebooks, the standard  workspace that data scientists use to build machine learning models. Data scientists can then use this as the basis for building their own machine learning models.

The public data, which includes over 10 billion objects, is a resource that data scientists should greatly appreciate it, but the company is offering access to this data out of more than pure altruism. It’s doing so because it wants to show what the platform is capable of, and in the process hopes to get companies to use the commercial version of the product.

Screen Shot 2019 09 16 at 2.31.53 PM

Quilt Data search results with data about the data found. Image: Quilt Data

Customers can try Quilt Data for free or subscribe to the product in the Amazon Marketplace. The company charges a flat rate of $550 per month for each S3 bucket. It also offers an enterprise version with priority support, custom features and education and on-boarding for $999 per month for each S3 bucket.

The company was founded in 2015 and was a member of the Y Combinator Summer 2017 cohort. The company has received $4.2 million in seed money so far from Y Combinator, Vertex Ventures, Fuel Capital and Streamlined Ventures along with other unnamed investors.


By Ron Miller

Lucidworks raises $100M to expand in AI-powered search-as-a-service for organizations

If the sheer amount of information that we can tap into using the internet has made the world our oyster, then the huge success of Google is a testament to how lucrative search can be in helping to light the way through that data maze.

Now, in a sign of the times, a startup called Lucidworks, which has built an AI-based engine to help individual organizations provide personalised search services for their own users, has raised $100 million in funding. Lucidworks believes its approach can produce better and more relevant results than other search services in the market, and it plans to use the funding for its next stage of growth to become, in the words of CEO Will Hayes, “the world’s next important platform.”

The funding is coming from PE firm Francisco Partners​ and ​TPG Sixth Street Partners​. Existing investors in the company include Top Tier Capital Partners, Shasta Ventures, Granite Ventures and Allegis Cyber.

Lucidworks has raised around $200 million in funding to date, and while it is not disclosing the valuation, the company says it been doubling revenues each year for the last three and counts companies like Reddit, Red Hat, REI, the US Census among some 400 others among its customers using its flagship product, Fusion. PitchBook notes that its last round in 2018 was at a modest $135 million, and my guess is that is up by quite some way.

The idea of building a business on search, of course, is not at all new, and Lucidworks works in a very crowded field. The likes of Amazon, Google and Microsoft have built entire empires on search — in Google’s and Microsoft’s case, by selling ads against those search results; in Amazon’s case, by generating sales of items in the search results — and they have subsequently productised that technology, selling it as a service to others.

Alongside that are companies that have been building search-as-a-service from the ground up — like Elastic, Sumo Logic and Splunk (whose founding team, coincidentally, went on to found Lucidworks…) — both for back-office processes as well as for services that are customer-facing.

In an interview, Hayes said that what sets Lucidworks apart is how it uses machine learning and other AI processes to personalise those results after “sorting through mountains of data”, to provide enterprise information to knowledge workers, shopping results on an e-commerce site to consumers, data to wealth managers, or whatever it is that is being sought.

Take the case of a shopping experience, he said by way of explanation. “If I’m on REI to buy hiking shoes, I don’t just want to see the highest-rated hiking shoes, or the most expensive,” he said.

The idea is that Lucidworks builds algorithms that bring in other data sources — your past shopping patterns, your location, what kind of walking you might be doing, what other people like you have purchased — to produce a more focused list of products that you are more likely to buy.

“Amazon has no taste,” he concluded, a little playfully.

Today, around half of Lucidworks’ business comes from digital commerce and digital content — searches of the kind described above for products, or monitoring customer search queries sites like RedHat or Reddit — and half comes from knowledge worker applications inside organizations.

The plan will be to continue that proportion, while also adding in other kinds of features — more natural language processing and more semantic search features — to expand the kinds of queries that can be made, and also cues that Fusion can use to produce results.

Interestingly, Hayes said that while it’s come up a number of times, Lucidworks doesn’t see itself ever going head-to-head with a company like Google or Amazon in providing a first-party search platform of its own. Indeed, that may be an area that has, for the time being at least, already been played out. Or it may be that we have turned to a time when walled gardens — or at least more targeted and curated experiences — are coming into their own.

“We still see a lot of runway in this market,” said Jonathan Murphy of Francisco Partners. “We were very attracted to the idea of next-generation search, on one hand serving internet users facing the pain of the broader internet, and on the other enterprises as an enterprise software product.” 

Lucidworks, it seems, has also entertained acquisition approaches, although Hayes declined to get specific about that. The longer-term goal, he said, “is to build something special that will stay here for a long time. The likelihood of needing that to be a public company is very high, but we will do what we think is best for the company and investors in the long run. But our focus and intention is to continue growing.”


By Ingrid Lunden

Dropbox overhauls internal search to improve speed and accuracy

Over the last several months, Dropbox has been undertaking an overhaul of its internal search engine for the first time since 2015. Today, the company announced that the new version, dubbed Nautilus, is ready for the world. The latest search tool takes advantage of a new architecture powered by machine learning to help pinpoint the exact piece of content a user is looking for.

While an individual user may have a much smaller body of documents to search across than the World Wide Web, the paradox of enterprise search says that the fewer documents you have, the harder it is to locate the correct one. Yet Dropbox faces of a host of additional challenges when it comes to search. It has more than 500 million users and hundreds of billions of documents, making finding the correct piece for a particular user even more difficult. The company had to take all of this into consideration when it was rebuilding its internal search engine.

One way for the search team to attack a problem of this scale was to put machine learning to bear on it, but it required more than an underlying level of intelligence to make this work. It also required completely rethinking the entire search tool from an architectural level.

That meant separating two main pieces of the system, indexing and serving. The indexing piece is crucial of course in any search engine. A system of this size and scope needs a fast indexing engine to cover the number of documents in a whirl of changing content. This is the piece that’s hidden behind the scenes. The serving side of the equation is what end users see when they query the search engine, and the system generates a set of results.

Nautilus Architecture Diagram: Dropbox

Dropbox described the indexing system in a blog post announcing the new search engine: “The role of the indexing pipeline is to process file and user activity, extract content and metadata out of it, and create a search index.” They added that the easiest way to index a corpus of documents would be to just keep checking and iterating, but that couldn’t keep up with a system this large and complex, especially one that is focused on a unique set of content for each user (or group of users in the business tool).

They account for that in a couple of ways. They create offline builds every few days, but they also watch as users interact with their content and try to learn from that. As that happens, Dropbox creates what it calls “index mutations,” which they merge with the running indexes from the offline builds to help provide ever more accurate results.

The indexing process has to take into account the textual content assuming it’s a document, but it also has to look at the underlying metadata as a clue to the content. They use this information to feed a retrieval engine, whose job is to find as many documents as it can, as fast it can and worry about accuracy later.

It has to make sure it checks all of the repositories. For instance, Dropbox Paper is a separate repository, so the answer could be found there. It also has to take into account the access-level security, only displaying content that the person querying has the right to access.

Once it has a set of possible results, it uses machine learning to pinpoint the correct content. “The ranking engine is powered by a [machine learning] model that outputs a score for each document based on a variety of signals. Some signals measure the relevance of the document to the query (e.g., BM25), while others measure the relevance of the document to the user at the current moment in time,” they explained in the blog post.

After the system has a list of potential candidates, it ranks them and displays the results for the end user in the search interface, but a lot of work goes into that from the moment the user types the query until it displays a set of potential files. This new system is designed to make that process as fast and accurate as possible.


By Ron Miller

Google brings its search technology to the enterprise

One of Google’s first hardware products was its search appliance, a custom-built server that allowed businesses to bring Google’s search tools to the data behind their firewalls. That appliance is no more, but Google today announced the spiritual successor to it with an update to Cloud Search. Until today, Cloud Search only indexed G Suite data. Now, it can pull in data from a wide variety of third-party services that can run on-premise or in the cloud, too, making the tool far more useful for large businesses that want to make all of their data searchable by their employees.

“We are essentially taking all of Google expertise in search and are applying it to your enterprise content,” Google said.

One of the launch customers for this new service is Whirlpool, which built its own search portal and indexed over 12 million documents from more than a dozen services using this new service.

“This is about giving employees access to all the information from across the enterprise, even if it’s traditionally siloed data whether that’s in a database or a legacy productivity tool and make all of that available in a single index,” Google explained.

To enable this functionality, Google is making a number of software adapters available that will bridge the gap between these third-party services and Cloud Search. Over time, Google wants to add support for more services and bring this cloud-based technology on par with what its search appliance was once capable of.

The service is now rolling out to a select number of users and over time, it’ll become available to both G Suite users and as a stand-alone version.


By Frederic Lardinois

Box acquires Butter.ai to make search smarter

Box announced today that it has acquired Butter.ai, a startup that helps customers search for content intelligently in the cloud. The terms of the deal were not disclosed, but the Butter.AI team will be joining Box.

Butter.AI was started by two ex-Evernote employees, Jack Hirsch and Adam Walz. The company was partly funded by Evernote founder and former CEO Phil Libin’s Turtle Studios. The latter is a firm established with a mission to use machine learning to solve real business problems like finding the right document wherever it is.

Box has been adding intelligence to its platform for some time, and this acquisition brings the Butter.AI team on board and gives them more machine learning and artificial intelligence known-how while helping to enhance search inside of the Box product.

Photo: Box

“The team from Butter.ai will help Box to bring more intelligence to our Search capabilities, enabling Box’s 85,000 customers to more easily navigate through their unstructured information — making searching for files in Box more contextualized, predictive and personalized,” Box’s Jeetu Patel wrote in a blog post announcing the acquisition.

That means taking into account the context of the search and delivering documents that make sense given your role and how you work. For instance, are if you are a salesperson and you search for a contract, you probably want a sales contract and not a one for a freelancer or business partnership.

The company launched in September, 2017, and up until now it has acted as a search assistant inside Slack you can call upon to search for documents and find them wherever they live in the cloud. The company will be winding down that product as it becomes part of the Box team.

As is often the case in these deals, the two companies have been working closely together and it made sense for Box to bring the Butter.AI team into the fold where it can put its technology to bear on the Box platform.

“After launching in September 2017 our customers were loud and clear about wanting us to integrate with Box and we quickly delivered. Since then, our relationship with Box has deepened and now we get to build on our vision for a MUCH larger audience as part of the Box team,” the founders wrote in a Medium post announcing the deal.

The company raised $3.3 million over two seed rounds. Investors included Slack and General Catalyst.


By Ron Miller