Sixfold Content

October 18, 2024 - Sixfold, the AI solution designed to streamline end-to-end risk assessments for underwriters, announced its partnership with AXIS, a global leader in specialty insurance and reinsurance, a collaboration that has yielded positive results in its initial rollout. Within the first month of deployment, AXIS underwriters leveraged Sixfold’s solution to improve efficiency, accurately classifying businesses and aligning cases with their risk appetite.

“This partnership is all about leveraging AI to empower our underwriters and even further enhance the service we provide to our customers. We were searching for a solution that could reliably deliver precision, and Sixfold has done just that and more.

The real game-changer has been the time savings—freeing up valuable hours so our underwriters can zero in on the work that drives results while ultimately benefiting the customer” said Josh Fishkind, Head of Innovation at AXIS.

“Our goal is to provide meaningful ROI for all our customers, and AXIS has already begun to see these benefits,” said Alex Schmelkin, Sixfold's Founder & CEO. “We look forward to continuing our partnership as AXIS discovers more ways Sixfold can enhance their underwriting processes.”

Read the full customer story here and check out the Insurance Post article covering our work with AXIS.

In 2025, the cyber risk landscape is expected to become more complex with increasing threats driven by rising privacy violations, data breaches, the rise of AI, and external factors such as emerging regulations. According to Munich Re, the cyber insurance market has nearly tripled in size over the past five years, with global premiums projected to surpass $20 billion by 2025, up from nearly $15 billion in 2023, as reported by CyberSecurity Dive.

Reflecting the rapid market growth and emerging threats, Sixfold has seen increased demand from specialty insurers in the cyber sector and has successfully brought on several industry leaders as customers.  "In the near future, cyber policies will become as essential as General Liability or Property & Casualty coverage. Given the world we live in, this shift is inevitable. Cyber policies are poised to become the most specific and highly customized policies available" said Jane Tran, Co-founder & COO at Sixfold.

"In the near future, cyber policies will become as essential as General Liability or Property & Casualty coverage. Given the world we live in, this shift is inevitable. Cyber policies are poised to become the most specific and highly customized policies available"

Empowering Underwriters to Quickly Adapt to New Cyber Risks

As cyber risks grow, the pressure on underwriters to assess risks accurately and expedite the case review process continues to increase. Sixfold’s AI solution for cyber insurance addresses these challenges by securely ingesting each insurer’s underwriting guidelines and aggregating all necessary business information to quickly provide recommendations that align with the carrier’s risk appetite. This capability allows insurers to quickly adjust their risk strategies in response to new cyber threats.

“With Sixfold, insurers can synchronize their underwriting guidelines across the board and adapt quickly. For example, when a new malware threat is identified, you can instantly incorporate it into your risk criteria through Sixfold. This ensures that the entire cyber team factors it into their assessments immediately without needing to learn every detail or the threat or spending hours digging for the right information” said Alex Schmelkin, Founder & CEO of Sixfold.

Besides, effective cyber underwriting demands deep expertise in IT systems, cybersecurity measures, and industry developments. This need for specific expertise presents a significant talent issue for insurers, especially with 50% of the underwriting workforce set to retire by 2028. Sixfold bridges the knowledge gap by instantly providing underwriters with the specialized knowledge they need for accurate risk assessments. 

“Underwriters no longer need to be cyber experts; they can rely on Sixfold to spotlight the critical information needed for accurate underwriting decisions. Our platform simplifies the complex world of cyber risk and empowers underwriters to make more confident decisions, faster” said Jane Tran, Co-founder & COO at Sixfold.

Sixfold Partners with CyberCube to Enhance Cyber Risk Assessments

‍Sixfold has teamed up with CyberCube, the world’s leading analytics provider to quantify cyber risk. This integration of CyberCube's advanced cyber risk analytics with Sixfold's AI underwriting solution enables insurers to achieve faster and more accurate risk assessments. The partnership enhances underwriting efficiency, strengthens regulatory compliance, and offers highly tailored cyber insurance solutions, empowering insurers to stay ahead of the rapidly evolving cyber threat landscape. "The partnership between CyberCube’s comprehensive cyber data and Sixfold’s innovative risk assessment is setting a new standard for the future of underwriting, keeping insurers prepared for new challenges in determining accurate cyber policies.” said Ross Wirth, Head of Partnership and Ecosystem for CyberCube.

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"The partnership between CyberCube’s comprehensive cyber data and Sixfold’s innovative risk assessment is setting a new standard for the future of underwriting, keeping insurers prepared for new challenges in determining accurate cyber policies.”
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To see our Sixfold speeds up the cyber underwriting process join our upcoming live product demo.

This content was originally published on PR Web

With the rise of AI solutions in the Insurance market, questions around AI regulations and compliance are increasingly at the forefront. Key questions such as “What happens when we use data in the context of AI?” and “What are the key focus areas in the new regulations?” are top of mind for both consumers and industry leaders.

To address these topics, Sixfold’s founder and CEO, Alex Schmelkin, hosted the webinar “How to Secure Your AI Compliance Team’s Approval”. Joined by industry experts Jason D. Lapham, Deputy Commissioner for P&C Insurance for the State of Colorado, and Matt Kelly, Data Strategy & Security Counsel at Debevoise & Plimpton, the discussion provided essential insights into navigating AI regulations and compliance.

Here are the key insights from the session:

AI Regulation Developments: Colorado Leads the Way in the U.S

“There’s a requirement in almost any regulatory regime to protect consumer data. But now, what happens when we start using that data in AI? Are things different?” — Alex Schmelkin

Both nationally and globally, AI regulations are being implemented. In the U.S., Colorado became the first state to pass a law and implement regulations related to AI in the insurance sector. Jason Lapham explained that the key components of this legislation revolve around two major requirements:

1. Governance and Risk Management Frameworks: Companies must establish robust frameworks to manage the risks associated with AI and predictive models.
2. Quantitative Testing: Businesses must test their AI models to ensure that outcomes generated from non-traditional data sources (e.g., external consumer data) do not lead to unfairly discriminatory results. The legislation also mandates a stakeholder process prior to adopting rules.

Initially, the focus was on life insurance, as it played a critical role in shaping the legislative process. The first regulation, implementing Colorado’s Bill 169, adopted in late 2023, addressed governance and risk management. This regulation applies to life insurers across all practices, and the Regulatory Agency received the first reports this year from companies using predictive models and external consumer data sources.

So, what’s the next move for the first-moving state in terms of AI regulations? Colorado Division of Insurance is developing a framework for quantitative testing to help insurers assess whether their models produce unfairly discriminatory outcomes. Insurers are expected to take action if their models do lead to such outcomes.

Compliance Approach: Develop Governance Programs

“When we’re discussing with clients, we say focus on the operational risk side, and it will get you largely where you need to be for most regulations out there.” — Matt Kelly

With AI regulations differing across U.S. states and globally, companies face challenges. Matt Kelly described how his team at Debevoise & Plimpton navigate these challenges by building a framework that prioritizes managing operational risk related to technology. Their approach involves asking questions such as :

• What AI is being used?
• What risks are associated with its use?
• How is the company governing or mitigating those risks?

By focusing on these questions, companies can develop strong governance programs that align with most regulatory frameworks. Kelly advises clients to center their efforts on addressing operational risks, which takes them a long way toward compliance.

The Four Pillars of AI Compliance 

Across different AI regulatory regimes, four common themes emerge:

1. Transparency and Accountability: Companies must understand and clearly explain their AI processes. Transparency is a universal requirement.
2. Ethical and Fair Usage: Organizations must ensure their AI models do not introduce bias and must be able to demonstrate fairness.
3. Consumer Protection: In all regulatory contexts, protecting consumer data is essential. With AI, this extends to ensuring models do not misuse consumer information.
4. Governance Structure: Insurance companies are responsible for ensuring that they—and any third-party model providers—comply with AI regulations. While third-party providers play a role, carriers are ultimately accountable.

Matt Kelly emphasizes that insurers can navigate these four themes successfully by establishing the right frameworks and governance structures. 

Protection vs. Innovation: Striking the Right Balance 

“We tend not to look at innovation as a risk. We see it as aligned with protecting consumers when managed correctly.” — Matt Kelly

Balancing consumer protection with innovation is crucial for insurers. When done correctly, these goals align. Matt noted that the focus should be on leveraging technology to improve services without compromising consumer rights.

One major concern in insurance is unfair discrimination, particularly in how companies categorize risks using AI and consumer data. Regulators ask whether these categorizations are justified based on coverage or risk pool considerations, or whether they are unfairly based on unrelated characteristics. Aligning these concerns with technological innovation can lead to more accurate and fair coverage decisions while ensuring compliance with regulatory standards.

Want to learn more? 

Watch the full webinar recording and download Sixfold’s Responsible AI framework for Sixfold’s approach to safe AI usage. 

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Companies of all sizes are actively exploring how emerging AI technologies can overcome longstanding business challenges. Inevitably, they run up against the reality that weathered AI pros like myself have long known: AI ain’t easy.  Rather than going it alone, many businesses choose to partner with firms that specialize in building solutions with LLMs. The good news? There are a growing number of AI vendors to pick from, with more popping up all the time. The bad? Discerning if a vendor can deliver what you need isn’t always so straightforward.

It seems like everyone and their little cousin touts the ability to “wrap” custom applications around one of the big-name LLMs. If that’s all they bring to the table, they might help you address simple use cases, but probably won’t have the chops to build and manage complex solutions in heavily regulated industries like insurance. That’s a whole different thing.

So, how can you tell if a prospective vendor can meet your business's needs? In this blog post, I’ll explore some key areas along the AI value chain and propose some questions to ask so you can make an informed decision.

So, how can you tell if a prospective vendor can meet your business's needs? In this blog post, I’ll explore some key areas along the AI value chain and propose some questions to ask so you can make an informed decision.

Input preparation

What you put into your AI system is what you get out of it. Make sure a prospective vendor prioritizes clean data, stored & handled in a secure compliant manner.

You can think of data like a commodity that powered the previous century: oil. You don’t just dig some oil out of the ground and pour it into your gas tank. (Or, I guess you could, but you wouldn’t get far before your engines seized up.) Like oil, data requires multiple rounds of preparation before it can be used. 

The value of the output your AI produces is directly related to the quality of the input. Before moving forward with any prospective vendor, ensure they have the means—and indeed, the knowledge—to help you build compliant, secure data workflows from beginning to end. 

The value of the output your AI produces is directly related to the quality of the input. Before moving forward with any prospective vendor, ensure they have the means—and indeed, the knowledge—to help you build compliant, secure data workflows from beginning to end. 

Here are some points to consider to ensure this is a vendor for you.

Questions to consider: 

• ‍How will the data be collected?
  ‍Data must be carefully collected to protect privacy and prevent bias. Ensuring that data has been ethically obtained and correctly governed is a point of emphasis for regulators.‍
• How will the data be “cleaned”?
  ‍Data needs to be refined and structured in a way that an AI solution can use and interpret. Make sure a prospective vendor understands what types of data are appropriate for your use case and how to prepare it at scale.‍
• How will the data be transferred, stored, and secured?
  ‍When developing solutions for complex, highly regulated industries, proof of certification for things like SOC2 and HIPAA are table stakes. Additionally, you’ll want to verify that the vendor uses secure data transfer methods, such as encryption during transit and at rest, to prevent unauthorized access. Also, ensure they effectively track the status of the data over time via robust version control and data lineage systems.

Prompt development 

LLMs work best when you make it difficult for them to make mistakes. An AI vendor should understand how to craft prompts to generate business value. 

For an AI solution to generate value, it must surface useful information with as little human intermediation as possible. This is achieved by ensuring that every prompt to an LLM includes all guidance, data access, and guardrails necessary to generate a high-quality return. Things like: 

1. Industry-specific content to guide results
2. Phrasing that reflects informed insight into the domain 
3. Precise instructions on the structure of the result being sought 

Your vendor will need to demonstrate they understand the capabilities and limitations of AI and can provide insights on how to structure LLM conversations to extract maximum value. Here are some points to review with a prospective partner to ensure they have the means—and better yet, a history—of value-oriented prompt engineering.

Questions to consider: ‍

• ‍How do they build prompts, and what domain-specific knowledge do they have?
  ‍Technical acumen is one thing, but does the vendor understand the specific needs of your industry? It’s one thing to ask an LLM to plan out a fun afternoon at the beach, it’s another thing to have it understand if, for example, family-owned restaurants align with a home insurer's risk appetite, or not. ‍
• What methods are used to select material included in the context window?
  ‍You should understand the vendor’s criteria for selecting contextually relevant information and how they ensure this information is timely and accurate. Ask what processes they use to filter and prioritize the most pertinent data for inclusion in prompts.‍
• How often, and in what ways are prompts updated over time? Are these changes tracked?
  ‍Learn about their schedule for reviewing and updating prompts to keep them aligned with the latest industry trends and data. Ensure they have a system for tracking changes to prompts, including version history and impact analysis, to maintain transparency and continuous improvement.‍
• What methods are used to evaluate the results of prompts, and to compare the results to prior versions when changes are made?
  ‍Ask about their evaluation metrics and benchmarks for assessing prompt performance, including accuracy, relevance, and consistency. Understand their process for A/B testing new prompt versions and how they compare the results to previous versions to ensure improvements.

Output control

Non-deterministic AI systems act in unpredictable ways. A quality vendor should know how to measure misaligned behaviors, as well as how to address them.  
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The value of the output your AI produces is directly related to the quality of the input. Before moving forward with any prospective vendor, ensure they have the means—and indeed, the knowledge—to help you build compliant, secure data workflows from beginning to end.  Ask an LLM the same question 10 times and you might get 10 different responses. The goal is to generate 10 accurate, useful answers. Achieving this requires putting as much care into reviewing the system’s output as you do into preparing the input. 

Ask an LLM the same question 10 times and you might get 10 different responses. The goal is to generate 10 accurate, useful answers. Achieving this requires putting as much care into reviewing the system’s output as you do into preparing the input. 

Continuous monitoring and tweaking are necessary to adapt your system to accommodate new data and evolving requirements. Here are some questions to explore when evaluating a vendor’s approach to scaled output control.

Questions to consider: 

• ‍What evals will you run?
  ‍Inquire about their evaluation frameworks, including both automated and manual assessments, to ensure outputs meet quality standards. Learn about the specific metrics they use to evaluate outputs, such as precision, recall, and F1 score, as well as checks for hallucinations and biases.‍
• What role will human experts play in this process?
  ‍Verify that human subject matter experts are involved in reviewing and validating AI outputs to ensure they are contextually appropriate and accurate. Ask about their process for incorporating expert feedback into continuous improvement cycles for the AI system.‍
• How often will you review overall results, and what metrics will you use to guide refinement and improvement?
  ‍Get a handle on their schedule for regular reviews and audits of AI outputs to ensure ongoing quality and relevance. Inquire about the key performance indicators (KPIs) and metrics they use to monitor and refine the AI system, such as user satisfaction scores, error rates, and feedback loops.

Transparency 

Not only does visibility allow you to properly evaluate an AI’s performance, it’s increasingly required by regulators as a means to address system bias.

Transparency is crucial for every step from data preparation to prompt development and output review. You cannot evaluate what you cannot see. To maintain the highest possible standards, every AI vendor should be prepared to provide a window into every step under their control. 

Questions to consider: 

• ‍Can you provide clear documentation of your processes and methods?
  ‍Ensure that the vendor offers comprehensive and understandable documentation covering all aspects of their AI processes, from data collection to output generation. Ask for examples of their documentation to assess its clarity and completeness.‍
• Can you demonstrate every point at which they interact with an LLM, and provide a complete trail of what information was exchanged?
  ‍Verify that the vendor maintains detailed logs and records of interactions with the LLM, including data inputs, prompts, and outputs. Ensure they can provide audit trails that detail the flow of information through their systems, which is crucial for regulatory compliance and troubleshooting.‍
• Will you provide a routine report about their evaluations and measuring for potential bias?
  ‍Inquire about their regular reporting practices, including how often they produce reports on AI performance, bias detection, and mitigation efforts. Ask to see examples of these reports to evaluate their thoroughness and transparency in addressing potential biases and other issues.

At Sixfold, we’ve created a Responsible AI framework for prospects and customers to showcase our ongoing transparency work.

In Summary

AI has the potential to overcome challenges that have been holding businesses back for decades. If you haven’t started your AI journey, now’s the time to get started. Partnering with an AI vendor can help you identify use cases ripe for transformation and provide the skills to get you there.

I hope that this checklist helped you identify which vendor has the right combination of technical know-how, industry expertise, and regulatory awareness to get your business where it needs to be.

This post was originally published on Linkedin

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

With our latest product updates, we’ve extended our commercial underwriting product with a suite of AI-powered features to facilitate end-to-end underwriting across all lines of business, scaling from transactional underwriting to complex, three-dimensional risks.

Sixfold’s number one superpower is to easily–and quickly–ingest carriers’ unique underwriting guidelines and automatically surface the submissions that match the carrier’s unique risk appetite. The platform empowers carriers to streamline the underwriting process by:

🔘 Analyzing publicly available information and ingesting data from multiple disparate sources in an instant for a comprehensive risk assessment

🔘 Generating a comprehensive summarization of the business’s operations and providing NAICS/SIC classification

🔘 Surfacing positive and negative risk factors tuned to a carrier’s unique appetite

🔘 Answering complex questions across large sets of documents

🔘 Prioritizing risks with an underwriter-facing dashboard for improved resource allocation

Over the past half, we’ve significantly matured our P&C underwriting platform with deep investments across accuracy, traceability, performance, and extensibility. Some of the key highlights include:

✔️ Improved Accuracy with Advanced Document Extraction  

As underwriters ask complex questions across large sets of documents, we’ve invested in new models to improve extraction across the universe of insurance documents. With Sixfold’s latest models, we’ve seen a 40% boost in accuracy in extracting data from the most illegible documents.

With our ongoing investments in extraction models tailored to the documents underwriters see daily, Sixfold ensures precise and reliable insights from large, challenging document sets, transforming how underwriters interact with data.

✔️ Appetite Match Scoring with Weighted Risk Scoring

To replicate underwriters’ cognitive processes, Sixfold is introducing weighted risk signals to reflect the nuance across how underwriting factors impact where a risk sits within a carrier’s appetite. Now, carriers can assign varying importance to different factors to prioritize risks more accurately based on alignment with their risk appetite.

✔️ Enhanced Transparency with Inline Citations

Not only can Sixfold answer complex questionnaires across lines of business, but now all answers are grounded in the original source material with citations to the most relevant sections for confident decision-making.

✔️ Bringing Risk Classification and Summarization Down to Seconds

With our continued commitment to investing in the Sixfold pipeline architecture to improve performance, availability, and resilience, we’ve brought down the median case processing time from 80 seconds to 31 seconds. With these enhancements, in less than a minute, Sixfold can research publicly available information to learn everything we can about a business and analyze the aggregated data for business summarization and NAICS/SIC classification.

✔️ Embed Sixfold across Underwriters’ Existing Workflows with our API

With the Sixfold API, carriers can seamlessly integrate Sixfold into existing workflows for enhanced productivity and unified risk management. From automated data gathering and ingestion to custom-tailored underwriting recommendations that can be embedded across existing workflows and systems, Sixfold cuts out the manual work and document handling overhead for 10x faster risk review processes.

✔️ Mitigating drift and bias with our Responsible AI framework 

Designed to navigate the rapidly evolving AI landscape confidently, Sixfold’s Responsible AI framework ensures carriers are well-insulated from risk with its enhanced auditability, data provenance, and traceability. By actively collaborating with regulatory bodies and legal counsel, Sixfold remains at the forefront of responsible AI innovation, safeguarding carriers with unparalleled diligence.

👀 Coming Soon: Research Assistant

Stay tuned for future launch updates to hear about upcoming capabilities like Sixfold’s Research Assistant, designed to find answers to complex research questions, all of which are grounded in the original source material with citations.

Want to see our new capabilities in action?
Get in touch →‍