A personal look at how smarter, NLP-driven systems, not hype or haste, will shape the future of legal decision-making.
Date:
Nov 17, 2025
Quality Over Speed in Legal Tech
Not long ago, a good friend asked me a pointed question during a discussion: “Is speed the ultimate goal for AI in law firms or teams?” That question hit at the core of a common misconception. In many industries, faster is better, but in the legal domain, being correct, secure, and thorough trumps being lightning-fast every time. A rushed but flawed legal analysis can be worse than no analysis at all. After all, what good is a lightning-fast answer if you can’t trust its accuracy or if it leaks sensitive client data?
In fact, recent research confirms that AI can boost efficiency but doesn’t automatically guarantee quality. One study found that using tools like ChatGPT led to “consistent and significant improvements in the speed” of legal tasks, enhancing drafting speed by up to 32%, but without a comparable improvement in output. In other words, AI helped law students write quicker, but it didn’t make their work substantively better or more correct. This echoes what many of us in the field feel intuitively: speed is nice, but quality is non-negotiable.
And this is not just an academic problem. PwC’s 2025 Law Firm Survey shows firms face the same reality. 36% of firms report writing off 15% or more of their revenue because key obligations, scoping details or contract terms slip through the cracks after signing. The pressure for “speed” often creates more hidden problems than it solves.
Quality concerns aren’t just academic, they’re very real for practicing lawyers. A 2025 industry survey revealed that among professionals hesitant about using AI, 40% cited accuracy and reliability as their primary concern, nearly double the next biggest worry. And we’ve all seen why. There have been headline-making incidents of “smart” systems hallucinating fake case citations or facts, which led to embarrassed lawyers and even court sanctions. (Yes, this has happened more than once!) Judges have little patience for errors from an AI; one court bluntly noted that the duty to check the accuracy of citations “trumps all” other considerations when it comes to using AI in legal work. The lesson is clear: legal tech must assist in getting things right, not just getting things done fast.
Security is the other side of this coin. Legal work deals with highly sensitive information: client secrets, confidential contracts, personal data. If a shiny new AI tool requires uploading confidential documents to some cloud where we lose control of the data, that’s a non-starter. It’s no surprise many law firms are extremely cautious about AI. Some firms have even issued guidelines or outright bans on tools like ChatGPT until privacy and security issues are resolved. The risk of inadvertent data exposure or breaches of attorney-client confidentiality is simply too great. Any technology we adopt must protect data with the same zeal as a lawyer bound by professional secrecy.
PwC reinforces this: 92% of law firms list cybersecurity as their top strategic risk for the coming year. This aligns exactly with what we are building toward, systems that prioritise control and security over model size or speed.
So, when it comes to legal tech, speed and flashy capabilities take a backseat to trust, accuracy, and security. Our venture embraces that reality. We believe that slow is smooth, and smooth is fast – meaning if we prioritize doing things right and securely now, we actually save time (and headaches) in the long run.
Rethinking "Smart Systems" Beyond the Hype
Buzzwords like “AI” and “smart systems” get thrown around a lot, but smart doesn’t always mean throwing a giant neural network at the problem – especially in law. We take a broader view of what a “smart legal system” entails. It’s not necessarily about an all-knowing superintelligence replacing the lawyer; it’s more often about augmenting the lawyer’s capabilities through a combination of reliable technologies.
Some of the components of our approach include:
Natural Language Processing (NLP): Legal work is text-heavy – from contracts and statutes to case law and emails. NLP can help sift and summarize vast amounts of text, extracting key terms, clauses, or precedents. Importantly, these AI-driven text analysis tools can be designed to minimize hallucinations and stick to what’s actually in the documents. For example, an NLP-based system might quickly find all clauses related to indemnification across hundreds of contracts, a task that would take a human days. This isn’t sci-fi; it’s a practical use of AI that’s already proven its worth in legal research and document review.
Data Visualization: Sometimes the best way to understand legal information is to see it. Timelines of events in a dispute, relationship maps linking entities and contracts, or trends in case outcomes by jurisdiction – these visual insights can be incredibly “smart.” They help lawyers make sense of complex data at a glance, leading to better decisions. A well-designed dashboard or visualization can show patterns (for example, which litigation strategies worked most often in similar cases) that might not be obvious from reading dozens of dense documents.
Knowledge Management & Data Availability: A system is only as smart as the data it can access. We’re focusing on ensuring lawyers have complete, organized, and instantly accessible information when they need it. This might mean integrating internal knowledge bases, prior case files, public databases, and more, all while respecting access controls and confidentiality. “Smart” also means not reinventing the wheel – if the firm solved a similar issue last year, the system should surface that precedent or document rather than starting from scratch. Sometimes the innovation is in connecting the dots and making data available when and where it’s needed. PwC’s survey underscores this need for visibility and access: firms are now prioritising improved internal reporting and data discipline because too much critical information never reaches the right people. This is exactly what happens when contract details live in folders, inboxes, PDFs or inside someone’s head.
Partial Process Automation: Rather than trying to automate a legal matter end-to-end (which is risky and often unrealistic), we target specific, well-defined tasks for automation. Think of things like initial contract draft review, compliance checklists, or drafting routine sections of a legal memo. By partially automating the repetitive or labor-intensive parts of a process, we save human lawyers time and energy – but we always keep the lawyer in the loop for the final review and decision. Interestingly, this approach can not only save time but also improve the quality and consistency of work by reducing human error. When a machine flags, say, the 5 out of 100 documents that really need attention based on predefined criteria, it ensures those critical pieces don’t get lost in the shuffle. Automation isn’t all-or-nothing; sometimes the biggest wins come from a well-placed assist, not full autonomy.
Each of the above elements might sound less glamorous than an AI that magically does a lawyer’s job. But glamour isn’t the goal – effectiveness is. Smart systems in law have to earn the trust of legal professionals. That means being transparent, reliable, and fitting into the workflow rather than disrupting it.
Any tool we build must feel like it was designed by lawyers for lawyers. Too many tech solutions that looked great in theory but failed to gel with how legal professionals actually work and think. Legal decision-making is a careful, contextual process; a smart system should respect that.
“Technology should support a lawyer’s judgment, not override it.”
That perspective keeps us grounded: if a feature doesn’t ultimately help a lawyer make a better decision (or save them time without introducing new risks), does it really belong in the product? This philosophy drives us to focus on augmentation over automation – giving lawyers better tools, not trying to substitute for their expertise or judgment.
Building a Vision in Stealth Mode
With these principles in mind, we set out to build something new. Our venture is still in stealth mode, so I can’t spill all the details yet, but I can share our vision at a high level. We’re creating a legal tech platform that embodies everything I’ve discussed: prioritizing accuracy, security, and usability, even if that means forgoing the latest hype.
We like to say we’re not just another AI startup throwing a large language model at legal problems. In fact, we deliberately avoided the “just plug in a GPT and go” route. Instead, we’re combining proven techniques (like the NLP and data integration methods mentioned above) with our own domain expertise to address specific pain points lawyers face. The goal is a system that integrates into a lawyer’s daily routine seamlessly. It might not write a perfect brief at the click of a button, but it can do the heavy lifting of research, organize facts, check inconsistencies, and provide a sort of intelligent second pair of eyes. Imagine a kind of smart legal assistant that works tirelessly in the background: flagging risks in a contract, reminding you of a forgotten compliance step, or instantly pulling up that one case that could strengthen your argument, all while keeping client data locked down tight. That’s what we’re aiming to deliver.
Personally, I’m excited about this approach because it aligns with my core belief about legal innovation: we can transform legal work for the better without losing the rigor and caution that defines the profession. If we get this right, the payoff is huge, not in some flashy “AI replaces lawyers” way, but in a much more meaningful way. It means lawyers freed from drowning in paperwork and mundane tasks, so they can focus on strategy, advocacy, and client counsel. It means fewer missed details that could cost a case or a deal, because our system quietly caught an issue early on. And it means even smaller firms or under-resourced legal teams can punch above their weight by leveraging an intelligent partner that amplifies their capabilities.
We’re still at the start of this journey, and there’s a lot of work ahead. Being in stealth mode has its downsides, I’d love to shout about what we’re building from the rooftops, but it’s also allowing us to iterate carefully without external pressure, making sure we uphold those values of precision and security from day one. Rest assured, when we emerge, we want to have something real and solid to show, not just a pitch deck full of promises.
Seeking a Technical Co-founder
To bring this vision fully to life, we’re looking for a key player: a Technical Co-founder. This is a call to the builders and innovators out there who share the passion for applying technology to legal in a thoughtful way. If you’re an engineer or technologist who gets excited about NLP, data architecture, and creating intuitive tools, and if the mission I’ve described resonates with you, I’d love to talk. We have an early prototype and a clear roadmap, but we know that finding the right technical partner is crucial to accelerate development and help shape the product’s future.
Feel free to reach out to me directly if this opportunity speaks to you or someone you know. Even if you’re just curious about what we’re up to in our stealthy corner of legal tech, I’m always open to conversations. Building something great is a team effort, and the best teams are those who share values and vision.
In closing, I’m genuinely optimistic about the future of legal tech when it’s done right. By prioritizing precision over speed, and wisdom over hype, we can create tools that not only make legal work more efficient but also more reliable and secure. Our venture is just one attempt to contribute to that future. I’m excited for the road ahead and invite anyone interested, be it potential collaborators, future users, or fellow legal tech enthusiasts. Join the discussion! After all, reinventing an industry is not a one-person job, and the more minds tackling these challenges, the better.
