How We Used AI to Build Jibble’s Invoicing Feature in Record Time

Building a full invoicing feature—from time tracking and client management to PDF generation usually takes weeks, if not months. Requirements gathering, documentation, design iterations, backend scaffolding, UI polish, help articles, marketing pages… the list goes on.

This time, we tried something different.

We leaned heavily into AI across product, design, development, and marketing—and built Jibble’s invoicing feature not in a quarter, not in a month, but in three blisteringly productive days. And we did it with a lean, four-person team: two product managers, a frontend engineer, and a backend engineer.

In this post, we’ll show you exactly how we did it, and why this isn’t just a feature launch or a shortcut, it’s a blueprint for the future of development.

Why Invoicing, and Why Now?

For years, Jibble has been the trusted companion for teams tracking their time. But there was always a gap—a frustrating, productivity-killing gap between tracking hours and getting paid for them. For many teams, agencies, contractors, and freelancers, the real end goal is simple: get paid accurately and on time.

Yet for many businesses, the reality looked more like this:

1. Track time in Jibble (efficient!)
2. Export CSV reports (clunky)
3. Manually recreate hours in accounting software (error-prone)
4. Chase approvals (time-consuming)
5. Send invoices (finally!)
6. Cross fingers that the numbers match (stressful)

This wasn’t just inefficient, it was costing our customers time, focus, and real revenue.

With invoicing built directly into Jibble, that gap disappears. Tracked hours flow seamlessly into invoices—no spreadsheets, no double entry, no guesswork. Just a clean, end-to-end path from work done to money in the bank.

It’s a natural evolution of time tracking, and one that finally connects effort to outcomes.

So we asked ourselves: How fast can we build this, and build it well?

Turns out—very fast, with the right tools.

Day 1: From Blank Page to Living Prototype

Day one started with conversations. What should invoicing look like inside Jibble? What problems must it solve on day one, and what can wait?

Instead of weeks of back-and-forth, we used AI to accelerate clarity.

Within minutes, ChatGPT and Gemini delivered structured specifications, edge cases we hadn’t considered, and even suggested user personas. 

Rather than drafting specs after the meeting, we did it live—product discussions and specifications evolved in real time, with AI facilitating the exploration of workflows, edge cases, and feature boundaries on the fly. 

Once the requirements were clear, we moved straight into design. Using the finalized specs, we generated a design prototype with Figma Make, guided by a well-crafted directive. 

What came back wasn’t just wireframes, it was a clickable prototype with:

• A dashboard showing invoice statuses (draft, approved, paid, void)
• A “Create Invoice” flow with two clear paths: “From Tracked Time” and “Manual Items”
• Realistic PDF previews

We iterated with prompts like “Apply Jibble’s existing table component styling to the invoice list.” and “Align action button placement with our project page patterns.” By attaching a reference frame from Jibble’s current UI, we guided the AI to extend our design system, ensuring everything felt like they belonged.

Each revision took seconds, not hours, and with each pass, the experience became sharper and more deliberate. After just a handful of refinements, we had something unprecedented: A prototype that felt unmistakably “Jibble”—clean, intuitive, and practical.

Day 2: The AI Development Sprint Begins

With specs and designs locked in, development kicked off. This is where AI really started to shine.

Our frontend engineer opened our existing codebase and told ChatGPT: “Here’s our current component structure. Generate an InvoiceTable component that matches our existing data-table patterns, with sorting, filtering, and status badges.”

What would normally take half a day was done in 20 minutes.

GitHub Copilot and Cursor became our relentless pair programmers:

• They understood and adapted to our existing codebase and conventions
• Generated components and services that fit our architecture
• Suggested optimizations we hadn’t considered
• Even caught potential bugs before they happened

The result? Less time scaffolding, more time refining the experience.

On the backend, we focused on building a solid foundation for invoicing. What we did:

• Created a template and proof of concept for the Invoices service
• Kept it detached from the main backend initially (no auth, cache, or message bus) to move faster
• Planned to integrate fully once the shape of the service was validated

Cursor’s agent mode (noted as “noticeably better” than other tools) was excellent for kickstarting smaller PoCs quickly, outperforming alternatives for exploratory work.

The Confluence MCP (Model Context Protocol) connected directly to our documentation, ensuring the PoC aligned with our existing patterns. Postman AI then generated the API tests automatically.

That said, not everything is magical. The biggest blocker? AWS infrastructure setup. As our engineer put it: “Nothing can help with that—only patience.” Some things remain gloriously, stubbornly human.

By the end of Day 2, invoicing wasn’t just an idea; it was taking shape, fast. 

Day 2 (Yes, Still): Marketing Starts Early

Instead of waiting until the feature was “done,” we built marketing in parallel while engineers polished.

Using platforms such as ChatGPT, DeepSeek, and Gemini, we drafted:

• Marketing pages around invoicing 
• Help articles for creating and managing invoices
• Launch email sequences, blog posts and social media announcements 
• Visual assets for announcements and social media posts
  
• In-app onboarding flows and guided product tours
• Interactive demos (via Supademo) to showcase the feature in action

Because the product specs already existed, AI could generate accurate, consistent content—fast. 

The result? A fully connected experience for when the feature goes live: from discovering the feature and onboarding, all the way to creating the first invoice.

Day 3: Polish, Test, and Refine

Day three was about tightening everything up. We focused on:

• More rounds of development and iterations on both frontend and backend—again, heavily AI-assisted
• End-to-end testing using Gemini and TestRigor, with AI-assisted test case instructions prompted via Gemini to ensure coverage of all real-world scenarios
• Polishing UX details, including responsive layouts, error handling, and button placement to ensure the prototype followed Jibble’s existing design language
• Continuing marketing work in parallel, so everything could be shipped together

By the end of Day 3, the invoicing feature was built, tested, and ready internally, delivering a seamless flow from tracked time to paid invoices, entirely within Jibble:

• Time-to-Invoice Conversion – Turn tracked hours into professional invoices in one click
• Flexible Manual Invoicing – Create custom line items for products, projects, or retainers
• Smart Status Tracking – Draft → Approved → Paid
• Professional PDF Export – Branded, compliant, beautiful invoices
• Secure Sharing – Send via link, or export in PDF
• End-to-End Reliability – Fully tested across workflows to ensure accurate billing
• Demo Videos & Visual Assets – Walkthroughs, social media graphics, and help documentation

The Result: Jibble Invoicing is Built

In 72 hours, we went from “Can we build this?” to “It’s ready for clients.”

What we built isn’t a prototype—it’s a complete, production-ready invoicing experience designed to close the gap between time tracked and revenue earned. 

AI didn’t replace product managers, designers, or developers. It removed friction. It helped us:

• Move from ideas to clear specifications in hours, not days
• Design with confidence instead of guesswork
• Build faster without sacrificing quality
• Create marketing and documentation alongside development

But the real story isn’t the feature—it’s how we built it: a small team thinking bigger, moving faster, and working smarter with AI as a force multiplier.

What used to take weeks now takes days. What used to take days now takes hours. We’ve shown what’s possible—the only question left is: What will you build next?