What The World's Best Startups Are Building Tomorrow

Primarily AI and ML startups between seed and Series B. The sweet spot is technical founders who've built something with real traction but are losing deals to competitors with better positioning, or who are heading into a fundraise and need their story tightened. We've also worked with growth-stage companies repositioning after a pivot or entering a new market. Our pattern library is deepest in AI/ML, but the positioning and GTM frameworks apply broadly to any developer-focused or technical product.

Crunchbase and PitchBook track funding rounds and headcount. We track what companies are actually building. Our intelligence reports reverse-engineer product roadmaps from public signals and synthesize that into analysis written for people making investment decisions. The difference is the "so what": we don't just tell you a company raised a Series B, we tell you what that money is being spent on and what it means competitively. Browse the directory to see examples.

It depends on the need. A one-off company profile takes about a week and delivers a written report covering product roadmap, competitive positioning, go-to-market strategy, team assessment, and funding context. A competitive landscape mapping for an investment thesis takes two to four weeks and covers multiple companies across a sector. Ongoing signal monitoring is a retainer where we track a set of companies or a market segment and flag material changes as they happen. All deliverables are written for a non-technical reader with enough depth that a technical reader respects them.

We scope the event around your team's actual challenges and data, not generic exercises. A typical hackathon runs one to two days: your team works alongside ML engineers from our network to build working prototypes that address real problems on your roadmap. Bootcamps are longer (usually a multi-week series) and focus on building internal AI capability, taking a team from "we know AI matters" to "we shipped something and learned how to do it again." We also help turn hackathon outputs into production roadmap items so the work doesn't die after the event.

We've built a vetted community of ML engineers, researchers, and technical builders across Canada that we've built over several years through events, meetups, and direct relationships. We tap this network for hackathon mentorship, project-based work, and hiring referrals. Every engineer in the network has been personally vetted or referred by someone we trust. This isn't a freelancer marketplace; it's a curated community with real relationships.

That's common, and it's fine. The three services often overlap in practice. A founder might start with a product marketing engagement and realize they need competitive intelligence to inform their positioning. An investor might commission intelligence on a portfolio company and then connect that company with us for GTM help. Start a conversation and describe the problem,  we'll tell you which service fits, or if it's a combination.

Product marketing and intelligence engagements are scoped and priced per project, typically ranging from a single company profile to a multi-month retainer. Hackathons and bootcamps are priced per event based on team size, duration, and the complexity of the technical challenge. We're happy to discuss budget constraints upfront -- we'd rather scope something useful within your budget than pitch something you can't afford.

Our intelligence and product building services are specifically focused on AI/ML, that's where our research depth, network, and pattern recognition are strongest. Product marketing is more flexible: the positioning and GTM frameworks apply to any technical product, though we'll be most useful if your product touches AI, data infrastructure, or developer tools in some way.