Gynger Innovation, Technology & Agility

What types of products or services does your engineering team build? What problem are you solving for customers?

At Gynger, we are building the only accounts receivable platform with embedded financing specifically designed for the technology sector.We solve a core B2B problem: buyers want flexible payment terms, sellers need cash upfront. Our platform lets tech vendors offer custom terms to customers while getting paid immediately.

We offer three products. Gynger Receivables is an AR automation giving finance teams receivables visibility, automated collections and cash flow insights. Gynger Capital is a financing engine providing upfront vendor payment and flexible buyer terms. Involves credit decisioning, pre approvals flows and qualification, lending limits and payment processing. Gynger Pay provides a seamless offer-to-payment flow handling invoice presentation through payment processing.

Tell us about a recent project where your team used AI as a tool. What was it meant to accomplish? How did you use AI to assist?

We use AI extensively across our product team, from writing and grooming tickets to reviewing PRs. Developers choose whatever tools provide them the most value. Personally, I lean heavily on Claude Code for planning and debugging work. There are few bug tickets I tackle without first asking Claude what the issue might be.

AI coding tools evolve rapidly and we maintain an open mind about leveraging them responsibly while shipping production-ready code faster.

You ask about a specific project, but the reality is AI is embedded in all of them at some level. It’s not a separate proof of concept or experimental consideration, it’s simply part of our daily development process. With the advent of model context protocol agents and third parties opening up their capabilities to models like ChatGPT and Claude, the leverage available to developers and product teams is only expanding.

What would that project have looked like if you didn’t have AI as a tool to use? 

Since AI is part of our daily workflow, the impact is cumulative rather than project specific.

Without AI, I’d spend significantly more time context switching. Debugging our credit underwriting flow means tracking down logs and tracing data through services. With AI tools, I describe symptoms, paste code and get a hypothesis in seconds, collapsing 20 minutes of investigation into two.

Code reviews would be slower. AI catches obvious issues, missing error handling, race conditions, unclear naming, before I push. Without it, those surfaces in PR comments require another round trip.

Documentation takes less time. Translating vague bug reports into structured tickets with repro steps and acceptance criteria used to take 15 to 20 minutes. Now it’s more like five minutes. The bigger shift isn’t speed, it’s cognitive load. AI handles pattern matching and boilerplate, letting me focus on architecture and business logic that requires human judgment. 

You still need strong fundamentals to know when AI is wrong. But with them, AI becomes a force multiplier for shipping quality code faster.