Thirteen named founders. One consistent pattern. Here is what the data looks like across our customer base, and what the founder side of the experience actually involves.
When founders ask Hal9 whether thirty days is realistic for their specific AI product, we point them to thirteen named founders who have already shipped on this timeline. The pattern is consistent enough that we can describe it before the prospect tells us their idea.
Here is what the data looks like across our customer base, and what the founder side of the experience actually involves.
Hal9's ideal customer is a non-technical founder building a B2B SaaS product where AI is core to the value proposition, not an add-on. Many of them are first-time founders. Several are experienced operators starting their second or third company. None of them have an in-house AI engineering team at the start of the engagement, and few of them have one even by the time the product ships.
Three things show up consistently in how these projects move from idea to launch.
The biggest predictor of whether a thirty-day delivery is achievable is whether the founder is willing to narrow scope to the smallest version of the product that actually proves the thesis. Founders who insist on shipping every feature they have imagined typically take longer. Founders who agree to ship the one feature that demonstrates AI-driven value typically beat the timeline.
This is why our autonomous AI platform leads with idea refinement. When a founder describes their concept on the Hal9 platform, our system narrows the scope to focus on the core experience, breaks the product into stages, proposes the technology stack, and generates the frontend before any human expert touches the build. The platform is opinionated about scope because scope discipline is what makes thirty days work.
Our autonomous AI handles infrastructure, scaffolding, deployment, and the repetitive parts of integration. Our AI experts — team members with backgrounds at Microsoft, Microsoft Research, RStudio, and similar — make the judgment calls: model selection, prompt architecture, and the specific business logic that makes the AI feel like it understands the customer's context.
Every product runs in its own isolated Kubernetes pod, which keeps the founder's data and IP fully separate. This is what "AI building AI" means in practice: the autonomous platform does the work that does not require human judgment, and humans focus on the decisions that compound.
Full IP ownership of the product. Full ownership of the data. Predictable pricing at $2,000 per month for the Startup Plan, with the ability to cancel any month directly from the website. We refund the most recent payment if the founder is not satisfied.
This is unusual for AI development engagements, which often involve some form of platform lock-in or shared IP arrangement.
Honesty matters here. Thirty days is a real timeline for the customer profile we serve, but it is not universal. Projects that involve novel ML research, custom model training on proprietary data at scale, or compliance-heavy enterprise integrations often take longer. We tell founders this in the first conversation.
The thirty-day promise is for AI products that compose existing foundation models — OpenAI, Anthropic, and other major LLM providers — into a focused, valuable workflow. For the customer profile we serve, this is the vast majority of AI products being built right now.
The most common reflection from founders after they ship is that they spent more time than they should have evaluating other paths before they tried ours. Several customers tell us they spent weeks interviewing freelance engineers, evaluating agencies, or trying to assemble a no-code prototype before realizing that none of those paths matched their actual constraints.
Apurva Luty at Optimly put it directly: she made six months of progress in six weeks with our team. The implicit cost in her statement is the calendar time founders lose evaluating paths that were never going to work for their specific situation.
Antonio Anguiano at Greenny was demoing the product to customers within two weeks of starting with us. Rodrigo Carriedo at Propio describes how Hal9 enabled his team to launch advanced AI insights in weeks with seamless integration.
We publish the full case studies for these engagements on hal9.com so that founders evaluating Hal9 can see specifically what was built, in what timeframe, for what use case.
If you are a non-technical founder building an AI-powered B2B SaaS product, the engagement begins with a conversation. Let's talk about your idea.