Same AI Upside, Fewer Competitors: The Robotics Angle

01 /The Concept

DBR77 describes itself as a "B2B marketplace for industrial automation" – but what they've built turns out to be considerably more interesting than that label suggests.

The entry point is hardware. A factory or plant looking to use the platform first needs to deploy sensors and measurement devices across its facility – equipment purchased from DBR77 along with the platform that connects it all.

Once that's in place, the platform generates a real-time digital twin of the production environment, reflecting what's actually happening on the floor at any given moment.

That's already useful on its own: plant managers can query the AI to get instant answers about what's happening now or what happened at any point in the past.

Beyond diagnostics, the platform lets managers run AI-powered scenario simulations – testing what would happen if they moved equipment, reconfigured a line, or changed a step in the production process. The AI helps them make decisions that improve efficiency using existing resources.

And then the marketplace enters the picture. Once the digital twin reveals where gains are possible, managers can browse for new solutions that could be added or used to replace specific components. The AI naturally surfaces relevant options – modeling both the upfront cost and the downstream savings in resources and productivity.

Because industrial automation solutions aren't off-the-shelf products, the marketplace supports an RFP flow: the plant sends its requirements to potential vendors, reviews commercial proposals, picks the best fit, and executes the contract for supply and installation – all through the same platform.

DBR77 also stays actively involved during implementation, answering questions, providing guidance, and keeping vendors accountable.

For production facilities, equipment decisions need to fit into a broader strategy, not be made in isolation. So the platform supports the creation of a comprehensive, long-term digital transformation roadmap – where each purchase is a step in an overall plan, grounded in industry trend analysis, organized into a phased timeline, and backed by a business case explaining the expected return for that specific facility.

The existence of such a plan creates a very favorable dynamic for DBR77 itself: the facility has essentially committed to a long-term purchasing pipeline through its marketplace. And alongside that, it's signed up for an ongoing subscription to the platform that keeps the digital twin current.

When the current roadmap is complete, technology will have moved on, and it's time to build a new one.

DBR77 is based in Poland and has real clients, who have on average improved their production efficiency by 20%, cut the time required to implement automation by 50%, and reduced the cost of that automation by 30%.

The company has just raised €3.5M to fund its expansion into Western Europe and the US. An earlier round of approximately $1.5M was raised in 2021 at the platform's launch.

02 /Why It Matters

Modern industrial robots are AI too – just implemented in hardware rather than purely in software like everyone's favorite ChatGPT.

So DBR77 is participating in the same broad, powerful trend: AI adoption in business operations. The difference is that the business processes being transformed here are physical production lines, where the AI takes the form of machines rather than software agents. The underlying logic is identical.

The way that AI adoption is being managed in manufacturing has also started to mirror how it's managed in software-driven operations. A [recent review](/review/1-trillion-dollarov-otdannyh-na-razgrablenie-ajtishnikam) covered this in the context of digital consulting startups, with references to earlier examples. The same principles apply here.

Drawing on the pitches of all the startups in this space, five principles are worth highlighting:

The first principle: AI adoption should be anchored to specific business outcomes, not driven by hype or fashion.

The second: don't implement AI everywhere at once. Focus on the functions, employee categories, or production steps where the return will be highest. Identifying those spots is itself a service worth charging for – as Workhelix ([related review](/review/jeto-ne-gemorroj-a-vozmozhnost-eshhjo-bolshe-zarabotat)) has demonstrated.

The third: before implementing anything, build a strategic plan. Move in short, sequential steps. Evaluate the results of each step before planning the next, and keep improving the plan accordingly. And keep being paid by whoever built that plan and helps monitor its execution.

All of the above resembles traditional consulting. But a new wave of startups has pushed further – turning consulting into a product business.

The fourth principle: the output of this new kind of consulting shouldn't be a report with recommendations. It should be a product – something that actually executes those recommendations once deployed in the client's workflow. The firm can build those products itself or source them from partners.

Workhelix, Quantum Rise ([related review](/review/na-jetom-uzhe-ne-stydno-zarabatyvat)), and Gruve all operate this way. DBR77 does the same – pairing its digital-twin platform with a marketplace where clients can immediately buy the specific solution the AI has identified.

The fifth principle, which follows naturally from the fourth, is somewhat cynical: the client now pays this new kind of consultant not once, for a deliverable, but continuously, for using the deployed products.

This fundamentally changes the economics of consulting. It creates compounding recurring revenue from past clients – a business model that looks much more like a SaaS company than a services firm.

DBR77's platform subscription – required to keep the digital twin current – is one example of this. The marketplace commission on every equipment purchase is another. And as the client's production grows more efficient and expands, so does the platform footprint and the volume of purchases.

03 /Opportunities

Manufacturing is a "dirty and dusty" sector that tech startups – comfortable behind screens – tend to ignore.

Big mistake. The industrial automation market hit $250B in 2024 and is expected to nearly double to $500B by 2032. And that estimate may prove conservative, as hardware AI is advancing rapidly too – forcing manufacturers to adopt it just to stay competitive.

But the broader opportunity isn't manufacturing-specific. It's the creation of a new type of consulting company – one that embeds AI-powered products into business processes of any kind, taking share from the old consulting model in the process.

Management consulting is already a market above $1 trillion in size. For product-focused founders who are far more comfortable building software than writing decks, the structural shift underway is a genuine opening.

And within that, manufacturing is a particularly interesting niche: a large, growing market where the relative scarcity of tech-native competitors means less pressure than in the "cleaner" sectors where most startups cluster.