In this exclusive interview conducted by Eqvista, Sean King, Co-Founder of Dragonfly (under Dragonfly Technology), shares insights into the explosive growth of the software landscape and the frustrations it creates for fast-scaling startups.
Dragonfly emerges as an AI-powered intelligence layer that maps real business workflows to deliver personalized, vendor-neutral tool recommendations, addressing bloated tech stacks and poor adoption head-on. From its “digital fingerprint” personalization to 2026 AI-SaaS trends and a recent £2.6M pre-seed raise, King’s vision positions Dragonfly as a game-changer for founders navigating overwhelming choices.
Sean, what inspired the launch of Dragonfly, and how do you see its mission shaping the AI-powered software discovery space?
Both my cofounder, Sven Sabas, and I have spent most of the last decade inside fast-growing startups. We’ve been on the front line of scaling teams, building products, and trying to make good technology decisions under pressure. Over that time, the software landscape has completely exploded. What used to be a handful of sensible options is now thousands of tools across every function, all promising to be “the one”.
The problem is that choosing software has quietly become a full-time job. You’re expected to understand hundreds of vendors, overlapping features, changing pricing models, and navigating the latest hype, all while still trying to do your full-time job.
We’ve both lived this frustration firsthand across multiple companies, and it led to the same outcome: bloated tool stacks, poor adoption, and teams spending more time stitching tools together than actually delivering value.
Dragonfly was born out of that pain. Our mission is to become the intelligence layer between businesses and the software they need to thrive. Instead of asking founders and operators to wade through endless comparison sites and sales decks, we flip the model on its head. We start with how a business actually works, the real flow of work inside their teams, and then recommend the right tools to support that, not the other way around.
What fundamental problems in tech adoption does Dragonfly solve for today’s fast-evolving businesses?
Fast-growing companies don’t struggle because they lack ambition; they struggle because their operating model can’t keep up with their pace of change.
Teams add tools to solve local problems, but over time, that creates a tangled stack that no one really understands end-to-end. Processes drift, ownership blurs, data lives in silos, and suddenly, simple changes feel risky and expensive.
Dragonfly tackles that root issue. We give businesses a way to see how work actually flows across their organisation, not how they think it flows. By mapping those real-world processes and measuring how they’re materialised in software today, we expose the gaps that quietly slow companies down, the hidden dependencies, manual hand-offs, and brittle automations that stop new technology from ever reaching its potential.
Can you share how Dragonfly’s AI-driven “digital fingerprint” system works to personalize software recommendations?
No two businesses are the same, yet most software recommendations treat them as if they were.
At Dragonfly, we build what we call a digital fingerprint, a living profile that captures what genuinely makes a business unique. That includes where they operate, the market they serve, the tools they already use, the skills of their team, and, just as importantly, what they’re trying to become.
Instead of asking companies to fill in endless forms, we automate this discovery so companies can answer the right questions quickly and without friction. That fingerprint then becomes the foundation for every recommendation we make.
The result is that businesses move from a place of overwhelming and endless choice to clear, confident decisions about what fits them now, and what will serve them next.
How does Dragonfly ensure unbiased, vendor-neutral suggestions for its users?
Trust is everything when you’re making decisions that shape how a business operates.
We’ve built what we believe is one of the most comprehensive datasets in the world, covering software vendors, their products, and the open-source tools and libraries that underpin them. But just as importantly, we’ve built it from a neutral position.
Vendors don’t pay to appear in our recommendations, and they can’t buy their way to the top of a list. Every suggestion Dragonfly makes is driven by what will genuinely benefit a business, not by who has the biggest marketing budget.
Can you share a case study or example where Dragonfly dramatically accelerated a company’s tech stack decision-making or helped avoid costly mistakes?
We’re working with several design partners, more to be shared soon.
What feedback have you received from early adopters regarding the platform’s value?
Since launching in October, just over 400 businesses have already used Dragonfly to explore and shape their software decisions. One of the clearest pieces of feedback we received early on was that people don’t want long reports or walls of text; they want to see how their business fits together.
That insight led us to launch the second version of our product, Architect, at our event at Google’s London HQ in November. Architect gives teams a visual canvas to map their systems and decisions. Because nobody thinks about software as a collection of isolated tools, they think about it as a connected system.
What emerging trends are you seeing in AI-powered SaaS adoption and tech stack innovation heading into 2026?
The pace of change right now is extraordinary. Everyone is talking about copilots, agents, and autonomous workflows, but when we sit down with real businesses, the reality is far more cautious.
Most companies have dipped a toe in, maybe they’ve rolled out a copilot or automated a few isolated tasks. But very few have introduced genuinely autonomous, AI-driven workflows across their core operations. As we head into 2026, that gap is only going to widen.
We’re about to see a clear divide emerge between the companies that have re-engineered how work flows through their business and those that are still layering AI on top of operating models that were never built to scale.
Congratulations for the recent £2.6M million raised in pre-seed round in October 2025, with this funding secured, what milestones or KPIs are you prioritizing in your next stage of growth?
2025 was about laying the foundations, building the right team, shaping the product, and working closely with our early design partners to make sure we’re solving real problems.
2026 is about momentum. We’re doubling down on product development, expanding the intelligence behind our recommendations, and getting Dragonfly into the hands of a much broader set of businesses. We’ve got a major launch planned for Q1, so this next phase is really about turning everything we’ve learned so far into something that delivers true value to our customers.
How does Dragonfly plan to stay ahead of industry shifts, such as generative AI and integration ecosystems?
Our ambition is to drive the adoption of AI across the industry by removing the sense of overwhelm that so many teams feel today.
We don’t see Dragonfly as just a product; we want it to become a place of education, clarity, and direction for founders and operators alike as they navigate constant change. At the core of the platform, we continuously monitor over 250,000 products and tools, watching for the shifts that really matter, whether that’s a new generative capability or a change in how ecosystems are integrating.
That constant awareness is what allows us to guide our users through each new wave rather than leaving them to figure it out on their own.
What advice would you give to founders building AI-driven B2B platforms in today’s competitive landscape?
Things are moving faster than they ever have before, and that pace isn’t slowing down.
My biggest advice to anyone building in this space is to be ruthless about who you surround yourself with. You need people who challenge your assumptions, who stay curious about what’s coming next, and who are prepared to evolve their thinking as quickly as the technology itself.
