As regulatory reporting matures into a data-driven discipline, n-Tier has emerged as one of the few technology firms able to bridge legacy fragmentation and the next generation of granular, real-time oversight. Speaking from n-Tier’s headquarters, Founder and Chief Executive Officer Peter Gargone describes a market reshaping around scale, consolidation and continuous validation – and a platform built to turn regulatory control into an intelligence layer.
Consolidation and Consistency
“The ongoing trend is for firms inclined to consolidate their reg footprints into a smaller number of vendors,” Gargone observes. Financial institutions “don’t want to have 15 reg vendors per region,” preferring a “validation framework” that applies consistent rules across jurisdictions.
n-Tier’s response is a configurable platform that supports multiple regulations and accommodates new Regs “without code rewrites”. It provides a “consistent approach to how you manage the data around the regs, the reporting of the exceptions and the correction process, giving firms a lot more flexibility and consistency,” says Gargone. Bespoke, one-off builds are “costing more in the end” and yielding “less than optimal” results, driving clients toward unified solutions.Beyond Templates
Across global markets, regulators from ESMA to the Monetary Authority of Singapore are exploring reporting frameworks that dispense with templates in favour of granular, API-based data exchange. Gargone acknowledges the benefits but cautions that entrenched infrastructures will make change slow, particularly in the US markets. “Data is the hardest part of what’s left in these tech platforms,” he says. “The regulators suffer from that just as much as everybody else … and the idea that you’re just going to kind of magically fix it is super hard.”
n-Tier’s architecture, he argues, “protects firms” from this reality by federating inconsistent source data “into a model where … it’s right regardless of what it kind of looks like behind the scenes,” allowing institutions to operate “without having to take on the risk of constantly reacting to … or having failures related to it”.
Scaling to Billions
That data-first approach has allowed n-Tier to reach volumes few vendors can match. “We’re at substantial volumes in the billions – individual clients putting five, ten billion records a day through our platform,” Gargone notes. Achieving that throughput while maintaining accuracy and searchability was “a substantial achievement.” With so much data consolidated, the next step is to build an analytics layer on top, “We have more data in one place than many of our clients … so we have the ability to layer more intelligence on top of the data.”Although every vendor now talks about artificial intelligence, Gargone takes a pragmatic view: “AI is really just massive compute,” he argues. For n-Tier, that compute power enables new diagnostic services. A key example is root-cause analysis; “When we find an error in the data, we want to get to the point where we can actually do the analytics on top of the errors, to tell clients where those errors came from and why they’re coming up … where the outliers are,” he explains.
Typical root causes range widely: For example, “You’re running along fine, and someone goes and changes your algo engine.” By tracing each error to its underlying process, n-Tier plans to deliver intelligence that traditional batch reconciliations cannot match.
That initiative defines 2026 for the firm. “We’re going step by step up that chain and ‘26 is really going to be the build-out of the analytics layers … the AI stuff,” Gargone says, describing pilots already underway across the client base to evaluate “what makes the most sense for them”.
Looking ahead to upcoming regulatory updates – e.g. FINRA SLATE in January 2026 – Gargone comments, “I would say from the clients we see, they’re pretty well prepared,” he notes. “Most of our clients tend to take this very responsibly.” For many of the newer regulations, n-Tier is now handling the reporting as well as the validation – a sign, he adds, of the platform’s growing maturity. He frames regulatory change as a recurring data-management challenge rather than a rule-specific issue. This approach enables firms to meet evolving requirements without fragmenting workflows or duplicating data processes.
The Future is Continuous Control
Looking forward, as regulatory data management evolves from periodic checks towards embedded control frameworks powered by scalable compute, firms are beginning to understand that control – “Is an ongoing process and not a one-off exercise of paying a consultancy to check the data,” notes Gargone.
In that sense, n-Tier’s trajectory mirrors the evolution of regulatory reporting itself – from periodic validations toward continuous, data-driven assurance. As Gargone puts it, “That’s the whole purpose of what we’re doing, and I don’t see the need for that going away”.
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