by Nick Ray Ball, Copilot🌠, and Sienna 4o🛰️👾
July 16, 2025

⚛️TLS-W🏹 — How to Win and Keep Winning

Introduction: Currently, it’s about how to succeed with AI, not about engineering the TLS-W🏹 App

When I discuss the TLS-W🏹 – Total Legal System AI Litigation Weapon, I’m describing it as it exists in this exact moment—July 15, 2025. Currently, my focus is hands-on: I’m immersed in figuring out precisely how to win a high-value, multi-track UK healthcare lawsuit using AI. At this stage, it’s not about scaling or mass replication; before engineering the software and apps into a system to spread globally, you must master the process itself. Each day, as I work on the groundwork for the TLS-W and advance the current suit against ICP-Epsom, I gain new insights. Sometimes I realise there’s more groundwork to lay; other times, I discover unexpected opportunities—either for greater financial reward or for achieving a more profound sense of justice.

For example, a significant challenge for litigants in person (LiPs) in the UK is that many expert witness companies primarily interact with law firms, making it difficult for individuals representing themselves to secure expert opinions. However, by issuing a one-time prompt instructing AI to act as an expert witness and repeating this across various AIs to prevent error, when all AIs produce the same answer, you can be confident that the answer is correct. You can then argue this in court, as an opposing expert witness is unlikely to risk appearing foolish by claiming that four different AIs are wrong on a very simple fact.

As part of this ongoing work, I’ve already created a presentation for ⚕️⚛️GP-AI Gatekeeper, which details the six-module design tailored to this very challenge—simply swapping out medical expertise for legal acumen. Naturally, TLS-W can operate as a layer on top of the GP-AI Gatekeeper, integrating both legal and medical knowledge. Gatekeeper stands as the fourth pillar on the Sienna AI August 1st, 2025 homepage, which presents 10 unique, unmatched ideas, hooks and concepts bound for venture capital firms and the World’s Top AI Companies. TLS-W is the sixth pillar, already highlighted as a central theme in the second pillar, the screenplay First One Back.

🚀S-Web 6 VC Franchising Model

When it comes to TLS-W🏹, the underlying marketing approach mirrors our 🚀S-Web 6 VC franchising model. Once the product is developed and the path to winning is fully mapped out, there are two more essentials to master: figuring out how to attract people to your platform, achieved by partnering with law firms, or making a product so useful that it becomes viral to the general public and, crucially, continually replicating the platform as a series of websites, apps, and e-franchises.

The final element—franchising—was already present in our previous work, notably in the Spotify Podcast Bonus episodes on ⚕️⚛️The GP-AI and TLS-W🏹. By adapting the 🏝️Villa Secrets franchise framework, we discovered a key advantage: human services, unlike physical products, don’t require a mark-up, making the model even more profitable.

Increasing the platform’s popularity hinges on making it easily replicable; anyone can create their own version, generate revenue, and a portion of that income flows back to the parent company. This self-reinforcing cycle not only drives growth but also sustains a thriving ecosystem for everyone involved.

The 🚀S-Web 6 VC franchising model transforms the app and website content management system into a product that can be easily replicated. Branding, managers, and managing companies can be switched with just a touch of a button. As the parent company advertises for new franchisees—or as existing team members seek additional earnings by running their own version of the app—the system steadily expands. Over time, this process generates 1,000, then 2,000, and eventually 20,000 apps nationwide, with different people attracting new users to the platform in a variety of ways.

Targeting Low-Value Lawsuits That Seldom Obtain No-Win No-Fee Representation

Our innovative approach targets a unique gap in the market: smaller claims—typically those valued under £50,000, and often even below £20,000—for which people rarely secure “no-win no-fee” legal representation. We’ve thoroughly analysed the numbers and identified this as an underserved segment. The proposition is simple but compelling: we assure every prospective client that if they present a strong enough case, we will guarantee their success.

This system isn't limited to medical negligence; it can also apply to various areas of law. But in cases of medical negligence, you basically use the system I designed for ⚕️⚛️GP-AI Gatekeeper Stage 2: “AI Decision-Tree Logic employs game-changing hierarchical questioning and decision-tree logic to guide the conversation, uncover critical details claimants might not naturally convey.” Employing tools like GPT-4, Gemini, Grok, or Copilot, you need to guide the AI to definitively identify the necessary elements for a case: establishing causation, something happened, it was caused by this, and the consequences are…

1. What happened
2. What was the cause (causation is critical)
3. What are the consequences

In addition, the system asks crucial questions such as “When did the incident occur?” to determine the statute of limitations. Importantly, there are four distinct ways to extend or bypass this legal deadline. This is precisely where the gatekeeper system excels far beyond a human receptionist; in my experience, the reception staff often lack awareness of these four critical exceptions, resulting in many potential clients being turned away unnecessarily.

1. Continuing Duty of Care / Ongoing Harm.
   Definition: The original negligent act continues to cause harm because the duty of care was never discharged, or the harmful consequence is active and unresolved.
2. Mental or Physical Disability (Statutory Suspension).
   Definition: The claimant was unable to bring a claim due to serious mental illness, cognitive impairment, or physical incapacity that prevented legal action.
3. Part of an Ongoing Series of Linked Harms.
   Definition: The earlier negligent act is legally inseparable from later harmful events, making it part of a continuous timeline.
4. Date of Knowledge / Delayed Discovery.
   Definition: The limitation period begins only once the claimant knew (or could reasonably have known).

Once it’s established you are within the statute of limitations, it’s a basic 1, 2, 3: What happened – What was the cause – What was the effect.

The Gamified AI-Powered Litigation App 🎮+ Never Lose Strategy

AI Litigation Weapon App and Never Lose Strategy – Sienna AI

The Gamified App: is where we train the AI to guide the customer into answering the important questions. As soon as they answer a critical question, it then gives a score, somewhat like an online slot machine—it goes Ding, Ding, Ding, Ding, Ding—and a voice will say: “Well done, you have earned five points for that answer. Only 15 more points to go.” Once you score 20 points, we’ll take this lawsuit, and you are guaranteed a victory.

You are guaranteed £10,000 or more if you can get to 20 points.

There are many ways to improve the score. The AI will guide you through it, and you will eventually reach 20 points or more, where higher scores translate to more money—for example, providing a piece of evidence, such as a document, that the AI in expert witness mode has deemed significant.

Once you reach this stage, your lawsuit is essentially ready to prevail against the NHSR, and it will be filed automatically. The process is designed to function much like creating an audiobook with AI: you’ll receive guided prompts along the way, each step building the structure of your claim. By the end, your case is thoroughly prepared, including all details of your financial claim or compensation sought.

The completed claim is then submitted directly to the NHSR, accompanied by a clear notice stating that if there is no response within three months, the matter will proceed as either a fast-track or multi-track lawsuit, ensuring the NHSR has a genuine threat to contend with.

In the vast majority of cases—about nine out of ten—if the NHSR realises they are unlikely to win, they will simply settle and pay out, for example £20,000. Statistically, only around 10% proceed to court; however, a significant portion of these 10% will be those who decline a settlement because they are seeking justice rather than just compensation, and they want their day in court.

Take my own situation, for example: it’s not about the money. They could offer me a substantial amount, but there’s no real justice in that. If settlements are the only consequence, the individuals responsible remain unaccountable and may continue to harm others. True altruism requires more than just a payout—it demands meaningful change.

The Can’t Lose Strategy. (Calculated on a £20,000 settlement)

The remarkable aspect of this system is that the Gatekeeper attendant at the law firm can confidently tell clients: “You’re guaranteed a 50% payout if NHSR settle and you don’t need to go to court, but if they do not settle and you must go to court, you’re guaranteed 100%—(£20,000) if you lose.” This is made possible because, in practice, the NHSR settles nine out of ten cases without the need for a court hearing. From each of these settled claims—each typically for £20,000—clients receive £10,000, and 20% of this amount (£4,000 per case) is contributed to a contingency fund. With ten clients following this route, nine settle and contribute a combined £36,000 to the fund (£4,000 x 9). The contingency fund then serves to cover the legal fees and provide financial security for the tenth client, who proceeds to court as a litigant in person (LIP). If this claimant loses, they still receive the full £20,000, paid from the contingency pool. If they win, they are awarded a bonus of £10,000—and for higher-value lawsuits, the rewards scale accordingly. This approach ensures every claimant is protected, incentivised, and never left at a disadvantage, regardless of the outcome.

In the scenario described above, we’re operating in a space where traditional “no win, no fee” lawyers are unlikely to take on cases—handling a £20,000 claim simply isn’t appealing when their potential earnings amount to just £6,000. However, with our innovative approach, there’s no additional effort required from the law firm. Instead, 30% of each claim is shared between Sienna AI and the system user, so the law firm receives approximately £3,000 per automated case. This streamlined, AI-driven process makes it not only feasible but attractive for firms to participate in claims that would otherwise be overlooked, creating opportunities for all parties involved.

Higher Value Cases

However, that's just the minimum scenario. In reality, the TLS-W🏹 Gatekeeper will frequently encounter cases with much higher values. It's entirely plausible that some law firms may only be interested in cases exceeding £50,000, dismissing anything below that threshold as not lucrative enough. Even so, for each qualifying case above £50,000 that comes through this system, the law firm stands to earn £7,500 or more—income they would have otherwise missed out on. Once claims surpass the £50,000 mark, they enter a territory where law firms would typically be eager to take them on, provided the cases are straightforward and score highly across all assessment criteria. Yet, if a claim is even moderately complex, the firm may still hesitate. The reason is simple: complexity translates to a significant investment of staff hours, making such work less attractive unless the likelihood of success is exceptionally high and the effort required is minimal. The TLS-W🏹 system addresses this by automating much of the groundwork, reducing the burden on law firms and making even complex, high-value cases more feasible to handle.

Cases The Law Firm Would Usually Take

For cases that law firms actively want to pursue, the entire process becomes far more streamlined thanks to the TLS-W AI system, which handles all the preliminary work. In these instances, lawyers simply need to reach out to clients, arrange meetings, and proceed using their standard procedures—yet with only a quarter of the workload they would normally face. I have observed AI systems developed by companies on Anthropic that are specifically trained as legal assistants. These systems can efficiently review all relevant precedents and become adept at drafting contracts—a considerable portion of legal work. However, TLS-W🏹 is fundamentally different. While it will also be trained on vast legal datasets to understand precedents and every facet of the law, its approach diverges in key ways. Current AI assistants are designed to support lawyers or paralegals, not to engage with the public directly. They do not motivate or incentivise users through gamified interfaces that make the process engaging by awarding points for progress. Most importantly, they don't generate complete legal claims that are automatically sent to the NHSR or other opposing parties as part of a legal dispute. TLS-W🏹, on the other hand, is built precisely to bridge these gaps, creating a platform that is both accessible and empowering for non-experts.

Franchising revisited

Imagine TLS-W as an online platform designed to generate real financial benefits for people who find themselves in challenging legal situations. The model is simple yet powerful: a law firm can feature the system on its homepage, offering its associates, members, and staff the opportunity to create franchises. This approach opens the door for hundreds or even thousands of individuals globally, each motivated to help someone with a legal claim—because the promise is compelling: with this system, there’s always a winning strategy.

To revisit my initial point, my current focus is on developing strategies for success prior to constructing the system.

In essence, my current focus is not only on conceptualising and outlining the system’s design, but also on devising a pathway to genuine success—one that does not rely on traditional legal training or years of experience. Instead, I am exploring how someone with no legal background, empowered solely by the capabilities of AI, can navigate and ultimately triumph in this space. This pursuit—demonstrating that meaningful legal victories are within reach for anyone, not just seasoned professionals—lies at the heart of what I’m working on now.