Building systems is about processes. It's about data — all kinds of data: emails, financial records, contracts, databases, documents scattered across platforms and custodians. It's about integrations. All of those things require understanding of logic, data, and flow — and systems are larger than any particular platform, technology stack, or group within an organization. They're larger than organizations themselves. Systems cross all of those things.
My name is Clay Cash. I've spent most of my career in legal and litigation — environments where the problems are complex, the data is messy, the stakes are real, and there's always pressure. I've learned to stay calm in that pressure, and to help the people around me do the same. If you have interest in re-engineering how your organization works, I'd like to hear about it.
Let's TalkLegal and litigation work generates complex problems — tight deadlines, massive data volumes, high stakes, and teams under pressure. Large-scale systems don't live inside a single tool or a single team. They span platforms, departments, and sometimes entire organizations. Re-engineering starts with understanding — the data, the workflows, and the information that drives decisions. From there, we figure out together what needs to change. Calmly, methodically, and with care.
Over the years I've worked across eDiscovery, litigation support, data architecture, compliance, and privacy. The common thread has always been the same: someone has a complex problem, and they need help figuring out the right way forward. Whether it's a large-scale migration, an operations bottleneck, or a process that's buckling under the weight of the data running through it — it begins with listening.
Legal teams deal with enormous volumes of data — structured and unstructured, spread across email systems, document repositories, financial platforms, and legacy databases. AI and modern tooling have changed what's possible with all of that. But tools work best when they're guided by experience — understanding the domain, knowing the pitfalls, and having a clear picture of what "done right" looks like. That's the combination I try to bring: good tools, grounded in real-world context.
Litigation is expensive. A lot of that cost comes from processes that were built around the limitations of older tools — or from solving the same problems over and over because the underlying system was never quite right. Better architecture, thoughtful automation, and workflows designed for how people actually work under real pressure can make a meaningful difference. It's one of the most rewarding problems to help solve.
When teams are under pressure — court deadlines, regulatory demands, investigations that won't wait — productivity isn't about working faster. It's about removing the friction that slows good people down: unclear handoffs, manual steps that should be automated, data siloed in places nobody can reach. The calm, steady work of getting the right infrastructure in place is what lets teams focus on the problems that actually need their attention.
I started in the mid-90s building database systems. Over three decades, the tools have evolved — mainframes, client-server, web, cloud, and now AI. Each shift opened up new possibilities, and I've tried to stay curious and keep learning through every one of them. What hasn't changed is the nature of the work: large-scale systems, complex data, and people who need things to work under pressure.
Right now, I'm building agentic AI systems that automate real legal workflows — production systems that run overnight, handle quality checks, and know when to ask a human before proceeding. The years of experience help with knowing what to automate, what to protect, and where the edge cases tend to hide. And the years of working in litigation help with staying calm when it matters most.
If you're facing a complex problem — whether it's a litigation workflow, a large-scale data challenge, a platform that needs re-engineering, or an operation under pressure that needs someone calm and steady to help sort it out — I'd like to hear what you're working on.