Developing a Data Strategy and AI Roadmap for an Early-Stage Startup

The Situation:

A pre-seed travel tech startup had a strong product vision: a mobile app that connected users with relevant, nearby attractions using intelligent recommendations.

The founding team had design clarity, early wireframes, and a realistic plan to ship an MVP. What they didn’t have was data experience—or a clear understanding of what was realistically achievable with AI at launch.

At the same time, the company was preparing for a seed raise and needed to communicate a credible technical roadmap to investors.

The Challenge:

After working closely with the founder and engineer, it became clear that:

• The long-term vision was a genuinely strong use case for AI

• The MVP timeline was reasonable for core product features

• Several planned AI-driven features were impossible to support at launch due to a lack of data

In short, the company wasn’t “behind”—but they were at risk of building the wrong things too early and misrepresenting their technical maturity to investors.

The Approach:

I worked directly with the founder to understand the business at first principles: value drivers, growth mechanics, and where intelligence would actually create leverage over time.

In parallel, I collaborated with the engineer to assess the current stack, planned architecture, and delivery constraints.

From this, I developed a North Star data and product framework—a single reference point tying:

• Business objectives

• Product roadmap

• Metrics and data dependencies

This made it immediately clear which AI capabilities required data the company simply wouldn’t have at launch—and which simpler approaches would deliver value immediately.

Rather than pushing premature machine learning, I laid out a staged AI roadmap:

• Start with deterministic logic and heuristics

• Introduce lightweight statistical models as data accumulates

• Progress toward more sophisticated recommendation systems only once the business could support them

This grounded the AI vision in reality while preserving long-term ambition.

Results:

The outcome was not “more AI,” but better decisions.

• The company avoided building fragile or misleading AI demos

• The founder gained a clear, defensible technical narrative

• The data and AI roadmap aligned cleanly with product milestones

• Investors saw a team that understood constraints as well as ambition

As a direct result, the startup entered fundraising with confidence and credibility, and successfully raised a $4M seed round.

Why this mattered:

Many early-stage teams damage trust—internally or with investors—by overstating what AI can do before the foundations exist.

This engagement worked because the goal wasn’t to impress. It was to build a path from where the company actually was to where it wanted to go, without burning time, money, or credibility along the way.

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If you’re planning AI features but unsure whether the foundations are there yet, that’s usually the right moment to slow down—and get it right.

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