How a National Asset Management Platform Democratized AI for Operational Efficiency

Note: The customer name and specific individuals have been anonymized for this case study.

The Challenge: Breaking the "Dinosaur" Cycle

For a mid-sized asset management platform, the path to growth meant transforming how their organization operated. Born out of a large financial institution, the company managed vast amounts of proprietary transaction data but was weighed down by legacy processes and a self-described "dinosaur" reputation in a fast-moving market.

Their leadership recognized that to compete with agile fintech startups, they needed to move faster. The goal wasn't just to adopt AI, but to democratize it—empowering employees across Finance, Operations, Sales, and Engineering to build their own tools without waiting on a constrained engineering team.

"We needed a way to get people off the sidelines," said one of the company's executive sponsors. "If we just gave them a chat window, they'd write emails. We wanted them to build actual workflows."

The Solution: A Safe Sandbox for Citizen Developers

The company partnered with elvex to create a secure, enterprise-grade AI environment. Unlike rigid off-the-shelf tools, elvex provided a flexible "sandbox" where power users could safely work with top-tier models like Anthropic’s Claude, OpenAI’s ChatGPT, and Google's Gemini, all hosted in their existing Microsoft Azure Foundry instance to keep their proprietary data secure.

The strategy centered on a "Power User Program," identifying subject matter experts in key departments and giving them the training to turn their manual headaches into automated agents. This approach allowed the company to bypass the bottleneck of centralized development and let the people closest to the problems build the solutions.

The Results: From Manual Slog to Automated Success

By empowering non-technical teams to build, the company saw immediate "lightbulb moments" where longtime employees realized they could solve their own technical problems.

1. The Contract Analysis Agent (Sales Operations)

One of the most critical bottlenecks in the organization was the review of lengthy purchase contracts, particularly for properties in complex jurisdictions. When a counter-offer was made, asset managers would often spend over an hour manually reviewing every page of the new PDF against the original to ensure no subtle terms had been altered without their knowledge.

A Sales Operations leader built a comparison agent in elvex. By simply dragging and dropping the original contract and the latest version, the agent instantly compared the two. In one early test, the agent flagged a critical discrepancy that a human eye might have missed: a closing date that had been quietly shifted by nearly two weeks.

"It takes an asset manager an hour or more to review each page... [The AI] flagged that the closing date was extended... that is a red flag. That is exactly the data point I need to know."
— Sales Operations Lead

2. The Asset Valuation Agent (Asset Management)

In the distressed asset world, pricing is everything. The team frequently dealt with conflicting property valuations where field estimates varied significantly from third-party appraisals. Resolving these discrepancies required a manager to pull up multiple lengthy PDFs, manually compare property attributes like square footage and repair estimates, and draft a synopsis for the valuation team. It was a manual, "swivel-chair" process.

Using elvex, a frontline manager built a valuation agent. Now, they upload the conflicting PDF reports, and the agent extracts the key attribute differences and drafts a dispute email synopsis automatically. This ensures consistency in how discrepancies are presented and frees up the manager to focus on negotiation strategy rather than data entry.

3. The Sales Analysis Agent (Data Analytics)

Every month, the executive team asked the same question: "Why did our conversion ratios change?" Answering this required two senior leaders to spend an hour each digging through transaction logs to find the root cause.

A VP of Operations used elvex to ingest raw transaction logs and created a trend analysis agent. When asked to compare previous quarterly performance to the current month, the agent correctly identified that the drop in performance wasn't due to operational failure, but a shift in the geographic mix of assets—specifically a higher volume of inventory in specific regions. What used to take two hours of executive time now takes seconds.

4. The Financial Reporting Agent (Finance)

The Finance team, typically bogged down by "blackout" periods and rolling forecasts, found a way to use AI as a second set of eyes. A senior finance manager built a consistency checking agent. By uploading draft monthly executive presentation PDFs alongside raw Excel data from their ERP, the agent could cross-reference numbers across slides to ensure internal consistency, flagging discrepancies before they reached executive review.

The Return on Investment

By shifting from manual data processing to AI-assisted workflows, the organization is already seeing significant efficiency gains across these pilot use cases:

• 80% Reduction in Contract Review Time: What previously took asset managers an hour per contract iteration now takes minutes, allowing them to handle higher transaction volumes without adding headcount.
• Faster Executive Decision Making: The time to answer critical "Why?" questions regarding operational metrics dropped from hours to seconds, enabling leadership to react to market trends in real-time rather than waiting for post-mortem analysis.
• Risk Mitigation: The ability to automatically catch subtle contract alterations or financial reporting discrepancies provides a layer of risk protection that is difficult to quantify but invaluable for a regulated financial institution.
• Strategic Shift: Perhaps most importantly, employees reported a shift in their daily focus. Instead of spending time gathering and verifying data, they are spending time acting on the insights—moving from data entry to true asset management.

The Takeaway

This deployment proved that you don't need to be a software engineer to build powerful software. By giving subject matter experts a safe, secure platform to build on, this legacy platform was able to turn static documents into dynamic agents.

While the journey began with a small group of "power users," the impact rippled across the organization. From Finance to Operations, employees stopped viewing AI as just a chatbot and started seeing it as a way to build their own solutions, proving that the best ideas for efficiency often come from the people doing the work every day.