How Belfry streamlined its quote-to-cash process

“We went from people interpreting contracts to a system that actually understands them. That shift changed how confident we are in billing and cash collection.”

— Jordan Wallach, CEO of Belfry

By the numbers

Learn how Belfry streamlined its quote-to-cash process by implementing FullSeam's AI agents.

• 95% of invoices are fully auto-validated by AI before reaching Finance

• 40–60% reduction in billing-related customer questions and disputes

• 10+ hours per month returned to the finance team without adding headcount

Company

Belfry Software is an all-in-one operating system for private security guard companies. The platform covers the core back-office and field workflows security firms run on: scheduling and shift management, timekeeping and GPS/location-based guard tracking, daily operations (incident reporting, messaging, client portals), and fully integrated payroll/HR and billing.

Belfry sells to security company owners, operations managers, and payroll/HR admins who need to run large, distributed workforces with tight margins and high compliance requirements—without stitching together spreadsheets and point solutions.

The challenge

As Belfry scaled, quote-to-cash became one of the most operationally fragile parts of their business.

Belfry sells into private security companies with complex operating models: variable guard counts, fluctuating schedules, customer-specific billing rules, overtime, shift differentials, and contract-specific pricing. As a result, their customer contracts are not simple “one price, one invoice.” Each customer contract embedded real operational nuance that had to be reflected accurately in billing, month after month.

Before FullSeam, that complexity leaked across systems and teams. Quotes were created in one place, contracts lived elsewhere, operational data came from Belfry’s core platform, and invoicing logic was ultimately reconciled by Finance using a mix of tools, spreadsheets, and manual checks. Small inconsistencies — a pricing exception, a contract amendment, a custom billing rule — routinely surfaced late in the process, after invoices were already drafted or sent.

This put Finance in a reactive position. Instead of operating a clean, repeatable billing engine, the team spent significant time reconciling what should be billed versus what the system could easily generate. Invoices were delayed while discrepancies were resolved, and edge cases required manual intervention. “The issue wasn’t effort,” CEO Jordan Wallach noted. “It was that too much institutional knowledge lived in people’s heads.”

As volume increased, the risks compounded. Billing errors created avoidable back-and-forth with customers. Disputes took longer to resolve because there was no single source of truth tying together quotes, contracts, and live operational data. Even when issues were caught internally, they often showed up downstream as slower collections and longer cash cycles.

Critically, the process did not scale cleanly. Adding customers or expanding existing accounts increased operational load on Finance, not just revenue. “We could see where this was heading,” Belfry CEO Jordan Wallach said. “More growth would have meant more manual work — unless we fixed the system itself.”

FullSeam's AI agents worked within Belfry's existing systems and closed the gaps

Belfry implemented FullSeam to bring intelligence and continuity to a quote-to-cash process that had become too complex to manage manually.

Instead of relying on finance and operations teams to interpret contracts and translate them into billing logic each month, Belfry now uses FullSeam’s AI agents to understand each customer agreement in context. FullSeam ingests Belfry’s quotes, signed contracts, amendments, and live operational data from the Belfry platform, then continuously reconciles them to determine what should be billed — and why.

For Belfry, this was critical. FullSeam’s agents understand custom terms, seamlessly handle billing and accounting, and remove the need for manual interpretation. “We went from relying on each account manager remembering how the customer's custom terms would affect their billing to a reliable automated system,” the CEO said.

As new contracts are signed or existing customers change scope, FullSeam automatically detects changes and updates downstream billing logic. Invoices are generated only after the AI has validated that pricing, usage, and terms align across systems. When something looks off, FullSeam flags it early and routes it with context, instead of letting it surface as a customer dispute weeks later.

The net effect is that quote-to-cash now runs continuously, not episodically. Finance no longer spends end-of-month cycles reconciling exceptions, and Operations doesn’t need to field billing questions tied to contract nuance. “The biggest shift was confidence,” the CEO noted. “We trust that what goes out the door matches what we agreed to.”