Sixfold Content

I’m just going to say it: I don’t care how accomplished your team is, they just won’t be able to build a proprietary horizontal LLM to compete, feature-wise, with the GPTs, Geminis, and Claudes of the world. 

Your team may, however, have it in them to build a vertical AI solution to execute specific high-level underwriting tasks. Their solution will probably incorporate one (or even several) aforementioned foundational models complemented with additional components, purpose-made for your specific use case. 

If you haven’t investigated advanced AI for your underwriting tech stack, you’re already behind. The question for carriers has long since moved on from “should we implement?” to “what’s the best way forward?” Some might think it preferable to build a proprietary AI solution using internal resources.

Many larger enterprises are certainly going to take on that substantial challenge. But is this strategy right for your organization? Here are four questions to consider before taking that leap:

1. Do you know what a quality AI-powered solution looks like?

You know how to measure the success of, say, a proprietary Java-powered microservice or web portal. But do you know what metrics to use for a non-deterministic AI solution? It’s a whole new thing.

LLMs are flexible and amazing, but they’re also unpredictable and can get things wrong (even when the end user did everything right). Developing non-deterministic systems requires an evolution in thinking about usefulness and quality control. It means getting acquainted with new concepts like “error tolerance.” 

If you’ve worked with traditional digital systems, you know that when a problem arises, it’s almost always attributable to human error somewhere along the line. LLMs, on the other hand, can do weird stuff when they’re working properly. Ask an LLM the same question 10 times in a row and you’ll get 10 different answers. The key with these solutions isn’t robotic repetition, it’s making sure they provide 10 useful answers. 

Ask an LLM the same question 10 times in a row and you’ll get 10 different answers. The key with these solutions isn’t robotic repetition, it’s making sure they provide 10 useful answers. 

Not only must you anticipate some amount of unpredictability with LLMs, you have to build out an infrastructure to mitigate their impact. This could mean building in extra layers of validation to detect errors. Or perhaps by giving human users the ability to spot errors and give feedback to the system. In some cases, it might mean that we live with some amount of "spoilage," i.e., accepting bad results from time to time.

This is new territory, I know. Are you ready for it? Almost as importantly—would you know how to communicate this new paradigm to the stakeholders who matter?

2. Are you prepared for a relentless pace of change?

Due to LLM’s inherent newness, few engineers or product managers have experience shepherding a vertical AI to market. That means your team must learn to deal with both structured and unstructured data when engaging with LLMs. It means learning the latest prompt design strategies to ensure you're providing consistently accurate answers (and indeed, defining what “accuracy” even means in a non-deterministic system). And it means occasionally having to re-learn it all over again after the next great AI innovation drops.  And a new AI innovation is always about to drop.

Developing cutting-edge vertical AI in 2024 is very different than it was in 2023 and I can promise you, it will be different in 2025.

Developing cutting-edge vertical AI in 2024 is very different than it was in 2023 and I can promise you, it will be different in 2025. Technology moves fast, and at this moment of peak-buzz AI, you have to be prepared for changes to come at your team weekly, if not daily. 

Last year, for example, we were a LangChain shop, as was pretty much everyone else attempting to address big challenges with LLMs. Fast-forward one year and we—and many players in this space—concluded that LangChain just isn’t for production and moved on to building scalable pipelines directly with generative AI primitives. That meant rebuilding some key features from scratch while adding resiliency and scale.

Determination is paramount in the face of rapid change. Are you prepared to hard-pivot a project you’ve been pushing along for months because the ecosystem has irrevocably changed with a new model release, new technique, or newly proposed regulation? Are you prepared to explain the necessity of these sea changes to your team and stakeholders?

3. Are you up on today’s AI regulations? How about tomorrow’s?

There’s a lot of talk in the public discourse about the potential negative impacts of scaled automation. As a result, regulatory bodies at all levels of government have drafted rules for how AI can be implemented, many of which single out consequential sectors such as insurance. 

Technological acumen is crucial, but it could all be rendered meaningless if it doesn’t comply with regulatory requirements. Do you have the infrastructure in place to keep on top of this evolving patchwork of global regulations?

To navigate these choppy waters, you need a team in place to make sure you’re complying with today’s rules, and prepared for tomorrow’s.

What’s better? Getting your team in the conversation with the rule-makers, and help inform the rule sets as they take shape.

4. Can you compete for AI talent?

You have an amazing dev team. They’re driven and passionate, and great colleagues too. I’m sure they could launch a top-notch mini-site in just a few weeks. But have they designed an LLM-powered AI solution before? 

If not, you’ll need to find yourself some AI experts.

That means competing for talent in a limited pool of AI engineers and paying top dollar for it to keep pace with MAMAA-caliber compensation packages.

That means competing for talent in a limited pool of AI engineers (Reuters reports a 50% skills gap in AI roles) and paying top dollar for it to keep pace with MAMAA-caliber compensation packages.

This pool becomes even smaller when looking for talent experienced with building systems for highly regulated industries in general, let alone insurance in particular.

Did you answer “no” to any question above?

I don’t know where you’ll land when it comes to building your vertical AI solution. If the go-it-alone path seems treacherous, then you can always partner with a team that’s been leading the way in the emerging LLM-powered AI for insurance.

I’m not a salesman, I’m a techie, but I can tell you we do great work and our team would love to talk through what you have in mind.

This blog post was originally posted on LinkedIn

Today’s LLM-based AI solutions boast powerful capabilities that just three years ago were only found in science fiction. Modern AIs, driven by advances in machine learning & computational methods inspired by the human brain, continuously gain new capabilities from the data they encounter, enabling them with previously unattainable potential.

However, when it comes to operating within complex, highly regulated sectors like insurance, not any ol’ AI solution will do. In this post, I want to explore why carriers are turning to a new generation of vertical AIs purpose-built to address the industry’s unique needs and challenges.

Horizontal solutions only leverage the Internet’s surface

“Horizontal” LLM-based chatbots (e.g., Open AI’s ChatGPT or Anthropic’s Claude) are competent at a wide range of tasks, but you’d never trust them to execute a consequential insurance underwriting workflow.

Well, I mean, you could. But your underwriters would still need to engage in dozens (or even hundreds) of rounds of prompts, follow-ups, and clarifications to surface the information they need—all of which would require close review & scrutinization for accuracy, compliance, and hallucinations. They'd need to invest time in sorting through pages of answers to find important facts, correlate & de-dupe information, build timelines, and draw relevant connections. After which, they'd have to relate all of these processed facts back to their risk appetite to evaluate the quality of the risk.

Horizontal, multi-use AI solutions deliver little—if any—operational efficiencies for a complex enterprise use case like underwriting. The industry needs something more from its AI.

How vertical solutions overcome the data dilemma

One key area where general-purpose LLM chatbots and wrappers crucially fall short is lack of access to specialized data. A LLM’s “knowledge” can only run as deep as the data it’s been trained on. With a horizontal solution, you’ve generally been limited by what’s publicly available online. To echo a common observation: these models offer “the average of the internet.” They might be perfectly helpful in, say, planning out a keto-friendly dinner for two, but much less so when it comes to assessing risk signals on insurance applications.

In order to be useful in underwriting, a generative AI solution must have been — as table stakes —trained on informative but isolated datasets such as loss histories. Even anonymized versions of these datasets aren’t available for AI training purposes (they can’t even be purchased).

To access this invaluable cloistered data, an AI vendor must cultivate relationships with specialized data gatekeepers and arrive at a precise alignment on use and security. It’d be impractical for a horizontal AI provider to address every possible enterprise niche. To pry these data doors open, you need highly specialized vendors with a singular industry focus.

Vertical solutions: a partnership of insurance nerds and tech geeks

Beyond special data access, a vertical AI solution is designed to address the highly specific needs of its sector. The complexity and regulations inherent to insurance underwriting require a team that is as well-versed in emerging tech as they are in long standing carrier challenges. 

A vertical AI solution likely incorporates a medley of intelligent tools under a single platform umbrella. A foundational LLM, for example, may be tapped for specific functions (e.g., summarization), but higher-level capabilities can only be achieved when the LLM is partnered with purpose-built functionality dedicated to specific tasks (e.g., external data APIs, vector stores, etc.) The solution’s precise structure must be guided by experts with an intimate understanding of today’s industry challenges—and an eye on the ones soon to be in effect.

Keep your eye to the verizon

Horizontal AI solutions are amazing, but they fall short in core underwriting tasks due to their shallow expansiveness; lack of access to specialized data; and ultimately the fact that they’re just one building block that must be partnered with industry-specific capabilities to deliver value to carriers. 

This article was originally posted on LinkedIn

Just 12 months ago, we introduced Sixfold and its vision to the world. Today, we’re thrilled to announce a $15 million Series A investment led by Salesforce Ventures with participation from Scale Venture Partners and our initial seed investors Bessemer Venture Partners and Crystal Venture Partners.

With these additional resources, Sixfold will expand our already exceptional team of engineers to further enhance our products and accelerate our R&D efforts. On the business & operations side, we will broaden our offerings and grow our footprint beyond North America to the United Kingdom and European Union. 

Most importantly, this new capital injection will advance our mission to overcome today’s most pressing underwriting challenges, not by iteration, but by building a new end-to-end risk analysis paradigm. That’s a big statement, I know. But we didn’t pick the name “Sixfold” out of modesty.

Year one of building insurance’s most consequential tech

In our inaugural blog post, I pledged that Sixfold would “focus on one of the most intractable challenges in insurance: the inefficiency of underwriting” and our team has backed up this promise with a year’s worth of accomplishments. 

We’ve developed a patent-pending AI that rapidly translates carriers’ unique underwriting guidelines into digital risk models, regardless of what form those guidelines take. This is Sixfold’s superpower, but far from our only bit of AI magic.

Leveraging 10 proprietary models, our platform surfaces appetite-aligned risk signals from disparate sources and independently “connects the dots” to generate natural language summarizations and recommendations. All our hard engineering work has resulted in unquestionable business value. In just one year, Sixfold has boosted underwriting capacity for our customers by a factor of 10, accelerated data collection by 2,000x, sliced submission-to-quote cycles from hours (sometimes weeks) to mere minutes, and pushed our platform accuracy to an industry-leading 94% so our customers can precisely assign NAICS/SIC codes at scale.

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Sixfold is uniquely posed for continued growth with top-tier global partners in the year to come. We were named a winner of The 2024 Zurich Innovation Championship, the industry’s largest open innovation contest. As one of only 9 winners selected from more than 3,000 global applicants, our team will participate in the Championship’s elite accelerator program to develop a new commercial underwriting solution.

Our Innovation Championship win was a powerful validation of Sixfold’s approach from a storied industry leader, and it wasn’t even the only one in 2024—in March, Sixfold was selected for the exclusive Lloyd’s Lab accelerator program (out of their largest-ever pool of applicants). Our team is currently working with Lloyd’s to develop innovative solutions for the world’s leading insurance and reinsurance marketplace, which will be invaluable as Sixfold expands into the UK and beyond.

Writing the next chapter in underwriting

Over the past year, we’ve grown from a team of three to a staff of 18, with more additions to come. In the near term, we’re focused on bringing on seasoned tech leaders to guide our continued success, as exemplified by the recent addition of Ian P. Cook, PhD, Sixfold’s first full-time head of AI. By adding select talent to our elite team, there’s no underwriting challenge we won’t overcome. 

I’m ridiculously proud of what this team has accomplished and excited about everything we will accomplish utilizing this latest fund round as a jumping-off point. 

I want to thank Laura Rowson, Nowi Kallen, and the rest of the Salesforce Ventures team for seeing the unique potential of our vision, or as Laura generously put it in our press release, for seeing Sixfold “as a company capable of transforming the insurance industry.” We’re beyond excited to work with Salesforce Ventures to accelerate our growth and materialize those changes.

And, of course, a huge thank you to Alex Niehenke at Scale Venture Partners and for continued support from our initial backers Charles Birnbaum & Jeremy Levine at Bessemer Venture Partners and Jonathan Crystal & Stephen McGovern at Crystal Venture Partners. 

If you haven’t yet had a chance to see what our platform can accomplish, there’s no better time to get started. Reach out for a personalized demo. We can’t wait to show you the future of underwriting.

This article was originally posted on LinkedIn

I’m thrilled to announce that Sixfold has been named a winner of this year’s Zurich Innovation Championship! Since 2018, Zurich Insurance Group has overseen an annual “collaboration program” with select startups from around the world to develop new offers and services for their customers. The yearly Championship has rapidly expanded to become the industry’s largest open innovation contest with thousands of applicants from over 30 countries resulting in more than 50 active initiatives implemented through Zurich Insurance’s global business. 

‍Celebrated for Pioneering Innovation

Sixfold was only one of nine teams selected out of a pool of more than 3,000 global applicants to take part in the accelerator portion of the Championship. Our team will take on the “Commercial Insurance” challenge to engineer technical solutions that “improve transparency and accountability, enhance risk management capabilities, and foster sustainability transition through a culture of trust and innovation.”

For the next four months, our team will collaborate with leaders from Zurich North America (ZNA) to build AI-powered risk analysis & summarization solutions to augment underwriter workflows through the automation of high-volume (but not necessarily high-value) tasks.

Improving Insurance Efficiencies

Unlike traditional accelerator programs, which focus on product development, the Innovation Championship aims to accelerate the adoption of a new solution within Zurich Insurance’s global business. We’re thankful to the leadership at Zurich Insurance for believing in Sixfold’s mission and our team, and we look forward to building an amazing solution that results in improved efficiencies that benefit Zurich’s customers, insurance brokers, and other stakeholders. 

This collaboration comes only two months after Sixfold’s selection to participate in the 12th cohort of Lloyd’s exclusive accelerator program, Lloyd’s Labs, which kicked off in April and we will be demoing in July. 

We’ve only just begun Sixfold’s second year — and it’s turning out to be a BIG one!

This article was originally posted on Linkedin

I’m beyond excited to announce that Sixfold has been officially selected to take part in the 12th cohort of Lloyd’s InsurTech accelerator program, Lloyd’s Lab. 

Lloyd’s Lab was recently recognized as a top-25 European start-up hub by the Financial Times and Statista and ranked the very top insurance-focused accelerator out of 19 countries and +2,000 organizations.

The program will give our team the opportunity to collaborate with Lloyd’s mentors to develop innovative solutions for the world’s leading insurance and reinsurance marketplace. Over the course of the 10-week “fast-track, fast fail” program kicking off in late April, our team will build, test, and iterate innovative solutions that “challenge how we do things and help the Lloyd’s market better serve its customers.”

Sixfold was one of just 22 insurtechs invited to travel to London to take part in Lloyd’s “Pitch Day” event. This year saw the largest-ever application pool for the program—Pitch Day invitees had to be culled down from more than 250 applications submitted by insurtechs spanning 33 different counties. 

For our pitch, I showcased how much we have built to serve underwriters in the past 10 months to 1,000-plus virtual and in-person attendees from across the Lloyd’s market ecosystem. We were selected into the program as one of only 12 teams by a panel of market leaders, mentors, and the Lab team, and we were the only underwriting solution to be accepted into the program! 

Members of Sixfold’s product and design teams will spend 10 weeks working out of the iconic Lloyd’s building in London. We will focus our efforts on accelerating and optimizing triage and risk appetite match capabilities within the scale of Lloyd’s markets. 

We’ll demo the fruits of our accelerator labor to the entire Lloyd’s market in early July—stay tuned for details!

Lloyd’s Lab unique approach to fostering global innovation is helping to tackle some of the world’s biggest insurance challenges. Progressing the Lab’s mission of supporting innovative insurance solutions across the globe, this latest cohort focuses on developing solutions to some of the biggest risks faced by businesses and communities in the Americas such as challenges arising from natural hazard prediction to risks associated with cybersecurity.

Thank you to Lloyd’s for believing in our vision and helping us bring it to fruition. We can’t wait to show the entire Lloyd’s market—and the rest of the world—what we have in store.

This article was originally posted on Linkedin

This time last year, Sixfold was little more than a name and a vague concept. But since officially launching in May, this remarkable team (now 17-strong and growing!) has revolutionized insurance underwriting. That’s a bold statement from a biased observer, but I think I can back it up. 

First, let’s briefly explore the state of affairs coming into 2023.

Modern carriers process vast amounts of data from a wide array of sources to inform underwriting decisions. Today’s competitive advantages are secured—or lost—based on the efficiency and accuracy with which one handles this data. 

Over the years, multiple data-tech vendors have promised to help carriers keep pace, but they haven’t been even close to sufficient. Meanwhile, data ecosystems have grown more expansive and the tools gap, more glaring.

At Sixfold, we saw this challenge compounding by the day—not for lack of trying, but for lack of imagination. It’s what inspired us to develop a new approach that prioritizes transformation over iteration.

Our platform was uniquely built to accelerate the bewilderingly complex process of modeling risk appetite, no matter the starting point. Is your risk tolerance detailed in a loose assortment of PDFs? An Excel document? Or just a bundle of past submissions? We can make sense of it all. And that’s just the start. Our proprietary generative AI engine ingests applicant data from disparate sources at scale and—unlike traditional “intelligent” data processing tools, which merely extract data—generates clear summaries, fully aligned with carriers’ appetites, empowering underwriters to move with unprecedented accuracy and speed.

And it’s been effective. Ridiculously effective. Let’s run through a few quick examples:

✅ A leading general liability carrier was averaging 4 hours for its submission-to-quote cycles, but once they implemented Sixfold, that time was slashed to just 4 minutes.

✅ Last year, a large global cyber carrier measured its submission-to-quote cycles in weeks; now they do it in 3 minutes and 24 seconds.

✅ Before adding Sixfold to its tech stack, a major life & health carrier needed days to extract, surface, and package relevant data from multiple sources for a single life insurance application. Now the entire process is handled automatically in a fraction of the time.

Perhaps you can see where the industry is headed and why this is the area where Sixfold is focusing.

The journey from there to here

The progress our engineering team has made over this not-even-a-full year has been nothing short of astonishing. Gen AI is moving forward at warp speed–and Sixfold is moving even faster. 

In just the past few months we’ve transitioned from relying on a single LLM vendor to tapping and training a plethora of platforms based on their unique abilities. Additionally, we’ve tailor-built our own proprietary AI models to increase speed, accuracy, and privacy—we’ll be tripling down on our internal R&D efforts in the years ahead. (As a side note: I can’t wait to show you what our engineers have been working on…more on that during our January webinar.)

The only thing evolving faster than AI technology is society’s views of it. More people are voicing concerns about the potential negative impact of AI, particularly around issues of scaled bias. We get it. We have a shared interest in ensuring that AI is deployed with a human-first approach. 

Sixfold has been proactively and uniquely engaged in the conversation. I, and other Sixfold leaders, have repeatedly met with the state regulators and commissioners throughout the year to better understand their concerns and thinking. These meetings have helped us design our platform in anticipation of new regulations and, conversely, offer our unique insights to influence the formation of emerging rules that will allow AI to work better for everyone.

Hello, 2024

We’re exclusively obsessed with insurance underwriting. We have been from the beginning and we will continue to be in the future.

I’m beyond proud of what this team has accomplished over the past 7 months, and I can’t wait to share with you what’s in store in 2024. Join us this coming January for our first-ever virtual session, Boost Underwriting Capacity in 2024: Discover the Sixfold Impact, for a demo of how our platform enhances underwriting capacity in P&C, Life, and Specialty insurance sectors.

None of this would have been possible without the support and backing of Charles Birnbaum and Jeremy Levine at Bessemer Venture Partners, and Jonathan Crystal and Stephen McGovern at Crystal Venture Partners. We’re grateful for our bold customers and partners who understood our vision and joined us on the first leg of our journey. And of course, I can’t say enough about the Sixfold team including my co-founders Jane and Brian as well as our growing lineup of researchers, innovators, and visionaries: Brooke, Drew, Emil, Gregg, Ian, Lana, Laurence, Leonardo, Lucas, Maja, Marie, Omeed, Stewart, and Ryan.

Don’t miss our 2023 recap. See you all next year!

This post was originally published on LinkedIn.