By Ishpreet Khanuja
Every choice a founder makes, about what to build, when to launch, where to spend, is a trade-off between time, capital and conviction. In the earliest stages of a venture’s journey, it can feel like every mistake is final and every delay costs momentum.
It is what made the promise of automation so tempting when artificial intelligence (AI) entered the conversation. If the right tools could take on the repetitive, mechanical work, founders could spend more time shaping vision, validating ideas and building relationships that move their venture forward.
But speed at the cost of innovation has never been sustainable and knowing which parts of your process can truly be automated, and which still require your judgment can make all the difference. In Altitude Accelerator’s recent workshop on ‘AI-Powered Coding, Prototyping, and MVP Development’, Codalio founder Ehsan Mirdamadi and his team explored this balance and illustrated how founders can use today’s AI tools to accelerate what they build without outsourcing how they think.
Building on Shifting Ground
Every entrepreneur knows the feeling of having a brilliant idea and realizing months later that bringing it to life requires persistence, in addition to a small army of developers, designers and capital. For the founders of Codalio, this familiar struggle became the inspiration for a product that bridges the long gap between vision and execution. As Mirdamadi and his collaborators from Key Logic explained in a recent session, the goal was to make innovation more accessible. “We realized,” he said, “that bringing ideas to life—and in our case, bringing them to market—is not that easy for a lot of us. We have great ideas, but we have to go through so many twists and turns before we can weave them into a product people actually adopt.”
This insight, drawn from hundreds of projects built in a previous consulting venture, led to Cordao’s mission to use artificial intelligence not as a novelty but as a genuine accelerator of creation. “If you don’t leverage these platforms,” Mirdamadi cautioned, “you’ll find yourself in a difficult situation. Innovation is moving so fast that in a few months, not using these platforms could make you irrelevant.”
Developing Software in the Age of AI
In the modern economy, where all products are essentially software products to varying degrees, the process of building software remains slow, complex and expensive. Despite the fact that the total cost of software development to the U.S. economy reached $2.4 trillion in 2022 (up 25 per cent in two years), nearly half of all software projects fail and Mirdamadi believes the culprit is scope creep. As he explained, “80 per cent of software projects are impacted by scope. You think this is the scope of work, and then the moment you have the first screenshot of your application, that scope starts to shift.”
Mirdamadi insists AI solutions have started to build a new infrastructure of speed, dismantling inefficiencies that were previously unavoidable. According to him, three distinct categories of AI tools are now transforming how software gets made:
AI-assisted coding tools, such as GitHub Copilot, Cursor and ChatGPT, act as copilots for developers completing code, identifying syntax errors and explaining their reasoning in conversational language.
AI-enabled prototyping tools like Figma, Uizard, Lovable and Bolt turn text prompts or hand-drawn sketches into functional user interfaces, allowing founders to visualize their ideas without waiting for a design team.
AI-driven development engines, like Codalio’s own platform, go beyond prototypes to help teams define scope, generate documentation, estimate effort and even simulate deployment environments.
Each of these tools serves a different layer of the product-development stack, but together they form a new spectrum of AI tools, a landscape in which founders can move from idea to iteration in hours rather than months.
Beyond Language Barriers
During the session, Codalio developer Yusuf demonstrated this shift in real time. Opening a blank Python file, he typed not a single line of code, only a few lines of plain English describing what he wanted to build. Instantly, GitHub Copilot suggested imports, logic and structure. When an error occurred while running the code, he didn’t debug it manually. He asked, “Tell me what the issue is in my code.” The tool responded, identified a typo and proposed a fix.
The tool also explained why it had failed in the first place. “It is guiding you to where you’re making mistakes in your code, and it will eventually help you debug it,” Mirdamadi added. “These models are getting better. You may soon find yourself in a situation where your programming language becomes simple English.”
These tools also accelerate collaboration between technical and non-technical teams. As Yusuf noted, “If I’m looking at someone else’s code and I don’t understand it, I can just ask the AI to explain it. That saves hours.”
However, perfection is still an unreasonable ask reserved for the future when it comes to AI. Mirdamadi noted, “Let me explain something. One of the reasons that in the first attempt you saw the generated code behaving oddly is because of the nature of the large language models and the way they interpret things. As the models get better and better, the generated code also gets better, but it’s unpredictable. In one of our demos, we never had any logs appear, but in this case the very first attempt came out with logging. That’s why we’ve not exactly scripted on these demos—you never know what’s going to happen. Sometimes, the same prompts behave differently each time.”
Visualizing in Real Time
The next phase of the session turned from code to design, the critical step between concept and execution. Traditionally, user-interface prototyping has always required specialized designers and endless feedback loops between founders, engineers and early users. Now, AI tools make it possible to generate polished mock-ups from a single prompt.
Yusuf demonstrated this using Uizard, a platform that translates text into interactive designs. He typed a prompt for a ‘modern bus-tracking app with real-time location tracking, route filters, ticket booking, delay alerts, and an admin dashboard.’ Within seconds, the system rendered a full mobile interface, complete with navigation, icons and data placeholders.
For a founder, this instant visualization is no small feat. It transforms the conversation from abstraction to reality, helping investors and users understand the product’s flow long before a single line of production code is written.
Figma, long the industry standard for design collaboration, has also integrated similar AI capabilities. Extensions can now convert textual descriptions into layouts and wireframes, allowing non-designers to experiment freely.
Lovable and Bolt, two newer additions to the field, go one step further still by rendering prototypes directly in a browser, enabling instant previews and iterative feedback. But, as Mirdamadi noted, while these recent tools are powerful accelerators, they focus primarily on front-end automation. They could instantly generate layouts, user flows and visual experiences, yet they fell short when you think about integrating with the deeper systems that make an application truly functional.
Codalio was built to bridge that gap, bringing structure and intelligence to the earliest stage of software creation, when the biggest decisions are still being made. To demonstrate what this looks like in practice, Mirdamadi invited a long-time collaborator and founder of Key Logic, Dean Lupini, to take the stage.
Bridging the Gap
Based in Northern Ontario, Key Logic was among Codalio’s earliest adopters. With operations across diverse industries, from mining and agriculture to medical devices and enterprise systems, Lupini’s team has used the platform to streamline scoping, documentation, and delivery.
“Reaching an MVP on time and on budget is a lot harder than it sounds on paper.” Lupini shared. “The planning is the first thing that comes up. Getting this right at the beginning is obviously important when time and money are of the essence. Knowing what you want to build and how long it’s going to take are key things you need to get right at the start.”
For most startups, those roadblocks start small as misunderstood requirements, shifting priorities and documentation that can’t keep pace with iteration cycles. Codalio, he explained, was designed to eliminate those friction points by blending human judgment with machine precision.
On screen, Lupini launched the Codalio interface, offering a live demonstration of how AI can turn loosely defined ideas into structured, documented, and executable blueprints. The system asked clarifying questions, generated specifications, and surfaced dependencies—all in real time.
He went on to frame Codalio’s value through five key areas of impact that reflect both his experience and the platform’s design philosophy: scoping, documentation, estimation, development and business-case design. Each represents a traditional bottleneck that Codalio’s AI now helps address with accuracy and speed.
CatFlock: Building in Real Time
To illustrate Cadalio’s capabilities, Lupini presented a hypothetical venture he called CatFlock, a playful ‘map-based collaboration tool’, designed to help groups coordinate activities. Using Codalio, he introduced the idea as a simple prompt. The platform generated a full business plan, market analysis and feature set, while also highlighting a logical conflict he hadn’t noticed (the app was meant to provide unbiased recommendations but also accept paid advertising).
Codalio repeatedly asked clarifying questions until Lupini re-framed the model: recommendations would remain neutral, but advertisers could brand their listings. “It built a whole business plan, first of all—which saves a ton of time,” he added. “But the content in that business plan also brought up things I didn’t think of like the accessibility guidelines, things like that you normally wouldn’t think of until it’s too late. There were also some new features, and it asked me, ‘Hey, this looks like a good option for group chat. Would you want that feature?’ That probably would have come up six weeks from now.”
It created a functional prototype, complete with business logic, UI suggestions and deployment plan. What once required weeks of planning and team coordination could now be accomplished in a single working session.
A Founder’s Evolution
Codalio’s real value lies in what happens after the first output. The tool will propose answers, but it’s still up to the founder to decide which version of the truth to trust and act upon. Saving time by automating documentation and scope is helpful, but you can’t automate vision. That part still belongs to you.
As AI becomes central to development, transparency about how decisions are made, what data is used and where bias may appear will matter more than ever. As Lupini shared, “The main thing there is when you save that time, it allows you to spend more time on the more important things that aren’t as redundant and the finer details that normally you don’t have time to really dig into.” The session was an exploration of whether automation can remove barriers without removing judgment, and in the end, revealed that the real advantage lies in clarity, not convenience.
For early-stage founders, clarity is everything. The ability to turn ideas into blueprints, blueprints into validation, and validation into execution is no longer limited by resources, but by imagination and intent. When all is said and done, the future belongs to the ones who know what to build, not just how to build it.
The full conversation goes deeper into how founders can turn ideas into action faster, smarter and with fewer trade-offs. Catch the workshop on YouTube for a deeper look at Codalio’s live demos and insights from the founders who are testing what’s next.
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