Sixfold Content

Yesterday, Sixfold hosted an event at Lloyd’s in London on the future of underwriting work.

The event started with a keynote from Dr. Naeema Pasha on the future of work across industries, along with findings from her research with Allianz. One of the key takeaways was that the future of insurance still needs to be focused on people. With AI becoming more present, the question is how to build trust, support empathy and skills development, and design roles for the next generation of talent.

“What stood out in my research with Allianz was how much people in the insurance industry care and are passionate about the industry and its future.“

─ Dr. Naeema Pasha, Researcher, Author and Writer

There was also a great question from an aspiring underwriter asking what skills are needed today. Soft skills came up a lot, things like curiosity and adaptability, but also something simple: get out there, engage with people in the industry, and ask questions.

At one point, the audience was asked if they would let a robot cut their hair. Most people wouldn’t. It’s a simple example, but it comes back to trust.

The panel discussion featured Simon Parris, CUO at Victor Insurance; John Enright, COO at Berkley; and Raoul Carlos, Founder & CEO at Torch Underwriting, and focused on whether this is the last generation of traditional underwriters.

They started by sharing where they are today when it comes to AI implementation. A clear theme was that adoption and engagement from underwriters really matters. Agentic AI is moving fast with a lot of potential, and there was a lot of discussion around how AI can improve broker relationships and risk assessments as a whole. Raoul talked about building AI into their foundation from day one, including the data layer needed to support institutional intelligence over time.

When it came to impact of AI, the conversation went beyond speed. Things like memorable engagement with brokers, pricing power through stronger relationships, more personal service, net promoter score, and the ability to think outside the box all came up. Better service and better products as well.

On hiring the future workforce, Raoul mentioned looking for curiosity and passion. Where the role used to be heavily focused on data wrangling, today it is much more about judgment, adaptability, creativity, and critical thinking. John highlighted integrity, trust, relationship skills, and the ability to work with a new toolkit. Simon mentioned market connections, strong underwriting fundamentals, and openness to new technology.

“The mechanics of how we work are changing, but this has happened hundreds of times throughout history.”

─ Raoul Carlos, Founder & CEO @Torch Underwriting

When asked what the underwriting role of the future might be called, the panel largely agreed it is still underwriting. Portfolio underwriting was mentioned, as well as next generation underwriter, but the core remains the same. From the audience, there were also questions around how to enter the industry. It is not just about academics. Apprenticeships matter. Continuous learning matters. And building teams with different perspectives and backgrounds still really matters.

On dealing with skepticism internally around AI, it was acknowledged that change can be uncomfortable. The advice was to make it part of the conversation, share examples, and host workshops. For insurance executives looking to learn more about AI, the message was to experiment and try it firsthand. Get into vibe coding, step outside of the comfort zone, and understand the opportunities by actually using the tools.

Lastly, Gianfilippo Giannini, Global Technical Coordinator Cyber Risk at Generali GC&C, took the stage for a Q&A and shared why they started looking for an AI vendor in the first place. They were operating in a soft market and needed to handle more submissions with the same headcount.

He also highlighted the importance of bringing underwriters in from the very beginning. The decision to choose Sixfold came down to security, privacy, a strong responsible AI framework, and a future proof roadmap, but also that the solution was clearly built for underwriting.

“Sixfold spoke the same language as us.”
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─ Gianfilippo Giannini, Global Technical Coordinator Cyber Risk @Generali GC&C

There was some early skepticism from users, but once underwriters saw how it supported their day to day work, feedback quickly became very positive. At one point, underwriters who were not part of the pilot started asking when they could start using Sixfold as well.

Looking ahead, the focus for Generali GC&C when it comes to AI in underwriting is on data driven underwriting, with better visibility into portfolio trends and broker success rates.

Most insurers have run an AI pilot. Far fewer have scaled one. At a recent Sixfold webinar, two carriers shared what it actually took to get there, and what production at scale really looks like.

Amy Nelsen, Head of UW Operations for US Middle Market at Zurich North America, and Jim Mormile, President of Professional Lines at Skyward Specialty, spoke about their experience rolling out Sixfold across their organizations.

Skyward is now live across more than 10 product lines with nearly 100 underwriters. Zurich has deployed across 30 US offices, with more than 200 underwriters using Sixfold in their daily workflow.

We walked through our five-step scaling framework with both of them to hear, in their own words, how each stage played out in practice.

Step  1: Pick Your Focus

The biggest mistake teams make is starting with AI and working backwards to find a problem. Both Amy and Jim were deliberate about doing the opposite, finding a genuine pain point first and then asking whether AI could solve it.

For Skyward, the problem was the triage wall. Underwriters were spending hours manually working through submissions just to determine basic appetite fit.

For Zurich it was starting with a use case which underwriters didn’t like doing: documentation. 

"We had a healthcare risk where the underwriter got about a 75-page submission. It wasn't until page 56 to 59 that they realized the submission should not be covered since it was out of appetite."
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─Jim Mormile @Skyward Specialty

Step 2: Prove It Works

Before scaling, you need a few signals that it's actually working, but that doesn't always come from a dashboard. Both Jim and Amy found that meaningful early indicators were user feedback.

For Jim, it was a veteran underwriter pulling him aside unprompted.

"The anecdotal evidence that really made us realize it was working: it was a veteran underwriter that came up to me and said, '[Sixfold] actually changed my day. It sped up my work progress and workflow'"
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─Jim Mormile @Skyward Specialty

The follow-up signal was equally telling “underwriters stopped asking whether they should use Sixfold and started asking when it was coming to their line of business.” That's when Skyward knew it was time to expand.

Step 3: Map Your Expansion

Scaling AI isn't a single rollout but it's a series of decisions about sequencing, readiness, and change management. Both organizations took a structured approach, but in different ways.

Skyward scored each of their 16 lines of business across criteria like guideline robustness, process standardization, underwriting complexity, and, critically, how tech-forward the underwriting team was. They then hand-picked early adopters rather than opening the floodgates.

Zurich took a geographic approach, piloting across four offices before expanding countrywide, and intentionally included underwriters at different levels of tech comfort so they could anticipate resistance before it became a problem at scale.

Both teams also addressed job security fears head-on.

"It's less about the underwriting role becoming irrelevant, and more about if you handle a $10M book of business versus a $20M book of business."
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─Amy Nelsen @Zurich North America

Jim's approach was to be explicit about what AI wouldn't do: no automated decision-making, no replacing the underwriter's final judgment. The framing was always about giving underwriters better information, not replacing their expertise.

Step 4: Roll Out in Phases

Both teams learned that trying to do too much too fast creates fatigue, and fatigue kills adoption. Skyward actually hit pause on one line of business after underwriters started showing signs of frustration. Rather than pushing through, leadership made the call to step back.

A few months later, that team came back ready to re-engage, pulled in by the FOMO of watching other lines of business benefit.

On the flip side, Skyward's phased approach led to real efficiency gains in deployment speed. Their first two lines of business took 12 to 14 weeks from introduction to production. By the time they were rolling out subsequent lines, they'd cut that down to eight weeks.

Amy's experience at Zurich echoed the same takeaway on speed:

"The most amazing part is how fast you can get from just a concept to deployment with Sixfold."
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─Amy Nelsen @Zurich North America

Step 5: Assess Impact

Once AI is live in production, measuring success means looking at two things in parallel: business outcomes and output quality. Neither alone tells the full story.

For Jim at Skyward, that meant tracking time to quote and number of quotes out the door, but also running a monthly accuracy review and a sentiment score across underwriting teams.

"We constantly look at both ends of the spectrum: the ROI metrics, and whether the accuracy is there to give our underwriters the information they need to make better decisions."
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─Jim Mormile @Skyward Specialty

Amy's approach at Zurich was similar, and revealing in its simplicity. Sixfold's success isn't measured separately from the business; it's measured through the business. When AI becomes embedded enough that you stop evaluating it as a standalone tool and start measuring it through your core business metrics, that's when you know it's really working.

"We're measuring our business outcomes like how many quotes underwriters are getting out the door and how much business have they bound. We also meet with Sixfold every month to look at quality metrics."
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─Amy Nelsen @Zurich North America

The Final Wisdom 

Scaling AI in underwriting isn't primarily a technology challenge, it's a people and process challenge. Both Amy and Jim closed with the same underlying message: be preapred for that thing change quickly in the world of AI, don't be afraid to fail, and don't stay stuck in proof-of-concept mode forever.

The insurers that will succed at scaling AI are the ones that move deliberately, learn fast, and bring their underwriters along for the ride from day one.

Watch the video recording of the entire webinar here.

In 2023, Brian, Jane, and I set out to build AI for underwriting. Not as another helper tool, but as the foundation for how underwriting should actually work going forward.

At the time, a few people thought we were crazy. But we believed underwriting was ready. And we believed underwriters deserved better.

That idea has now become real.

Today, I’m excited to share that Sixfold just raised a $30M Series B, led by Brewer Lane, with strategic investment from Guidewire, and continued support from Bessemer Venture Partners and Salesforce Ventures.

From Idea → The AI Underwriting Brain

Fast forward, we’ve built what we originally set out to build:

An underwriting AI brain that global carriers rely on every day.

Sixfold is already trusted by insurers like Zurich North America, Skyward Specialty, Guardian, Generali GC&C, AXIS, and Mosaic.

We’ve processed over 1 million submissions, across 40+ lines of business, supporting insurers representing $265B in gross written premium.

Sixfold is live across North America, the UK, Europe, Latin America, and Australia, underwriting risks across Property & Casualty and Life & Health. Production underwriting at global scale.

AI underwriting isn’t coming someday. It’s here, and Sixfold is leading it.

Underwriters Run the Book, Not the Tasks

We’ve already started to see the shift. Underwriters are no longer spending their days processing information. Instead, they’re making decisions. AI agents execute the work end-to-end. Humans focus on judgment, portfolio performance, and market opportunities.

Or put more simply:

Underwriting operations run on Sixfold. People run the strategy.

That’s the AI Underwriter.

Why Customers Love Sixfold

One of the biggest reasons Sixfold has scaled so quickly is that we don’t ask insurers to rip and replace everything.

Sixfold integrates directly into the tools underwriters already live in. Their workbenches, policy admin systems, existing workflows, and, of course, email inboxes.

Near-zero time to value isn’t a slogan. It’s how we’ve earned trust across the industry.

This Isn’t Theory — It’s Happening Now

What makes this moment exciting is that it’s no longer hypothetical.

Customers are already seeing major impact:

• Skyward deployed Sixfold across 11 underwriting teams and cut quote response time by 35%
• Zurich rolled Sixfold out to 200+ underwriters, saving up to 2 hours per submission

And the real win is what happens next:

• Underwriters are happier because they get time back
• Carriers respond faster with better quotes and better coverage
• Agents and brokers prefer working with those carriers
• Teams win more business and select risk more intelligently
• Competitive advantage starts showing up in combined ratio

Sixfold becomes the underwriting tool you can’t live without.

That’s what “AI transformation” looks like when it’s real.

Why Series B Now

We’ve proven autonomous AI works in real underwriting environments. Now it’s time to scale.

This funding lets us go faster on what matters most:

• More autonomous underwriting agents
• Deeper integrations into carrier workflows
• Portfolio-level visibility
• Global expansion across markets where demand is already there

Underwriting remains the most complex and important function in insurance.

We’re rebuilding it with AI at the core and underwriters in control.

Bring on even more #Funderwriting!

Think post was originally posted on LinkedIn

Sixfold was featured on the big screen at Open AI’s DevDay 2025, recognized for surpassing 100 billion tokens processed. To make it even better, Senior AI Engineer, Drew Das, was honored among global developers for his contributions.

This is a huge milestone for Sixfold, a reflection of the real impact our AI is making in underwriting. But what does 100 billion tokens actually mean in practice? We sat down with Drew to find out.

Can you start by explaining what exactly OpenAI recognized Sixfold for at Dev Day, and what it felt like to see your name up on the screen?

Seeing my name on stage felt like public validation of the engineering team’s hard work. Sixfold is operating at the frontier of large-scale AI in insurance underwriting. We’re not a pilot anymore; we’re in production.

It also hit home the responsibility that comes with it. Yes, we’ve built scale, but now we have to make sure what we produce is high quality and meaningfully used. Sixfold isn’t experimenting with LLMs; we’re running them reliably where accuracy, latency, and auditability really matter.

We’re not a pilot anymore; we’re in production.

What does “100 billion tokens” actually represent in Sixfold’s day-to-day work?

Each token is a fragment of understanding, a piece of text, data, or context that our models interpret and turn into something underwriters can act on.

It signals the volume of underwriting data, documents, broker submissions, and risk information that our platform analyzes and processes through generative AI workflows.

Hitting 100 billion means we’ve moved many workflows off manual underwriting review and into AI. When repetitive work is reduced, underwriters spend more time on relationships, strategy, and higher-value decisions. 

Hitting 100 billion means we’ve moved many workflows off manual underwriting review and into AI. When repetitive work is reduced, underwriters spend more time on relationships, strategy, and higher-value decisions. 

So… what’s next, another 100 billion tokens?

Token count is becoming table stakes. What matters now is how those tokens are applied and the outcomes they create. 

We’re focusing on deeper workflow integration, bringing AI agents that go beyond risk assessment to support decisions, automate workflows, and deliver real-time underwriting intelligence. Our Research Agent is a great example of that.

Considering the high-stakes nature of insurance, every risk insight and decision generated by Sixfold must be auditable and traceable. Our solution isn’t just built for scale, but for governance.

On the engineering side, what does it take to run something at that scale?

From an engineering perspective, scale brings complexity. We’re constantly balancing latency, cost, and accuracy.

Many AI projects stop at the “cool demo” stage, but we’re pushing through the messy engineering: hybrid search, re-ranking, prompt tuning, and evaluation,  all happening under the hood.

Scaling means better signals, more edge cases surfaced, and faster model learning. It’s a flywheel that keeps getting smarter.

Scaling means better signals, more edge cases surfaced, and faster model learning. It’s a flywheel that keeps getting smarter.

Implementing new AI technology in your underwriting workflow can be a challenge. Between IT team bandwidth and integration complexities, getting a new workflow up and running smoothly takes time.

At Sixfold, we’re all about making underwriters’ lives easier and speeding up the quote process.  So, of course, we want to make implementing our platform just as smooth and just as fast.  One way to do that? Partner with experts who know operations & technology inside and out. That’s where Mphasis comes in.

Meet Mphasis

If you haven’t heard of them, think of Mphasis as a company that helps insurance carriers streamline operations, integrate technology, and make their day-to-day workflows simpler. Basically, they’re the experts at making complex systems work together, which makes this partnership a win-win for everyone involved.

Mphasis will integrate Sixfold’s platform to help carriers deliver faster, more confident quotes by giving underwriters contextual risk insights tailored to each insurer’s unique appetite.

“Mphasis is excited to partner with Sixfold to accelerate AI adoption in the insurance industry. By leveraging Sixfold’s AI expertise, Mphasis enhances its insurance technology capabilities to deliver advanced, data-driven automation solutions for global insurers, driving efficiency, accuracy, and innovation across the insurance value chain.”
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- Nitin Rakesh, Chief Executive Officer and Managing Director, Mphasis

When it comes to new tech, speed to value matters and no one wants a slow start. With Mphasis, we’re making sure insurers can start seeing value from their Sixfold integration faster.

This partnership is about improving insurance underwriting and speeding up implementation, enabling insurers to scale quicker and stay ahead of the challenges in today’s market.

Read more about the partnership on Yahoo Finance, Life Insurance International and Fintech Finance.

At Sixfold, we always integrate the latest AI advancements, but only when they truly help make underwriting faster, easier, and more accurate. One of the most promising technologies we’re exploring right now is Model Context Protocol, or MCP. Curious why? Read on. 

What Is MCP? 

MCP is a way for different AI models, and the agents (read about agents here) that use them, to talk to each other.

Think of it like this: instead of manually connecting different systems when you want to share data, MCP lets one AI model pull context from another in real time, seamlessly and instantly.

Basically, instead of teaching every AI everything, you teach each one what it’s best at, and they learn to ask each other for help.

Who’s Behind It?‍

Since its introduction, MCP has gained traction among major AI providers:​

Anthropic: The creator of MCP in November 2024, Anthropic has integrated the protocol into its Claude family of language models, enabling them to interact seamlessly with external systems. 

OpenAI: In March 2025, OpenAI announced support for MCP across its Agents SDK and ChatGPT desktop applications, facilitating broader adoption of the protocol. ​

Google DeepMind: Shortly after OpenAI's announcement, Google DeepMind confirmed MCP support in its upcoming Gemini models and related infrastructure, highlighting the protocol's growing industry acceptance.

...and many more! It’s not just the AI giants. Tools like Linear, Zapier and Atlassian are jumping on board. Signaling that MCP is becoming foundational infrastructure, not just something for the leading LLMs, but for the everyday tools teams use to get work done.‍

MCP + Underwriting AI?

Why is MCP relevant for AI underwriting technologies? Today, many insurers have their own internal AI tools. MCP basically turns all these isolated AI models into a connected ecosystem. 
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Here’s an example:

1. Let’s say a carrier has its own internal ChatGPT-like app as well as Sixfold's AI risk assessment solution.

2. With MCP, an underwriter using an internal chat tool, for example, can simply ask a question “What’s the risk appetite score for this customer named Gamehendge Widgets?”

3. Their AI doesn’t need to know everything itself, it just knows who to ask, in this case - Sixfold. It reaches out to Sixfold’s models, gets the answer, and serves it up directly to the underwriter.

No complex integration projects. No heavy lift for IT. MCP would act as a seamless bridge between Sixfold’s risk assessment expertise and the additional AI tools underwriters are using. 

Why Sixfold Cares (a Lot)

Right now, almost everyone is talking about "agentic" behavior, how AI agents plan and reason independently. But the thing is that MCP is the quieter, more practical sibling: it’s about getting the right data in the right place, way faster than before.

The impact MCP can bring: 

• It can meet underwriters where they are today -  inside the tools they already use every day
  
  
• Enables flexible adoption where carriers can pull in just the capabilities they want, without a massive rollout
  
  
• It’s a leap forward in making underwriting AI more accessible and useful

At Sixfold, we’ve made MCP connections between our models and other systems, We’ve, and also exposed internal tools where AI chat assistants can query Sixfold’s underwriting insights. 

What to Watch Out For

Like any new tech, MCP isn’t perfect. There are a few important risks to keep in mind:

Security:

• MCP is pretty quiet on the security mechanisms with which connected systems lock down their data. Existing enterprise-grade methods that companies like Sixfold use to protect sensitive customer data will need to be considered and implemented.
  
  
• Malicious tools could hide bad instructions if care is not taken on how different models talk to each other. 

Accuracy: 

• Incorrect or messy data leads to bad decisions. AI pulling data quickly doesn’t mean it’s always right, double-checking and validation are key.
  

What’s Next with MCP?

MCP done right (e.g. secure, validated and tested over and over) could be a kind of quiet but transformative technology that will make AI in insurance not just smarter, but more practical.

From many steps to one: MCP collapses complex processes into simple interactions.

From heavy integrations to light connections: Carriers can plug into any AI expertise easily.

From standalone tools to connected ecosystems: Underwriters get the best of all worlds, without even noticing the heavy lifting happening in the background.

Emerging protocol: Agent-to-Agent (A2A). A2A was launched by Google in April 2025 and has already gained support from Microsoft. The protocol addresses the need for AI agents, often developed on disparate platforms, to communicate and collaborate. Even with certain similarities, MCP and A2A work well together instead of competing. Google says that A2A is meant to go alongside Anthropic’s MCP.

As more organizations roll out MCP and A2A, AI assistants and agents are getting way better, more up-to-date, more capable, and way more useful in the flow of work.

The future of AI in underwriting isn’t just about smarter models, it’s about models that collaborate and talk to each other.
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This article was originally posted on LinkedIn