Historically, people-based workflows scale in a linear progression. Want to get more done? Add more people. This type of scaling is expensive and inefficient and often impossible to achieve in an environment where rapid throughput increase (super-scaling) is needed.
Super-scaling a workflow requires rethinking the process itself. This involves essentially applying the theory of constraints to the process to identify the limiting factor(s). Once the process constraints are known, an evaluation can be done to see how automation could be applied in targeted ways to remedy those constraints.
That’s precisely the kind of transformation we engineered in partnership with a technology progressive client in the legal industry.
They had a people-based workflow for processing complex and time-sensitive documents and needed to super-scale the process to meet the business’ demands. This is within a highly regulated industry where there are severe penalties for missteps.
Breaking the Daily Bottleneck
The first process constraint showed up in the inbox. Each morning brought thousands of emails, many carrying legal documents and case updates that had to be matched to the right records without delay. For years, this was done manually. It worked in the early days, but as volumes climbed, the process became unsustainable. Careful processing was slow and led to frequent delays. Rushed processing increased the risk of errors and the subsequent severe consequences resulting from a breach of regulatory mandates.
Our answer was targeted automation designed to go beyond standard rule-based workflows. We built a custom email dispatcher using Natural Language Processing (NLP) to interpret messy subject lines, extract intent, key entities such as case numbers and client names, and provide context from unstructured text. By also analyzing attachments, the system automatically matched messages to the correct records with over 95% accuracy, routing them into the right workflow in seconds and reducing manual effort and errors.
When there was uncertainty, such as two possible matches or missing references, the system escalated to a human reviewer. But in the majority of cases, it processed messages automatically, accurately, and at speed.
The result: more than 80% of incoming messages were handled without manual effort. Human workloads dropped, turnaround times improved, and the processing team gained breathing room to focus on higher-value tasks.
Regaining Control Over Data Costs
The second constraint we addressed was a cost constraint incurred through the per-use fees of a third-party provider. As volume super-scaled, so did the third-party costs.
Instead of accepting those costs, we engineered alternatives. Many of the most common queries were redirected to in-house services that leveraged government APIs for public legal records, along with smart caching to avoid duplicate lookups. This meant the system could find and reuse existing results instead of paying for the same information twice. External platforms remained part of the toolkit, but only when they delivered unique data.
This shift cut related expenses by nearly 50% within a single quarter. That freed valuable budget dollars for investing in further innovation. Even more important, it gave the business control over its own data strategy and largely avoided being locked into expensive third-party costs.
Engineering for Resilience and Scale
These gains were built on a modular, microservices-based architecture, designed for scalability and reliability. The system used message queues to process work asynchronously, so it could handle sudden spikes in activity without crashing or slowing down. Each service ran independently, allowing updates or changes to be made without disrupting the rest of the platform.
This design gave the business both the stability of today and adaptability for tomorrow through an infrastructure capable of supporting day-to-day operations while scaling seamlessly as demand increased.
More Than Technical Results
The true outcome was not just faster processing or lower costs. It was control. Control that came from visibility into processes and the discipline of consistent execution. With that control, the business could deliver faster service without sacrificing quality, maintain compliance in a shifting regulatory environment, and plan growth initiatives with fewer operational risks.
The result was not only efficiency, but a foundation strong enough to support future expansion, including the integration of AI capabilities to unlock new levels of automation and decision-making.
Engineering Growth That Works for You
When thoughtfully designed, technology isn’t just a background function. It is an accelerator of business performance, allowing organizations to grow with stability, adapt quickly, and seize opportunities. This is true even in areas of the business which are high in complexity and operating in a highly regulated context.
In this case, the business objective was multi-faceted. The initial aspect was the challenges of overwhelming data volume and third-party costs highlighted in this article. The second was a different but equally complex problem: transforming inconsistent, paper-heavy legal documents into accurate, actionable data at scale.
That’s where the story continues in an upcoming part 2 of this article series.
At RIA Solutions Group, we believe technology should serve the business, not the other way around. That’s why we combine technical expertise with a deep understanding of business priorities to deliver solutions that make a measurable impact from day one.
Grigorie Marcus
Team Leader at RIA Solutions Group
