2024 - The Year of AI; Redefining Product Development at Tatango

For months, I’ve been grumbling to my colleagues about wanting to write more, and now here I am—finally penning my thoughts about the very AI that’s helping me do it. There’s a certain irony in using AI to reflect on how it’s reshaped Tatango engineering and product workflows, but it’s a testament to just how far we’ve come as a team.

AI has gone from being a curiosity to becoming a true copilot for our engineering organization. Across every step of the software development lifecycle (SDLC), it’s fundamentally changed how we build, test, and deliver features for our customers. AI isn’t just a "tool". It’s a partner in innovation, one that’s changing the way we work, think, and create.

Here’s the journey we’ve taken and the tools we use, broken down for engineers, product managers, and leadership teams alike.

Starting Small: Early AI Experiments

Last winter, we began experimenting with AI tools like GitHub Copilot for Business to automate test suite creation. While features such as code summaries and autocomplete showed promise, they didn’t drive widespread adoption or bring the meaningful change we hoped for.

Everything shifted with the arrival of GPT-4o last summer, which fundamentally altered how we approached software development. Its advanced capabilities empowered developers to take full ownership of test coverage, enabling them to write their own unit and end-to-end (E2E) tests with greater confidence and efficiency. This fostered a new level of control, accountability, and creativity within the team.

What made this moment transformative wasn’t just the introduction of a more advanced tool but the mindset shift it enabled. Developers began seeing AI not as a novelty but as a genuine assistant in their workflows. Freed from traditional constraints, they could innovate faster, tackle challenges more effectively, and deliver higher-quality work.

Recognizing the potential of AI to drive change beyond development, we expanded its use across the organization. By integrating AI into every stage of our workflows—from product design to testing, project management, and beyond—we unlocked efficiencies, enhanced collaboration, and raised the bar for what we could deliver. This marked a turning point where AI became integral to how we operate, setting the stage for its transformative impact throughout the company.

Revolutionizing User Research and Design

User research and design are critical to creating features that resonate, but they’ve traditionally required significant time and effort. With tools like FigJam AI, we’ve revolutionized this process by automating tasks such as clustering ideas, synthesizing feedback, and drawing actionable insights. What once took hours or days can now be done in minutes, ensuring faster and more thorough analysis.

On the design side, Figma AI enhances our ability to iterate quickly, offering suggestions for responsive prototypes and design improvements. These tools free our team to focus on creativity and strategy rather than repetitive tasks.

By integrating these AI-powered tools, we’ve transformed how we approach research and design, enabling us to act on feedback faster, explore more concepts, and deliver features that deeply connect with our users.

Making "Product" Management Effortless

AI has been a game-changer for our product managers, particularly in the execution aspect of building products. By leveraging AI, we’ve enabled them to focus on strategy and outcomes while automating routine tasks like feedback collection and ticket creation. This shift ensures that their efforts drive meaningful impact across projects, enhancing both speed and quality in product development. Tools like ChatGPT help streamline and automate feedback tickets into project management systems like Shortcut and Linear.

At Tatango we dogfood every feature that goes out the door, ensuring that it meets our high standards before reaching customers. Automating the collection and assignment of feedback through a variety of observability tools has been game-changing. This approach not only streamlines the process but also provides critical insights that help us quickly refine and perfect our products. By leveraging this automation, we’ve been able to maintain consistency, enhance visibility, and make data-driven improvements across all teams.

Eliminating Traditional QA

We’ve automated away nearly all manual QA and project management tasks related to testing, and the results speak volumes. In 2025 alone, we successfully rolled out 72 product roadmap items without a dedicated manual QA process. Tools like Cursor, Cursor Agent, and Windsurf handle test writing seamlessly, enabling developers to take full ownership by writing comprehensive unit and end-to-end (E2E) tests. This shift has empowered our engineering managers (EMs) and product managers (PMs) to focus on higher-value tasks. By automating the testing process, we have been able to concentrate our efforts on the features we push, rather than the labor-intensive work of writing and maintaining the tests behind them. This streamlined approach has significantly elevated our quality benchmarks and team efficiency.

Enhancing Communication with Tailored Release Notes

One area where AI truly shines is in generating release notes. We rely on a variety of communication tools—Slack, Intercom, Beamer, and Shortcut docs—to document and share information about features and updates. These tools generate a wealth of data that must be distilled into clear, audience-specific release notes. AI has become a critical part of this process, transforming how we communicate internally and externally.

By feeding data from Shortcut, feature documentation, and communication logs into AI, we can produce release notes tailored to different audiences. Customers receive concise, easy-to-understand updates highlighting new features and benefits, while internal teams, such as customer success and sales, get more detailed notes with technical context and implementation details. This ensures everyone has the information they need, presented in the most relevant way for their role.

Prototyping New Features with AI

When faced with the challenge of integrating charting and graphing capabilities into our app, we used AI-driven tools to supercharge our prototyping process. Instead of relying solely on traditional development environments like VSCode, we enhanced our workflow by feeding Figma designs into tools like Cursor and Windsurf.

These tools allowed us to rapidly prototype three charting libraries in TypeScript and React—Recharts, ECharts, and NEVO. What would typically take an entire sprint or more to evaluate and refine was completed in just a few days. By automating much of the initial development and testing, we quickly identified NEVO as the best fit for our design and functionality requirements.

This example highlights the transformative power of AI in accelerating prototyping. It turned what could have been a weeks-long decision-making and development process into a fast, efficient workflow, enabling our team to focus on delivering value rather than getting bogged down in early-stage evaluations.

Supercharging Data Engineering and Analysis

AI has revolutionized how we handle data at Tatango, making complex tasks more accessible and breaking down barriers between teams. Traditionally, our data engineers specialize in crafting and optimizing database queries using tools like SQL, while some application developers rely on simpler methods or pre-built tools to interact with data. AI tools like GPT bridge this gap, empowering data engineers to explore broader development tasks and enabling developers to handle more advanced data work. This has fostered collaboration across teams, helping us work more seamlessly together.

With a product roadmap heavily focused on data-driven solutions, AI has become indispensable. It allows us to create complex queries in seconds speeding up data analysis and problem-solving.

By automating these processes and enhancing individual capabilities, AI has significantly improved how we build scalable, efficient data systems. This has allowed us to deliver innovative, data-driven products to our customers more quickly and effectively than ever before.

Streamlining Meetings and Collaboration

Meeting summaries are no longer a chore thanks to tools like Circleback , which distills discussions into actionable insights and sends summaries directly to Slack. As a remote-only company, Circleback has been nothing short of transformative.

While I am not an advocate for missing standups, it does happen from time to time. Circleback bridges that gap by ensuring every team member can access the insights they need. It provides unparalleled visibility into team dynamics across all pods, empowering individuals to stay efficient and connected without missing a beat. It allows us to quickly identify and address bottlenecks, streamline workflows, and ensure every team member remains aligned and productive. This level of clarity has not only improved individual workflows but has also elevated our overall operational efficiency, making it a cornerstone of how we navigate the challenges of remote work.

Measuring Success with DORA Metrics

Success at Tatango is defined by our ability to ship frequently, reduce cycle times, and ensure every piece of code that reaches production meets the highest quality standards. These standards, known as DORA metrics, have only improved with our incorporation of AI:

• Product Delivery: A 24% increase in the second half of 2025 reflects how AI enables us to tackle more initiatives with fewer resources.
• Cycle Time: Features now move from start to production in under two business days.
• Bug Escape Ratio: A 14% improvement shows how AI helps catch issues early, ensuring higher reliability.

A Truly Integrated AI Ecosystem

Today, AI serves as a true copilot for our entire engineering organization, transforming the way we think, collaborate, and create. It’s not just about efficiency—it’s about empowering our teams to achieve more, solve harder problems, and innovate at a pace we never thought possible. Our current AI "stack" reflects this integration and stands as a testament to the incredible journey we’ve been on:

• FigJam for user research insights.
• Circleback for automated insights from customer feedback, project meetings, and standups.
• Figma AI for generating UX designs.
• Cursor, Cursor Agent, and Windsurf for coding prototypes and production level features.
• Cursor for writing unit and end-to-end (E2E) tests.
• Greptile for code reviews integrated with GitHub. This tool has become a cornerstone of our workflow, enabling us to streamline the review process while ensuring code quality. It's one of the tools we truly love for its simplicity, reliability, and ability to integrate seamlessly with our development pipeline.
• Gamma for dynamic, visually engaging presentations that resonate with both internal teams and external audiences, helping us communicate our vision and results effectively.

The Road Ahead

AI has fundamentally reshaped how we build, test, and deliver at Tatango. It has also helped us accelerate our SOC-2 and HIPAA compliance efforts, streamlining these critical processes and ensuring we meet stringent industry standards efficiently. Looking ahead, as we adopt more AI tools, we envision even greater transformations. For instance, AI-driven predictive analytics and proactive issue detection could further reduce our cycle times and minimize production risks.

In 2025, we anticipate more advancements in AI-assisted code generation, which will enable us to ship features even faster without compromising quality. Imagine a future where AI tools proactively suggest architectural improvements or automatically adapt code to handle scale. These possibilities are no longer theoretical—they are within reach and will fundamentally redefine software development.

By automating repetitive tasks, enhancing communication, and enabling faster decision-making, AI has already allowed us to focus on what matters most: delivering exceptional value to our customers. And perhaps most fittingly, AI is even helping me write this article—a meta-moment that captures just how deeply it’s embedded in our workflows.

To my fellow engineers, product managers, and leaders: AI is more than a tool—it’s a catalyst for reimagining what’s possible. It’s reshaping how we solve problems, collaborate, and deliver value. The organizations that harness its potential now will set the pace for innovation tomorrow.

Don’t wait to be left behind—embrace it and lead the change.