In preparation for our upcoming Building SaaS Businesses with AI Superstream, I sat down with event chair Jason Gilmore to discuss the full lifecycle of an AI-powered SaaS product, from initial ideation all the way to a successful launch.
Jason Gilmore is CTO of Adalo, a popular no-code mobile app builder. A technologist and software product leader with over 25 years of industry experience, Jason’s spent 13 years building SaaS products at companies including Gatherit.co and the highly successful Nomorobo and as the CEO of the coding education platform Treehouse. He’s also a veteran of Xenon Partners, where he leads technical M&A due diligence, advises their portfolio of SaaS companies on AI adoption, and previously served as CTO of DreamFactory.
Here’s our interview, edited for clarity and length.
Ideation
Michelle Smith: As a SaaS developer, what are the first steps you take when beginning the ideation process for a new product?
Jason Gilmore: I always start by finding a name that I love, buying the domain, and then creating a logo. Once I’ve done this, I feel like the idea is becoming real. This used to be a torturous process, but thanks to AI, my process is now quite smooth. I generate product names by asking ChatGPT for 10 candidates, refining them until I have three preferred options, and then checking availability via Lean Domain Search. I usually use ChatGPT to help with logos, but interestingly, while I was using Cursor, the popular AI-powered coding editor, it automatically created a logo for ContributorIQ as it set up the landing page. I hadn’t even asked for one, but it looked great, so I went with it!
Once I nail down a name and logo, I’ll return to ChatGPT yet again and use it like a rubber duck. Of course, I’m not doing any coding or debugging at this point; instead, I’m just using ChatGPT as a sounding board, asking it to expand upon my idea, poke holes in it, and so forth.
Next, I’ll create a GitHub repository and start adding issues (basically feature requests). I’ve used the GitHub kanban board in the past and have also been a heavy Trello user at various times. However, these days I keep it simple and create GitHub issues until I feel I have enough to constitute an MVP. Then I’ll use the GitHub MCP server in conjunction with Claude Code or Cursor to pull and implement these issues.
Before committing resources to development, how do you approach initial validation to ensure the market opportunity exists for a new SaaS product?
The answer to this question is simple. I don’t. If the problem is sufficiently annoying that I eventually can’t resist building something to solve it, then that’s enough for me. That said, once I have an MVP, I’ll start telling everybody I know about it and really try to lower the barrier associated with getting started.
For instance, if someone expresses interest in using SecurityBot, I’ll proactively volunteer to help them validate their site via DNS. If someone wants to give ContributorIQ a try, I’ll ask to meet with the person running due diligence to ensure they can successfully connect to their GitHub organization. It’s in these early stages of customer acquisition that you can determine what users truly want rather than merely trying to replicate what competitors are doing.
Execution, Tools, and Code
When deciding to build a new SaaS product, what’s the most critical strategic question you seek to answer before writing any code?
Personally, the question I ask myself is whether I seriously believe I will use the product every day. If the answer is an adamant yes, then I proceed. If it’s anything but a “heck yes,” then I’ve learned that it’s best to sit on the idea for a few more weeks before investing any additional time.
Which tools do you recommend, and why?
I regularly use a number of different tools for building software, including Cursor and Claude Code for AI-assisted coding and development, Laravel Forge for deployment, Cloudflare and SecurityBot for security, and Google Analytics and Search Console for analytics. Check out my comprehensive list at the end of this article for more details.
How do you accurately measure the success and adoption of your product? What key metrics (KPIs) do you prioritize tracking immediately after launch?
Something I’ve learned the hard way is that being in such a hurry to launch a product means that you neglect to add an appropriate level of monitoring. I’m not necessarily referring to monitoring in the sense of Sentry or Datadog; rather I’m referring to simply knowing when somebody starts a trial.
At a minimum, you should add a restricted admin dashboard to your SaaS which displays various KPIs such as who started a trial and when. You should also be able to quickly determine when trialers reach a key milestone. For instance, at SecurityBot, that key milestone is connecting their Slack, because once that happens, trialers will periodically receive useful notifications right in the very place where they spend a large part of their day.
On build versus buy: What’s your critical decision framework for choosing to use prebuilt frameworks and third-party platforms?
I think it’s a tremendous mistake to try to reinvent the wheel. Frameworks and libraries such as Ruby on Rails, Laravel, Django, and others are what’s known as “batteries included,” meaning they provide everything 99% of what developers require to build a tremendously useful, scalable, and maintainable software product. If your intention is to build a successful SaaS product, then you should focus exclusively on building a quality product and acquiring customers, period. Anything else is just playing with computers. And there’s nothing wrong with playing with computers! It’s my favorite thing to do in the world. But it’s not the same thing as building a software business.
Quality and Security
What unique security and quality assurance (QA) protocols does an intelligent SaaS product require that a standard, non-AI application doesn’t?
The two most important are prompt management and output monitoring. To minimize response drift (the LLM’s tendency for creative, inconsistent interpretation), you should rigorously test and tightly define the LLM prompt. This must be repeatedly tested against diverse datasets to ensure consistent and desired behavior.
Developers should look beyond general OpenAI APIs and consider specialized custom models (like the 2.2 million available on Hugging Face) that are better suited for specific tasks.
To ensure quality and prevent harm, you’ll also need to proactively monitor and review the LLM’s output (particularly when it’s low-confidence or potentially sensitive) and continuously refine and tune the prompt. Keeping a human in the loop (HITL) is essential: At Nomorobo, for instance, we manually reviewed low-confidence robocall categorizations to improve the model. At Adalo, we’ve reviewed thousands of app-building prompt responses to ensure desired outcomes.
Critically, businesses must transparently communicate to users exactly how their data and intellectual property are being used, particularly before passing it to a third-party LLM service.
It’s also important to differentiate when AI is truly necessary. Sometimes, AI can be used most effectively to enhance non-AI tools—for instance, using an LLM to generate complex, difficult-to-write scripts or reviewing schemas for database optimization—rather than trying to solve the core problem with a large, general model.
Marketing, Launch, and Business Success
What are your top two strategies for launching a product?
For early-stage growth, founders should focus intently on two core strategies: prioritizing SEO and proactively promoting the product.
I recommend prioritizing SEO early and aggressively. Currently, the majority of organic traffic still comes from traditional search results, not AI-generated answers (GEO). We are however certainly seeing GEO being attributed to a larger share of visitors. So while you should focus on Google organic traffic, I also suggest spending time tuning your marketing pages for AI crawlers.
Implement a feature-to-landing page workflow: For SecurityBot, nearly all traffic was driven by creating a dedicated SEO-friendly landing page for every new feature. AI tools like Cursor can automate the creation of these pages, including generating necessary assets like screenshots and promotional tweets. Landing pages for features like Broken Link Checker and PageSpeed Insights were 100% created by Cursor and Sonnet 4.5.
Many technical founders hesitate to promote their work, but visibility is crucial. Overcome founder shyness: Be vocal about your product and get it out there. Share your product immediately with friends, colleagues, and former customers to start gaining early traction and feedback.
Mastering these two strategies is more than enough to keep your team busy and effectively drive initial growth.
On scaling: What’s the single biggest operational hurdle when trying to scale your business from a handful of users to a large, paying user base?
I’ve had the opportunity to see business scaling hurdles firsthand, not only at Xenon but also during the M&A process, as well as within my own projects. The biggest operational hurdle, by far, is maintaining focus on customer acquisition. It is so tempting to build “just one more feature” instead of creating another video or writing a blog post.
Conversely, for those companies that do reach a measure of product-market fit, my observation is they tend to focus far too much on customer acquisition at the cost of customer retention. There’s a concept in subscription-based businesses known as “max MRR,” which identifies the point at which your business will simply stop growing once revenue lost due to customer churn reaches an absolute dollar point that erases any revenue gains made through customer acquisition. In short, at a certain point, you need to focus on both, and that’s difficult to do.
We’ll end with monetization. What’s the most successful and reliable monetization strategy you’ve seen for a new AI-powered SaaS feature? Is it usage-based, feature-gated, or a premium tier?
We’re certainly seeing usage-based monetization models take off these days, and I think for certain types of businesses, that makes a lot of sense. However, my advice to those trying to build a new SaaS business is to keep your subscription model as simple and understandable as possible in order to maximize customer acquisition opportunities.
Thanks, Jason.
| For more from Jason Gilmore on developing successful SaaS products, join us on February 10 for our AI Superstream: Building SaaS Businesses with AI. Jason and a lineup of AI specialists from Dynatrace, Sendspark, DBGorilla, Changebot, and more will examine every phase of building with AI, from initial ideation and hands-on coding to launch, security, and marketing—and share case studies and hard-won insights from production. Register here; it’s free and open to all. |
Appendix: Recommended Tools
| Category | Tool/service | Primary use | Notes |
| AI-assisted coding | Cursor (with Opus 4.5) and Claude Code | Coding and AI assistance | Claude Opus 4.5 highly valued |
| Code management | GitHub | Managing code repositories | Standard code management |
| Deployment | Laravel Forge | Deploying projects to Digital Ocean | Highly valued for simplifying deployment |
| API/SaaS interaction | MCP servers | Interacting with GitHub, Stripe, Chrome devtools, and Trello | Centralized interaction point |
| Architecture | Mermaid | Creating architectural diagrams | Used for visualization |
| Research | ChatGPT | Rubber duck debugging and general AI assistance | Dedicated tool for problem-solving |
| Security | Cloudflare | Security services and blocking bad actors | Primarily focused on protection |
| Marketing and SEO | Google Search Console | Tracking marketing page performance | Focuses on search visibility |
| Analytics | Google Analytics 4 (GA4) | Site metrics and reporting | Considered a “horrible” but necessary tool due to lack of better alternatives |