With a deep background in enterprise SaaS technology and software architecture, Vijay Raina is at the forefront of tracking the evolution of AI from general-purpose models to specialized, high-stakes applications. We sat down with him to explore two groundbreaking developments: a new post-transformer AI architecture inspired by the human brain that promises continual learning and an almost infinite attention span, and a specialized AI platform designed to navigate the complex, high-stakes world of legal litigation. Our conversation delves into how brain-like structures can solve the memory and hallucination problems plaguing current LLMs, the innovative concept of “gluing” models together like Lego blocks, and the critical guardrails needed for AI to earn trust in a field where factual accuracy is paramount. We also touch on the clever use of synthetic data to train models when real-world data is off-limits and what the future holds for AI’s role in the legal profession.
Your post-transformer model is inspired by the brain’s structure. Could you walk us through how concepts like local neuronal activations and synaptic strengthening create intrinsic memory, and what practical advantages this offers over the massive context windows and RAG systems used with traditional LLMs?
It’s a fundamental shift in thinking. We looked at the brain, this incredibly efficient system with 100 billion neurons packed into a light structure, and realized it learns continuously without needing to see “all the soap data in the world” to know not to eat soap. Our architecture mimics this. Think of neurons as small computational entities connected by synapses. When a new piece of information arrives, certain neurons fire up and send a message to their neighbors. If a neighbor deems the message important enough, it fires too, and crucially, the synaptic connection between those two neurons gets stronger. This process is local and builds memory directly into the fabric of the model. Unlike traditional models where context is something you feed in and is limited by hardware, here, the context is the model. The synapses are the state, and that state is constantly evolving. This completely obviates the need for cumbersome RAG systems or worrying about context window limits; your memory is essentially as vast as your hardware allows.
The ability to “glue” models together like Lego blocks is a fascinating concept. What specific architectural properties allow this fusion, and could you provide a step-by-step example of how a model trained for legal analysis could be combined with one for finance to create a new, emergent capability?
The secret lies in the model’s structure and how it grows. Because the architecture relies on local interactions and is inherently distributable, it charts very well. It’s not a monolithic matrix; it’s more like a dynamic network. This allows us to literally connect two separately trained models. Imagine you have one “baby dragon” model trained exclusively on your legal department’s data and another trained on your finance department’s processes. Step one is simply to establish a connection between them—you physically link the models. Initially, they can immediately start passing messages, allowing for a rudimentary mix of languages and concepts without any new training. Step two is to introduce a task that requires both domains, like analyzing the financial implications of a potential lawsuit. As the models work on this, new synaptic connections will naturally form between the legal and financial neurons. This isn’t just concatenating outputs; it’s creating a higher-level judgment, a new capability that didn’t exist in either individual model, much like how a person with expertise in both law and finance can provide insights that a separate lawyer and accountant could not.
Traditional LLMs often struggle with hallucinations and lose focus during long, complex tasks. How does your model’s architecture, with its focus on continual learning and reasoning pathways, maintain its attention span? Please share a real-world scenario, like a multi-week business process, where this is a game-changer.
This is precisely the problem we’re solving. Hallucinations often happen when a model loses the thread of a long conversation or task. The current benchmark for a top model like GPT-4 is keeping focus for about two hours and 17 minutes before the success rate drops to around 50%. Our architecture’s intrinsic memory fundamentally changes this. Because time and sequence are baked into the model’s state through synaptic plasticity, it doesn’t “forget” the initial goal. Consider a complex end-of-quarter financial closing process. This can be an eight-week ordeal involving ten different departments. A traditional LLM would need to be re-prompted constantly and would struggle to track all the dependencies. Our model, however, can maintain a persistent state throughout the entire process. It learns from an issue in week one in the sales department and remembers it when processing data from accounting in week five, maintaining focus and ensuring a coherent, error-free outcome over a period that is simply impossible for current transformer-based models.
Given that lawyers are ultimately responsible for accuracy, how do your “Confidence Tooling” features, like “Inferred Dates” and “Relevance Rationale,” work in practice? Could you describe the user experience and how these guardrails help a litigator trust, yet verify, the AI’s output from messy evidence?
Trust is everything, and we know every error erodes it. So, we built what we call “Confidence Tooling” to make the AI a transparent partner, not a black box. A litigator is often faced with incredibly messy evidence. For instance, a key event date might be buried in a complex table of medications. Our “Inferred Dates” feature won’t just pull out a date; it will flag it with a message saying, ‘We’ve inferred this date from the top of the chart,’ and provide a direct link to that exact spot in the source document. The user sees the AI’s reasoning and can verify it with a single click. Similarly, with “Relevance Rationale,” we don’t just say a fact is “high relevance.” The UI will display an explanation generated by the LLM, stating precisely why it’s considered relevant in the context of the case. It’s about arming the lawyer with tools to quickly get to the original source and understand the AI’s logic, building confidence by making verification seamless.
Your platform first creates a “fact layer” before vectorizing documents. What is the strategic advantage of this approach for litigators who may not know the right questions to ask initially? Please detail how this structured layer provides more utility than a standard semantic search over raw documents.
The key insight here is that in litigation, you often don’t know what you’re looking for at the start. Jumping straight to vectorizing raw documents forces you into a question-and-answer paradigm, which is limiting if you haven’t formulated the right questions yet. Our approach is different. We first use LLMs to perform an objective pass over all the evidence—thousands of pages—and extract every potential fact, whether it’s an allegation, an event, or a statement. This creates a structured “fact layer.” It’s like having a detailed, searchable index of the entire case before you even begin your analysis. This layer provides immense utility because it allows a lawyer to see the landscape of the evidence, identify patterns, and discover unknown unknowns without having to conduct a blind semantic search. We then vectorize both the original documents and this fact layer, which gives our RAG system far more power to answer complex questions later on, because it can draw connections from a pre-digested, structured set of facts rather than just raw text.
You cannot use private client data for training, so you create synthetic data based on public judgments. What are the biggest machine learning challenges in this process, particularly in maintaining factual consistency across thousands of simulated documents, and how have you overcome them?
This is one of the most interesting and difficult challenges in legal tech. Unlike legal research tools that can train on public case law, we absolutely cannot use any private customer data. So, we’ve become very good at creating high-quality synthetic data. The biggest challenge is maintaining factual consistency. It’s not enough to just generate a fake invoice; we have to simulate the entire ecosystem of evidence for a case. We might take a public judgment and then work backward to simulate the thousands of pages of emails, contracts, and reports that might have led to that outcome. The hard part is ensuring a character’s name, a specific date, or a monetary amount remains perfectly consistent across thousands of documents in that synthetic bundle. A lot of our team’s effort goes into building systems that can track these entities and relationships across a massive context, ensuring the synthetic case is as complex and internally consistent as a real one. It’s a massive AI and engineering challenge, but it’s the only way to build a powerful model without compromising client confidentiality.
What is your forecast for AI’s role in the legal field over the next five years?
Over the next five years, AI will move from being a peripheral research tool to a core component of a lawyer’s daily workflow. We’ll see a shift away from just using AI for precedent research towards tools that actively manage the factual basis of a case, like what we’re discussing. The focus will be on augmenting the litigator, not replacing them. AI will handle the monumental task of sifting through and structuring evidence, freeing up lawyers to focus on strategy, interpretation, and argument. We’ll also see more integration, where a firm’s internal knowledge—the IP from its past cases—is leveraged by AI to provide insights on new ones. The biggest hurdle will continue to be trust, so the platforms that succeed will be those that prioritize transparency, verifiability, and building robust “confidence tooling” directly into the user experience, ensuring the lawyer always remains in control.
