Sixfold Content

Life & Health underwriting is a constant balance between speed and quality: getting the right coverage to customers before they go somewhere else. One missed detail can change the whole quote. But the longer pricing takes, the greater the risk of applicant drop-off.

Accelerated underwriting helps with simpler cases that don't require additional evidence, but as recently reported by Gen Re, up to 88% of cases still need some type of human review.

Those cases still go through the same workflow: sorting through documents at initial review, going back to the advisor or broker when something's missing, and when the case is finally ready, reading through hundreds of pages of medical records like EHRs, APS, and lab results to piece together the full health picture before making a decision.

It's a lot of steps and a big manual workload that keeps underwriters from bringing in more premiums and getting back to customers faster with better coverage. That’s where Sixfold comes in; underwriting AI built to help at every step of the process, bringing together the details that matter to your team, and now with our new Narrative, a tailored initial analysis that simplifies triage and reduces the unnecessary back and forth, before the deeper analysis.

The First Look At a Case

At the initial review, underwriters are trying to answer one question: Do I have what I need to move this case forward? Most of the time, that means going through multiple documents just to find out, and when something's missing, at least 30 minutes are spent on a case they'll have to come back to later.

Our new Narrative capability for Life & Health is a focused, structured case summary tailored to what matters to your underwriting team, surfacing the information you want to see upfront to help underwriters with triage and to figure out next steps. Sixfold’s AI agents read through all the case documents, application, APS and labs, and pull together the relevant details into one structured view, so underwriters don't have to. Each narrative is configurable to your team's specific workflow, so what gets surfaced reflects what actually matters for your decisions.

Imagine a case where an applicant has hypertension or a history of elevated cholesterol. The underwriter immediately needs to know if they have all the information about blood pressure and what meds they're on. Does this applicant smoke? What is their family history? This is information they need before they even begin to assess the case.

The Narrative gives you exactly the information you need to know before you move on.

"We want to support underwriters at every point in their workflow. Sometimes that means a focused Narrative to help with triage and next steps. Sometimes that means a deep condition analysis across hundreds of pages. The point is, wherever an underwriter is in the process, Sixfold is there to help." 

— Noah Grosshandler, Product Manager at Sixfold

On the reinsurer side, it's a similar story. Underwriters are spending precious time just organizing loosely assembled submissions from cedents because most times the cases come in an inconsistent format, requiring additional questions before quoting, many of which wouldn't even be necessary if the information were clearly structured upfront.

With Narrative, these questions are answered upfront. Fewer emails back and forth with brokers and faster responses.

‍The Full Clinical Story: Conditions Analysis

After triaging and making sure a case is ready to quote, underwriters go back to the manual review of hundreds of pages of medical records, trying to spot that one detail that will change the whole quote. It's pretty much like detective work, finding how a medicine connects to a diagnosis and what the impact is on that patient, trying to piece together the full medical story. Assessing history, severity, and how well a diagnosis is managed. Meanwhile, customers are waiting for a response.

That's where Sixfold's in-depth Conditions Analysis comes in. When the case is ready for a full review, our Conditions Analysis connects all the medical pieces, bringing together medications, labs, procedures, and diagnoses under each condition so underwriters can see the full clinical picture without jumping between documents. It highlights what's relevant to your underwriting guidelines, shows how conditions have progressed over time, and surfaces the clinical data that matters most for your decisions.

One example is a case where an applicant is taking Spironolactone, a common medication used to control high blood pressure, but also prescribed for hormonal acne. An underwriter would have to go through lengthy pages of documentation to find what dosage they're on, how long they've been taking it, and what it's actually being prescribed for. And that's just one medication. For complex cases, underwriters are doing this across multiple conditions with years of medical history.

With our Conditions Analysis, underwriters can spend their time on decision-making rather than document review and data gathering. Customers get faster and better quotes. Guardian, for example, saw a 50% reduction in review time using Sixfold.

When Speed is What Matters

Some of our customers work with higher-volume lines like structured settlements and annuities and often process over 10,000 cases per year; they don’t always need an in-depth Condition Analysis to move a case to the next step.

There are fewer key risk details that influence the analysis, so they have been able to use Narrative to move forward on up to 70% of their cases, consulting our full Conditions Analysis only for the most complex ones. For some teams, Narrative handles most of the heavy lifting of manual review.

"One of our customers built Narrative into their workflow for initial case review. It helps their underwriters get up to speed quickly and know exactly what needs attention. When a case requires deeper analysis, they move to our full Conditions Analysis, but Narrative gives them that strong starting point." — Justin Sorce, Customer Success Manager at Sixfold

Confident Underwriting Decisions

Whether it's a quick initial review or a deep dive into a complex case, Sixfold is there to help your team get to a confident underwriting decision faster.

And it works where your team already works, whether that's in our UI, a workbench, or a policy admin system. Every insight ties back to the source documents, so underwriters can verify what they're seeing. Built for underwriting since day one. HIPAA and SOC 2 certified, with single-tenant environments to keep your data secure. Rigorous AI fairness testing to meet evolving regulatory standards and grounded in our Responsible AI principles.
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Interested in a hands-on demo? Reach out.

The bottleneck in life & health underwriting

Life & health underwriting is slowed by manual submission triage and the work of reading dense medical evidence for risk assessment. AI underwriting platforms now read and reason through an entire submission before an underwriter even opens it.

Every life and health submission arrives as a stack of unstructured information, like application data and medical records such as attending physician statements, lab results, and prescription histories, that can run to hundreds, even thousands of pages. Historically, underwriters spent the bulk of their time finding, reading, and organizing that evidence before they could make a single risk decision. That manual effort is the real constraint, not the decision-making process itself.

Sixfold changes that. Its AI agents read and reason across the full submission the way an experienced underwriter would, surfacing the relevant medical evidence aligned with the carrier's underwriting manual, flagging key risk factors, and delivering a clear health overview. All steps to support the underwriter's decision-making.

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Using medical software to read records and find key information faster

Medical evidence software exists because reading through the records is the most time-consuming part of a life & health case analysis. 

Sixfold processes attending physician statements, lab panels, and prescription data, extracts the clinically relevant findings, and ties them back to the risk factors that matter for the underwriting decision. Instead of skimming a 200-page record for a single relevant medical condition, the underwriter gets a structured, evidence-backed summary with a traceable source.

Because the system comes pre-trained with underwriting knowledge, it recognizes what matters most to a life or health risk from day one, rather than requiring months of configuration. It plugs into the underwriting workbench the team already uses, so the medical evidence summary appears within the existing workflow.

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Reducing time spent in manual submission triage with AI

Doing the initial review of a submission, checking if all the information is available to proceed or if it needs more data from brokers or agents, is where underwriting teams spend the most time on operational work. An AI platform automates triage by ingesting each submission as it arrives, assessing completeness, pulling and organizing the medical evidence, and producing a preliminary recommendation before a human review.

The result is that when underwriters start the case review, it's already prioritized, and the risk assessment is complete, ready for a final underwriting decision. Underwriters get to spend their time on judgment, complex risk cases, and broker relationships rather than on sorting and data entry. 

Across Sixfold customers, this contributes to a 50% reduction in turnaround time, hit ratios up 15% or more, and up to 30% more gross written premium per underwriter, because capacity is redirected from administrative triage to writing business.

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L&H Frequently Asked Questions

How do life and health insurers use medical-evidence software to speed up the underwriting process?

Life insurers use Sixfold's AI to automatically read and interpret medical records, attending physician statements, lab results, and prescription histories. It extracts relevant findings, links them to risk factors, and presents an evidence-based summary, so underwriters can focus on the decision rather than digging into information. It provides a risk assessment ready for decision-making, including recommendations on what to do next in a case.

Sixfold comes pre-trained with underwriting knowledge from day one. It integrates directly with existing underwriting workbenches, keeping the source evidence visible for every conclusion. Customers see processing times 50% to 97% faster as a result.

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How do underwriting teams reduce manual submission triage using AI platforms?

Underwriting teams reduce manual triage by having the AI platform ingest each submission on arrival, check completeness, organize the medical evidence, and automatically generate a risk assessment and next-step recommendations. Underwriters then start case review with cases that are already prioritized and pre-analyzed, focusing their time on risk judgment rather than sorting and data entry.

Having Sixfold help with triage, teams redirect capacity from administrative work to writing business. Sixfold customers report hit ratios up 15% or more and up to 30% more GWP per underwriter. Adoption stays high, over 90% of expected users, because the platform fits how underwriting actually works rather than forcing a new process.

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What are the top tools for automating life underwriting decisions?

The top tools for automating Life and Health underwriting decisions are purpose-built underwriting AI platforms that reason across a full submission, automated medical evidence and record summarization software, and integrations into the underwriting workbench. The strongest options combine all three: decisioning, evidence analysis, and workflow fit.

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About Sixfold

Sixfold is the first AI solution built specifically for insurance underwriting, in market since early 2023, and is live in production across 50 lines of business, including life & health lines. It has processed more than 1.5 million submissions globally and is used by more than 1,000 underwriters, from insurers like Guardian and New York Life.

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Life & Health underwriters often have to go through hundreds of pages of medical documents to understand an applicant’s health profile, knowing that one missed detail could lead to the wrong coverage decision.

When we introduced our Life & Health Underwriting AI, we set out to give underwriters everything they need in one place: diagnoses, medications, and procedures pulled from applications and supporting documents, aligned to the insurer’s unique risk appetite. They no longer had to spend time manually going through medical documents. 

But after getting feedback from underwriters across global insurers, we realized something important: presenting information alone isn't enough. What they really need is the full health story, with all the pieces connected. For example, if a medication appears, they need to know the full context around it: how often it was prescribed and any related diagnoses or procedures.

That’s why we’re upgrading our Underwriting AI with Conditional Insights and Clinical Data, designed to reflect the way underwriters think about the overall health profile.

What’s New?

Conditions: From Medical Facts to the Full Story

Conditions completely change how Sixfold’s insights are presented to underwriters. 

Previously, all relevant data, like Personal Health, Medications, Procedures, and more, appeared as individual facts. While surfacing this information is essential, it didn’t show underwriters how it all connected.

The thing is, Life and Health underwriters don't analyze each fact in isolation; they think about how it fits into the bigger picture. What does this medication suggest? How severe is the condition? How does it all connect?

That’s why Sixfold now brings together all relevant information under a diagnosed condition, including:

• Medications and ongoing treatment
• Procedures and lab results
• The condition’s full history, with context and progression over time
  

For example, take Gastritis, an inflammation of the stomach lining that can cause pain, indigestion, and discomfort. An applicant might have been diagnosed with it a while ago, is taking medication such as Pantoprazole, and could have undergone procedures like Endoscopies five years ago.

Instead of treating these details as in isolation, Sixfold now recognizes they’re all part of the same conditions story. This mirrors exactly how an experienced underwriter would naturally connect the dots, seeing not just health facts, but the bigger picture of an applicant’s health history.

“This release has been a huge focus for our team, and the feedback from early users has been very positive. They’ve shared firsthand how impactful it is to have a single experience that brings together every aspect of an applicant’s medical history.” 

- Noah Grosshandler, Product Manager at Sixfold.

Want a closer look at how Conditions work? Join our live product demo on September 11th.

Core Clinical Data: Lab Insights and Historical Trends

Underwriters often have to go through dozens of lab results to answer: Does this applicant have an underlying condition? Are the results concerning? Could they worsen over time?

Core Clinical Data brings all of that information into one place, instantly. It pulls information found in lab results and medical records uploaded to Sixfold, presenting a pre-defined set of health indicators commonly tied to high-risk conditions such as: Vitals, Cardiovascular Health, and Hematology.

It gives underwriters instant and standardized insight: lab values, normal ranges, and historical progression, making it easier to assess the presence, progression, and severity of chronic conditions.

The impact? Core Clinical Data gives underwriters a snapshot of the applicant’s health at a glance, meaning time saved in reading lab reports that can be used to actually bind accounts. With immediate access to historical trends and key lab indicators, underwriters can make faster and more accurate decisions. 

How Guardian Cut Review Time in Half

Guardian, one of the largest life insurers in the U.S., faced a common challenge: manual case reviews slowed down underwriters and created bottlenecks in the underwriting process.

By adopting Sixfold for its Disability line, Guardian was able to automatically extract medical data, triage information faster, and speed up case assessments end-to-end.

The impact: a 50% reduction in review time, freeing underwriters from being stuck reviewing medical records and letting them focus on risk decisions. Read more about the results Guardian has seen here. 

Now, Guardian is expanding the program across more business lines, and you can see why! Join our Life & Health Product Demo on September 11th, where we’ll showcase how Sixfold works for life & health insurance underwriting. 

As AI becomes more embedded in the insurance underwriting process, carriers, vendors, and regulators share a growing responsibility to ensure these systems remain fair and unbiased.

At Sixfold, our dedication to building responsible AI means regularly exploring new and thoughtful ways to evaluate fairness.¹

We sat down with Elly Millican, Responsible AI & Regulatory Research Expert, and Noah Grosshandler, Product Lead on Sixfold's Life & Health team, to discuss how Sixfold is approaching fairness testing in a new way.

Fairness As AI Systems Advance

Fairness in insurance underwriting isn’t a new concern, but testing for it in AI systems that don’t make binary decisions is.

At Sixfold, our Underwriting AI for life and health insurers don’t approve or deny applicants. Instead, it analyzes complex medical records and surface relevant information based on each insurer's unique risk appetite. This allows underwriters to work much more efficiently and focus their time on risk assessment, not document review.

“We needed to develop new methodologies for fairness testing that reflect how Sixfold works.”
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— Elly Millican, Responsible AI & Regulatory Research Expert

While that’s a win for underwriters, it complicates fairness testing. When your AI produces qualitative outputs such as facts and summaries, rather than scores and decisions, most traditional fairness metrics won’t work. Testing for fairness in this context requires an alternative approach.
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“The academic work around fairness testing is very focused on traditional predictive models, however Sixfold is doing document analysis,” explains Millican. “We needed to develop new methodologies for fairness testing that reflect how Sixfold works.”

“The academic work around fairness testing is very focused on traditional predictive models, however Sixfold is doing document analysis,” explains Millican. “We needed to develop new methodologies for fairness testing that reflect how Sixfold works.”

“Even selecting which facts to pull and highlight from medical records in the first place comes with the opportunity to introduce bias. We believe it’s our responsibility to test for and mitigate that,” Grosshandler adds.

While regulations prohibit discrimination in underwriting, they rarely spell out how to measure fairness in systems like Sixfold’s. That ambiguity has opened the door for innovation, and for Sixfold to take initiative on shaping best practices and contributing to the regulatory conversation.

A New Testing Methodology

To address the challenge of fairness testing in a system with no binary outcomes, Sixfold is developing a methodology rooted in counterfactual fairness testing. The idea is simple: hold everything constant except for a single demographic attribute and see if and how the AI’s output changes.²

“Ultimately we want to validate that medically similar cases are treated the same when their demographic attributes differ,”
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— Noah Grosshandler, Product Manager @Sixfold

“We start with an ‘anchor’ case and create a ‘counterfactual twin’ who is identical in every way except for one detail, like race or gender. Then we run both through our pipeline to see if the medical information that’s presented in Sixfold varies in a notable or concerning way” Millican explains.

“Ultimately we want to validate that medically similar cases are treated the same when their demographic attributes differ,” Grosshandler states.

Proof-of-Concept

For the initial proof-of-concept, the team is focused on two key dimensions of Sixfold’s Life & Health pipeline.

1. Fact Extraction Consistency
Does Sixfold extract the same facts from medically identical underwriting case records that differ only in one protected attribute?

2. Summary Framing and Content Consistency
Does Sixfold produce diagnosis summaries with equivalent clinical content and emphasis for medically identical underwriting cases?

“It’s not just about missing or added facts, sometimes it’s a shift in tone or emphasis that could change how a case is perceived,” Millican explains. “We want to be sure that if demographic details are influencing outputs, it’s only when clinically appropriate. Otherwise, we risk surfacing irrelevant information that could skew decisions.”

Expanding the Scope

While the team’s current focus is on foundational fairness markers (race and gender), the methodology is designed to evolve. Future testing will likely explore proxy variables such as ZIP codes, names, and socioeconomic indicators, which might implicitly shape model behavior.

“We want to get into cases where the demographic signal isn’t explicit, but the model might still infer something. Names, locations, insurance types, all of these could serve as proxies that unintentionally influence outcomes,” Millican elaborates.

The team is also thinking ahead to version control for prompts and model updates, ensuring fairness testing keeps pace with an evolving AI stack.

“We’re trying to define what fairness means for a new kind of AI system,” explains Millican. “One that doesn’t give a single output, but shapes what people see, read, and decide.”

Sixfold isn’t just testing for fairness in isolation, it’s aiming to contribute to a broader conversation on how LLMs should be evaluated in high-stakes contexts like insurance, healthcare, finance, and more.

That’s why Sixfold is proactively bringing this work to the attention of regulatory bodies. By doing so, we hope to support ongoing standards development in the industry and help others build responsible and transparent AI systems.

“This work isn’t just about evaluating Sixfold, it’s about setting new standards for a new category of AI." Grosshandler concludes.

“This work isn’t just about evaluating Sixfold, it’s about setting new standards for a new category of AI. Regulators are still figuring this out, so we’re taking the opportunity to contribute to the conversation and help shape how fairness is monitored in systems like ours,” Grosshandler concludes.

Positive Regulatory Feedback

When we recently walked through our testing methodology and results with a group of regulators focused on AI and data, the feedback was both thoughtful and encouraging. They didn’t shy away from the complexity, but they clearly saw the value in what we’re doing.

“The fact that it’s hard shouldn’t be a reason not to try. What you’re doing makes sense... You’re scrutinizing something that matters.” said one senior policy advisor.

“The fact that it’s hard shouldn’t be a reason not to try. What you’re doing makes sense... You’re scrutinizing something that matters.”

— Senior Policy Advisor

One of the key themes that came up during the meeting was the unique nature of generative AI, and why it demands a different kind of oversight. As one senior actuary and behavioral data scientist put it: “Large language models are more qualitative than quantitative... A lot of technical folks don’t really get qualitative. They’re used to numbers. The more you can explain how you test the language for accuracy, the more attention it will get.”

That comment really resonated. It reflects the heart of our approach, we’re not just tracking metrics. We’re evaluating how language evolves, how facts can shift, and how risk is framed and communicated depending on the inputs.

The Road Ahead

Fairness in AI isn’t a fixed destination, it’s an ongoing commitment. Sixfold’s work in developing and refining fairness and bias testing methodologies reflects that mindset.

As more organizations turn to LLMs to analyze and interpret sensitive information, the need for thoughtful, domain-specific fairness methods will only grow. At Sixfold, we’re proud to be at the forefront of that work.

Footnotes

¹While internal reviews have not surfaced evidence of systemic bias, Sixfold is committed to continuous testing and transparency to ensure that remains the case as we expand and refine our AI systems.

²To ensure accuracy, cases involving medically relevant demographic traits, like pregnancy in a gender-flipped case, are filtered out. The methodology is designed to isolate unfair influence, not obscure legitimate medical distinctions.

‍Check Sources Instantly‍

Trust and transparency are essential when underwriters use AI in their daily work. Underwriters need to know that the information they rely on is accurate; otherwise, a policy decision could result in incorrect coverage, claims issues, or unnecessary risk for the carrier. 

One of the best ways to build that confidence is by clearly showing the source of each piece of information. That’s why we’re excited to introduce a new In-line Citations feature for our Life & Health customers. This feature makes it easy to check the source behind any insight Sixfold surfaces.

So, how does it work?

When reviewing a case in Sixfold,  underwriters can now see exactly where each fact came from, including the document and page number. Here’s what you’ll see when clicking into a fact card:

• Document category listed for each file.
• Page number shown on hover
• One-click access to the exact source page
• All of the documents where the fact was found

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Our goal? To increase underwriter confidence and efficiency by clearly showing the source of medical and lifestyle facts within the insurance application analysis.

New Info? Now Flagged for You

In Life and Health underwriting, it’s common for some cases to take time, sometimes weeks, to gather all the documents needed for final analysis. The result? A lot of new information is coming in, and it’s not always clear what’s actually new facts.

That’s where our new capability, New Case Facts comes in.

Now, when new facts are surfaced within a case, you’ll see a bell icon next to the relevant fact card, a simple way to flag which facts came from the latest documents added. You can click into the fact to see more context, including which document category it came from.

This makes it easier to understand what’s been added, without having to reread the whole submission. It’s especially useful when multiple underwriters are collaborating on a case; one might start the analysis, while a colleague might actually finish it.

With new facts clearly marked, everyone can stay aligned and quickly assess what’s different and what it means for the overall risk profile of the applicant. 

In life and disability underwriting, one of the most time-consuming and error-prone steps is verifying an applicant’s self-reported information.

Why? Because applications are long and detailed, and even when applicants are trying to be honest, omissions, intentional or not, are common. This is a growing concern across the industry, a recent Munich Re’s survey identified applicant misrepresentation as the most rapidly increasing form of fraud.

Sixfold’s Discrepancy Scan capability was built to address exactly this issue. Sixfold’s AI is now able to automatically flag mismatches between what applicants report and what’s found in their medical records, giving underwriters a faster, more standardized way to catch inconsistencies before they become costly.

When risk is hiding in the records

When someone applies for individual life or disability coverage, they complete a health questionnaire, like Part II, eMed, or a Med Supplement, disclosing conditions, medications, and history. From there, the underwriter kicks off verification: ordering APS reports, Rx histories, labs, and other third-party records. 

As these often hundreds of pages of documents arrive, the underwriter is essentially playing detective — comparing what the applicant said to what the medical records reveal. Did the applicant disclose all relevant conditions? Are they taking medications they didn’t mention? Is there a difference in diagnoses or treatment history?

Underwriters dedicate significant time to identifying discrepancies because they are critical. A person's prescription history can reveal underlying health issues, sometimes even before a formal diagnosis is made. For example, a prescription for a weight-loss medication might indicate an associated morbidity.

Any inconsistency could signal fraud or simply an oversight.​ Either way, it matters.

See below for a quick product walkthrough with Noah Grosshandler, Product Manager at Sixfold.

The feature is currently focused on medications, but that’s just the beginning. We're planning to expand this capability to detect discrepancies across pre-existing conditions, procedures, family history, and lifestyle factors—always guided by what’s material to each insurer.

Minutes vs. hours of detective work

Sixfold’s new capability eliminates a critical bottleneck in underwriting. The traditional approach of manually reviewing hundreds of pages to spot inconsistencies is both time-intensive and susceptible to oversight.

The Discrepancy Scan changes that completely, surfacing critical discrepancies automatically instead. The result is a more efficient process where underwriters can confidently assess risk based on complete information, without the administrative burden of document comparison.

“Sixfold goes beyond summarizing medical histories, we spotlight the contradictions that can change a morbidity assessment. By drawing connections across medical records, we emphasize the most crucial facts for investigation.

This approach transforms hours of detective work into minutes, providing underwriters with confidence and efficiency in their decision-making processes.”

— Lana Jovanovic, Head of Product @ Sixfold

Get the full story upfront

Accuracy is everything in life and disability underwriting. With Sixfold’s automatic discrepancy detection, underwriters are able to get to a more accurate underwriting decision by:

• Catching omissions and inconsistencies at the beginning of the review cycle
  
  
• Reducing misclassification of risk due to overlooked or conflicting information
  
  
• Detecting potential fraud patterns before they result in costly claims
  
  
• Maintaining consistency and transparency when cases move between underwriters 

How the feature works

The Discrepancy Scan automatically compares the self-reported application data against the supporting medical documents and flags any mismatches related to material facts.

Prescriptions are often a leading indicator of an underlying diagnosis, one that could directly impact insurability or rating decisions. But not every medication matters the same way, and what’s considered “material” varies from carrier to carrier.

By securely ingesting each carrier’s unique underwriting guidelines, Sixfold identifies which medications are truly relevant in each context, connecting the dots between prescriptions, diagnoses, and underwriting impact.

Here’s how the feature works in practice:

1. Medical Document Review
Sixfold’s AI reviews both the submitted application and any supporting documents uploaded (APS, MIB, Rx histories, etc.) for medical data relevant for risk assessment.

2. Discrepancy Detection
Sixfold then compares the findings in the medical documentation to what the applicant reported. If a medication appears in the documents but not in the application, it’s flagged as a discrepancy.

3. Discrepancy Alert
Within the underwriter's dashboard, discrepancies appear clearly labeled with clear icons. Clicking into a card brings up the relevant context e.g., “Blood thinner mentioned in the medical report, not disclosed by the applicant.”

4. Clear Next Steps
Underwriters can use this insight to request clarification from the applicant or additional documentation from providers.

5. Always-Current Monitoring
Because documents arrive asynchronously, the system continually updates as new files are uploaded. Discrepancies are flagged dynamically based on the most current information.

Learn More

Insurtech Insights takes a closer look at the Discrepancy Scan
Interested in a hands-on demo? Reach out for a Sixfold walkthrough