DBR77 is an industrial technology company focused on helping manufacturers adopt artificial intelligence, digital twins, robotics, and automation to improve operational performance. Its technology portfolio includes a manufacturing-focused large language model, plant operating software, and a robotics marketplace designed to connect manufacturers with automation suppliers and implementation partners. The company emphasizes what it calls Agency in AI, an approach that prioritizes human decision-making, accountability, and operational transparency as industrial systems become more autonomous. Torian Richardson brings more than 25 years of experience as an operator, entrepreneur, board director, and investor, including experience at NVIDIA and in international technology ventures.
Pulse 2.0 interviewed DBR77’s Torian Richardson to learn more about industrial AI adoption, manufacturing automation, digital twin technology, robotics, and the importance of preserving human agency in increasingly automated environments.
Torian Richardson’s Background
When asked about his background, Richardson shared:
I was born in Chicago, but my life and work have been shaped by experiences across four continents and more than 100 countries. Over the past 25 years, I have worked as an operator, entrepreneur, board director, and investor, helping organizations navigate growth, complexity, and change.
That journey has included building an education technology company in China, supporting mobile-learning initiatives in Africa, and working at NVIDIA during a formative period in the company’s growth.
Across those experiences, I came to believe that technology matters most when it expands what people are able to see, decide, and do. But that belief is also deeply personal. I served as a caregiver for both of my parents, and caregiving changed the way I think about dignity, access, choice, and the systems people depend on during vulnerable moments.
Those experiences now converge in my work at DBR77, where we help manufacturers adopt AI, digital twins, robotics, and, eventually, humanoid systems in ways that improve performance while increasing people’s capacity to understand and act.
The principle at the center of that work is simple: as intelligent systems become more capable, human agency must remain a design requirement.
For me, the future of AI is not defined only by what machines are able to do. It is defined by what human beings remain empowered and accountable to do.
What Differentiates DBR77 From Its Competition
When asked how DBR77 differentiates itself from competitors in industrial innovation, Richardson explained:
DBR77 does not begin with a product. We begin with the operating reality of the plant. One manufacturer may first need reliable data collection and basic process visibility. Another may be ready for a digital twin, an industry-specific AI application, robotics, or eventually humanoid systems.
Our role is to identify the highest-value constraint and build the right sequence of capabilities around it.
Much of the market focuses on a single layer of the technology stack or expects customers to conform to a predefined solution. We take a more adaptive approach. The technology has to move at a pace the organization can absorb, govern, and sustain.
When innovation moves faster than the people, processes, and decision rights surrounding it, the result is rarely durable transformation.
That is where our view of Agency in AI becomes practical. Leaders and frontline teams need visibility into how systems work, confidence in the information they produce, and meaningful choice in how they are deployed.
We are not simply installing technology. We are helping manufacturers build the capacity to shape and ultimately own their transformation.
Challenges In Establishing DBR77’s Industry Position
When asked about the biggest challenges in defining DBR77’s position within the industrial technology market, Richardson explained:
The biggest challenge has been translating a broad technology portfolio into an immediate and credible business case for manufacturers.
Plant leaders are passionate about what they do, and they are often responsible for processes that already produce results. When new data reveals that a process may be less efficient than assumed, it can feel like a judgment on someone’s expertise, team, or identity.
At the same time, the people who influence implementation every day are not always the same people who approve the investment.
We have learned that defining our position is not simply a messaging exercise; it is also a change-management exercise. We have to engage decision-makers, operators, and trusted internal influencers early, connect the technology to a measurable operating problem, and make the path forward understandable.
The goal is not to sell transformation as an abstraction. It is to help the organization see where it is, decide where it needs to go, and move with its people, not around them.
Core Products And Features
When asked about DBR77’s core products, technology capabilities, and competitive differentiators, Richardson explained:
Our portfolio connects industrial intelligence, plant operations and physical AI. DBR77 Vector is our proprietary manufacturing-focused large language model. Consultify, powered by Vector, applies that intelligence to plant and manufacturing environments so teams can work with context that is specific to industrial operations rather than relying solely on a general-purpose model.
IRIS is our plant operating system. It is designed to bring critical plant measures into a unified view while allowing customers to connect existing systems, including enterprise resource planning, human resources and finance tools, or use capabilities within IRIS where gaps exist.
Our robotics marketplace addresses the implementation gap in physical AI. After a manufacturer identifies an opportunity for automation, the platform can help evaluate a global network of more than 14,000 robotics suppliers and connect the company with a local integrator that has relevant project experience.
The differentiator is the connective tissue: insight, operating visibility, technology selection and implementation can become part of one transformation pathway instead of disconnected projects.
Challenges Facing The Manufacturing Sector
When asked about the biggest challenges facing manufacturers and how DBR77 helps address them, Richardson explained:
U.S. manufacturing is confronting several pressures at once: decades of outsourced capacity, persistent labor constraints, fragmented data and an accelerating race to adopt AI and automation.
The challenge is not a lack of technology. It is deciding what to deploy, where it will create value, and how to integrate it without disrupting the operation that must keep producing every day.
We address that challenge by beginning with operational relevance. We examine the current flow of work, identify the constraint or opportunity, and establish a practical sequence, from data and visibility to simulation, decision support, and physical automation.
We also involve the people closest to the work. That is essential because human agency is not the opposite of automation; it is the ability of people to understand the system, exercise judgment and participate meaningfully in how the system changes.
How DBR77’s Technology Has Evolved
When asked how the company’s technology has evolved since launching, Richardson explained:
Since launch, our technology has evolved from more linear prompting toward continuous, iterative AI workflows. Our systems can increasingly help write, test, and refine software, shortening development cycles and allowing the product team to learn and adjust faster.
That does not reduce the need for engineering discipline or human oversight. It changes where we apply them, shifting more attention toward architecture, validation, and judgment.
At the same time, we have become more focused on the connection between software intelligence and the physical world. Manufacturing is not a purely digital environment. Models must operate alongside machines, materials, safety requirements, established workflows, and people.
Our evolution has therefore happened on two fronts: faster, more adaptive software development and deeper attention to how the technology performs in real operating conditions.
That combination, adaptive intelligence with physical-world accountability, is central to how we think about the future of AI in industry.
Significant Company Milestones
When asked about DBR77’s most significant milestones, Richardson highlighted the company’s U.S. market expansion, industry engagement, and digital twin projects:
One of our first significant U.S. milestones came within six weeks of entering the market, when we convened more than 70 manufacturing and technology stakeholders at our inaugural event.
We followed that with an AI Pathfinder tour across manufacturing communities in the greater Charlotte region, creating direct conversations about digital transformation and the practical barriers plants face when adopting new technology.
Over the past year, we have expanded our relationships across North Carolina and South Carolina, participated in major industry gatherings such as Automate, and introduced Consultify to a broader manufacturing audience.
We have also advanced into active digital-twin work, including a pilot with a large German enterprise and a separate implementation in real estate.
These milestones matter because they move us beyond market entry and into market learning. Each engagement gives us direct U.S. customer feedback, helps us validate where the technology creates measurable value, and allows us to refine the platform before scaling.
Customer Projects And Digital Twin Applications
When asked about customer projects demonstrating DBR77’s potential industry impact, Richardson explained:
We are not yet in a position to identify those customers publicly, and the current engagements are still too early for us to claim definitive, quantified outcomes.
What we can share is that our digital-twin work is demonstrating the value of giving organizations a common environment in which to see assets and processes, test potential changes, and make better-informed decisions before committing resources in the physical world.
That is the impact we are working toward: reducing the distance between insight and execution while preserving the customer’s ability to question assumptions, validate the information, and choose the path forward.
As the pilots mature, we expect to publish more detailed case studies, with customer permission and verified performance measures.
Total Addressable Market
When asked about the total addressable market DBR77 is pursuing, Richardson explained:
We frame the opportunity in layers rather than asking investors to accept one inflated headline number.
At the category level, published estimates place the U.S. smart-manufacturing market at approximately $75 billion to $81 billion annually, within a global market of more than $330 billion. DBR77 participates in the portion of that market centered on industrial intelligence, AI-enabled planning, digital twins, automation, and operational decision support.
Our initial serviceable market begins in the Carolinas and expands across the Southeast.
North Carolina and South Carolina together represent approximately $158 billion in annualized manufacturing GDP. That is not revenue available to DBR77, but it demonstrates the size and density of the regional manufacturing base.
The latest SBA profiles identify 2,945 manufacturers with 20 to 499 employees across the two states, before accounting for larger companies and multi-plant enterprises.
For investors, the important question is not only whether the TAM is large enough. It is whether we have a credible path into it.
Our strategy is to enter through a measurable plant-level constraint, prove economic value, expand across the facility, and then replicate the solution across additional plants, sectors, and geographies.
The broader market gives us room to build a significant company. The Carolinas give us a disciplined beachhead from which to prove repeatability, establish recurring revenue, and scale responsibly.
As our pricing and customer profile mature, we will continue to define the serviceable market from the bottom up, qualified plants multiplied by validated annual revenue per site, rather than applying an arbitrary percentage to a large market headline.
Future Company Goals
When asked about DBR77’s future goals, Richardson outlined plans to expand its industrial intelligence platform and eventually develop proprietary robotics systems:
Our near-term goal is to deepen the platform across industrial intelligence and digital twins, expand the robotics marketplace, and scale successful U.S. deployments.
Longer term, we intend to design and manufacture our own robots and humanoids for industrial applications.
The larger ambition is to help manufacturers move beyond fragmented automation projects toward an adaptive operating system that can sense, model, recommend, and act.
As those systems become more autonomous, Agency in AI becomes a practical governance discipline: Who defines the objective? Who can challenge a recommendation? Who authorizes action? And who remains accountable for the result?
We want DBR77 to help organizations answer those questions through both the technology itself and the way it is implemented.
The future of AI in industry will be shaped not only by what machines can do, but by what people remain empowered and accountable to do.
The Importance Of Human Agency In Industrial AI
When asked whether there were any additional topics he wanted to discuss, Richardson emphasized the importance of organizational readiness, accountability, and human decision-making:
The most important conversation is not about any single product. It is about whether an organization can remain relevant as the pace of change accelerates.
Leaders need to understand the system they have today, decide where intelligence and automation can improve the flow of work, and build the organizational capacity to execute.
Most organizations already agree that change is necessary. The real difficulty begins when priorities, incentives, identity, and daily operating pressure collide.
That is why I believe human agency should be treated as infrastructure, not sentiment.
Better data can reveal an opportunity, and AI can recommend a course of action, but people still need the authority, context, and skills to interpret that information, challenge it, and act well.
The future of AI should produce better decisions and broader participation, not a world in which responsibility becomes harder to locate.
The organizations that lead the next era of industrial intelligence and physical AI will not simply automate more. They will know where judgment lives, where authority sits, and who remains accountable when the system acts.
