Last Updated: 17 September 2026
Robots can already move items, deliver orders, and carry out other routine tasks. The harder part is dealing with everything that happens around those tasks. A hallway gets crowded. A staff member gives an unclear request. Conditions change, and the robot still has to understand what is happening and respond correctly.
That is the gap Ried Floco is trying to address at TechForce Robotics. Floco, the company’s President and Director, brings more than 30 years of hospitality leadership and 15 years of software and technology development experience. After overseeing more than 200 hotel properties representing over $3 billion in assets, he has spent much of his career working around the operational problems that automation is now trying to solve.
TechForce develops autonomous robotics systems for hospitality, healthcare, pharmaceutical, commercial, and other environments. Its robots are designed to handle repetitive workflows such as transport, room service delivery, food and beverage service, concierge support, and lab or clean room tasks. The company uses AI to help those systems interpret requests, make context-aware decisions, and adapt when operating conditions change.
At the center of the platform is TechForce’s Robotic Connective Network™, which connects robots, AI models, software, and facility infrastructure so autonomous systems can exchange information and coordinate workflows. In this interview, Floco explains how that system works in practice, where AI adds value beyond pre-programmed automation, and how TechForce thinks about autonomy, reliability, and the role of people in increasingly automated operations.
TechForce Robotics is a U.S.-based Robotics-as-a-Service (RaaS) company that develops and deploys autonomous service robots for commercial and institutional environments. Its robots automate repetitive tasks such as waste transport, linen and material movement, internal deliveries, inventory distribution and beverage service. The company’s proprietary Robotic Connective Network integrates robotics, software, operational data and AI, including LLMs and vision models.
1. For readers discovering TechForce Robotics for the first time, what does the company do, and which operational tasks are its robots designed to handle?
TechForce Robotics develops, deploys and supports plug-and-play autonomous robotics that help organizations across hospitality, laboratory, and commercial environments automate repetitive operational tasks. We believe robotics and AI should support people, not replace them. By automating the movement of linens, trash, luggage, food, beverages, room service, laboratory supplies, and other operational materials, our robots allow employees to spend more time focused on guests and the work that matters most.
What makes TechForce different is that our robots are connected, not standalone. Through our proprietary Robotic Connective Network™, robots communicate with one another while connecting operational software, data, and AI into one connected robotics ecosystem. Combined with integrations to building technologies such as elevators and automatic doors, our robots complete real operational workflows autonomously. We support every deployment through our Robotics-as-a-Service Provider (RaaSP) model, which includes facility mapping, software updates, maintenance, training, and ongoing U.S.-based support without significant facility modifications or large upfront capital investment.
2. TechForce works across hospitality, healthcare, pharmaceutical, commercial, and other industries. What problems do these very different environments have in common when it comes to repetitive operational work?
At first glance, a hotel, a laboratory, and a commercial facility seem like completely different environments. But when you look at the day-to-day operations, they all face many of the same challenges. Employees spend a surprising amount of time moving materials from one place to another, whether that's linens, luggage, food, laboratory supplies, inventory, or waste. Those tasks are essential, but they're repetitive, time-consuming, and pull employees away from the work they're really there to do.
That's where robotics and AI can make a real impact. We focus on the work that happens every day and look for opportunities to automate it, allowing people to stay focused on guests, research, customer service, or the work that matters most.
3. Your background combines more than 30 years in hospitality with 15 years in software and technology development. How has that experience influenced the types of operational problems TechForce chooses to address with robotics?
My hospitality background taught me that every minute matters. I spent more than 30 years watching teams work incredibly hard, but I also saw how much of their day was spent on repetitive tasks that didn't directly improve the guest experience. My experience in software and technology taught me that automation only matters if it solves real operational problems and fits into the way organizations already operate.That's really how TechForce started. We don't start by asking, "Where can we put a robot?" We start by asking where employees are spending time on repetitive work that technology can reliably take over. If we can automate those tasks while helping people do their jobs better, we've created something that delivers real value for both employees and the organization.
4. TechForce's approach is built around automating repetitive workflows while supporting the workforce. Where do you see robots providing the most value to employees in day to day operations?
I think the biggest value comes from the tasks people don't always think about because they've simply become part of the day. Moving linens, supplies, food, laboratory materials, luggage, or waste from one location to another doesn't seem like much until you start adding up how many trips employees make over the course of a shift. That's a lot of time spent walking instead of doing the job they were hired to do.
That's where robotics fits in. Our robots handle those repetitive transportation workflows consistently throughout the day, allowing employees to stay focused on guests, operations, or research instead of constantly moving materials around the facility. Current robotics that assist staff and businesses should support people by taking on repetitive operational tasks, not replace the workforce. We see robotics as another tool that helps people do their jobs more efficiently.
5. A traditional automated machine can follow a predefined set of instructions. What changes when AI becomes part of that system? What can a TechForce robot understand or do that would be difficult to achieve through pre programmed commands alone?
A traditional automated machine is built to follow a specific set of instructions. If everything stays the same, it works very well. The real world doesn't work that way. People are moving, priorities change, workflows change, and facilities are constantly adapting throughout the day. That's where AI becomes valuable. It gives robotics the ability to adapt to changing conditions and continue completing operational tasks instead of relying only on a fixed set of programmed instructions.
At TechForce, AI works alongside autonomous robotics, operational software, and our Robotic Connective Network™ to help robotics adapt to changing conditions. That's what allows robotics to operate more effectively in real-world environments where no two situations are exactly alike.
6. Agentic AI is becoming an important part of the conversation around autonomous systems, but the term can mean different things. What does Agentic AI mean specifically within TechForce's robotics platform?
At TechForce, Agentic AI in autonomous robotics for human workforce support is about helping robots operate more effectively in real-world environments. A robot shouldn't simply complete one task and wait for the next instruction. As conditions change throughout the day, it should be able to recognize what's happening, interpret new information, adapt, and continue supporting the operation while helping people do their jobs more efficiently.
Agentic AI is one part of our broader Robotic Connective Network™, which connects autonomous robotics, operational software, data, and AI technologies into one connected platform. Whether it's Large Language Models, Vision Language Models, Audio Language Models, or other AI technologies, they're all working together to help robots communicate, coordinate workflows, and respond more effectively as conditions change. That's where connected robotics becomes most valuable.
7. TechForce's platform is designed to let its robots perceive their surroundings, interpret requests, and make context-aware decisions. What kinds of decisions can the AI make autonomously today?
One of the biggest differences AI brings is that a robot doesn't just complete a task and stop. As robots move throughout a facility, they're constantly observing what's happening around them. If a robot identifies debris, a spill, or another operational issue that needs attention, it can communicate that information through our Robotic Connective Network™ so another robot or connected system can respond.
That's where connected robotics becomes really powerful. Instead of every robot working independently, they're able to share information and help coordinate what happens next across the operation. Instead of simply completing one task, the robot can recognize when another task needs attention and help trigger the appropriate response.
8. Can you give a real example of this process from beginning to end? What does the robot perceive, how does the AI interpret the situation, and what physical action follows?
A good example would be one of our robots delivering supplies through a facility. Along its route, it identifies debris on the floor. Instead of simply ignoring it and continuing its delivery, the AI recognizes that something needs attention. Using vision language models and other AI technologies within our Robotic Connective Network™, it communicates that information so the appropriate cleaning robot can be dispatched while the delivery robot continues its route.
That's where AI and connected robotics really begin working together. Instead of every robot operating independently, they're able to recognize when something else needs to be done, communicate with each other, and coordinate the next workflow. That's a very different approach than simply programming one robot to do one task.
9. At the center of TechForce's technology is the Robotic Connective Network™, which connects robots, AI models, software, and facility infrastructure. How do those pieces work together during an actual task?
The Robotic Connective Network™ is really what brings everything together. During any task, robotics, AI, and software each have a specific role. Autonomous robotics performs the physical work. AI helps robotics understand what's happening around it, recognize changing conditions, and interpret information from its environment. The software manages the workflow, priorities, and operational information needed to complete the task. The Robotic Connective Network™ connects all of those technologies so they're continuously working from the same information instead of operating independently.
That's important because real operations are constantly changing. Priorities shift, people move throughout a facility, and new situations come up every day. By connecting robotics, AI, software, and operational information, organizations can support complete operational workflows instead of simply automating individual tasks. That's where the real value is.
10. What types of AI models are currently used within TechForce's platform, and what role does each type play?
Our platform uses several different types of AI models because each one serves a different purpose. Large Language Models (LLMs) support natural language understanding and communication. Audio Language Models (ALMs) enable voice interactions between people and the platform. Vision Language Models (VLMs) help robotics recognize and interpret what's happening around them by combining visual and language understanding. Multimodal Large Language Models (MLLMs) bring together information from vision, language, audio, sensors, and other data sources, giving the platform a more complete understanding of the operational environment.
Those AI models work alongside autonomous robotics, software, hardware, sensors, and computing technologies through our proprietary Robotic Connective Network™. As we continue integrating Agentic AI capabilities, the platform becomes even better at coordinating activities, sharing information, and supporting real-time decision making. Every technology has a specific role, but the real value comes from bringing them together into one connected platform that helps organizations solve real business challenges.
11. For a robot to make a useful decision, it first needs to understand what is happening around it. What kinds of information can TechForce's AI use to interpret its operating environment?
Our AI brings together information from cameras, sensors, vision systems, voice interactions, operational software, facility systems, and other connected technologies to understand what's happening around the robot in real time. Depending on the application, it can recognize people, equipment, obstacles, objects, and changing conditions that may affect the task at hand. That situational awareness allows the robot to adapt as conditions change and continue supporting the operation safely and efficiently.
12. TechForce's robots are also designed to interpret requests and interact naturally with people. What does that interaction look like today, and how does the system turn a person's request into something the robot can actually do?
A good example is Kebb-i, our AI concierge robot. A guest can ask for directions to a meeting room, where to find a restaurant, or request information about the property using natural language, even in different languages. Large Language Models (LLMs) interpret what the person is asking, identify the intent, and determine the appropriate response or action. Depending on the request, that could mean answering a question, providing directions, navigating to a location, or initiating another operational workflow.
13. The Robotic Connective Network™ allows autonomous systems to exchange information and coordinate workflows. What information are those systems exchanging, and how does AI turn that information into coordinated action?
The information being exchanged depends on what's happening throughout the facility. That could include where a robot is, what task it's performing, what it's observing, whether something unexpected needs attention, or whether another workflow should be supported. AI helps interpret that information and coordinate the next step.
The Robotic Connective Network™ keeps that information moving across the platform so every technology is working from the same operational picture. As conditions change, the platform helps coordinate workflows while each robot continues doing the job it was designed to do. That's where connected robotics delivers its greatest value.
14. What happens when several robots need to handle different tasks at the same time? How does the system organize those workflows and determine what should happen next?
In a real operation, there are always multiple things happening at once. One robot may be delivering room service, another transporting supplies, while another is handling waste or supporting a different workflow. The platform keeps track of what's happening across the operation, including task status, robot availability, location, priorities, and changing conditions, so each robot can continue performing the job it was assigned to do.
As new tasks come up, robotics and AI work together to interpret the operational information being shared across the Robotic Connective Network™ and determine what needs attention next. This enables multi-robot coordination and orchestration across the operation, while allowing every robot to stay focused on its assigned task. Technology should make operations easier to manage, not more complicated.
15. TechForce operates across environments with very different requirements, from hotels to healthcare, pharmaceutical facilities, laboratories, and clean rooms. How does the role of AI change depending on where the robot is working and what it needs to accomplish?
AI deployment in hospitality, pharmaceutical, and commercial operations is driven by the environment and the work it's supporting. In a hotel, AI may help a robot interact with guests, navigate busy hallways, recognize obstacles, or support room service and housekeeping operations. In a laboratory or pharmaceutical facility, the priorities are very different. AI is supporting controlled workflows, material transport, standard operating procedures, documentation, and operating within highly regulated environments where consistency and accuracy are critical.
The AI is supporting different operational goals depending on where it's deployed. The technology adapts to the environment, the workflow, and the people it's supporting. That's what allows us to apply the same connected platform across very different industries while staying focused on solving real-world challenges.
16. TechForce's robots are built to continuously adapt as conditions change. What does "adapt" actually mean here? What is happening inside the system when a robot adjusts to a changing situation?
For us, adaptation means the robot can respond to what's happening around it instead of following the exact same sequence every time. Real operations are constantly changing. People move throughout the facility, new obstacles appear, equipment gets relocated, and priorities shift throughout the day. AI helps the robot recognize those changes, understand how they affect the task at hand, and adapt while continuing to complete its assigned work.
That's really no different than what people do every day. They adjust as conditions change while still focusing on the job they're there to accomplish. That's what makes autonomous robotics practical in real-world operations.
17. Real environments are unpredictable. What happens when a robot encounters a situation that the system cannot immediately interpret or respond to with confidence?
Real-world operations are unpredictable, so the platform is designed with that in mind. If a robot encounters a situation it can't interpret with confidence, it doesn't guess. It slows down, stops if necessary, requests additional information, or alerts the appropriate personnel depending on the situation. The priority is always to maintain safe and reliable operation while protecting the workflow.
That's one of the advantages of combining robotics, AI, and connected technologies. The platform is continuously gathering more information about what's happening around it to support the next appropriate action. The measure of a good autonomous system isn't how often it acts on its own. It's knowing when to act, when to wait, and when to involve a person.
18. AI can influence decisions that ultimately become physical actions. How does TechForce make sure those decisions remain appropriate and controlled, and when should a human become involved?
We've always believed robotics and AI should support people, not replace them, and that philosophy carries through to every decision the platform makes. AI helps interpret information, recognize changing conditions, and support operational workflows, but it operates within defined parameters based on the application and environment. Safety, reliability, and the operational objective always come first.
Some decisions are best left to people. If the platform encounters something unexpected, lacks the confidence to respond appropriately, or requires human judgment, it can notify the appropriate personnel and allow them to make the next decision. We see AI as a tool that helps people work more efficiently, not one that replaces their experience or judgment.
19. TechForce's RaaSP model includes deployment, training, software updates, maintenance, and ongoing support. How does staying involved after deployment help the company improve the AI and software running behind its robots?
One of the biggest advantages of staying involved after deployment is that the platform continues to evolve alongside our customers' operations. As customers grow and their operational needs change, we continue refining AI, improving software, enhancing workflows, and introducing new capabilities based on real-world use.
Deployment is just the beginning. Ongoing software updates, maintenance, training, and support help customers continue getting more value from their robotics while keeping the platform aligned with the way they operate.
20. Ultimately, how do you know whether the AI is getting better? What do you look at in real world operations to determine whether changes to the system are actually improving robot performance?
At the end of the day, we measure success by how well the platform supports our customers. Is the robot completing its tasks reliably? Is it adapting to real-world conditions? Is it helping people work more efficiently? Those are the things that matter most. Robotics and AI are only valuable if they help people and improve the way an organization operates.
Real-world deployments give us that feedback every day. We look at how the robots are performing, how operations are improving, how the technology is being used, and where we have opportunities to make it even better. We believe that's the future of connected autonomous robotics, where robotics, AI, software, and facility infrastructure continue working together to better support people and real-world operations. Success isn't measured by how advanced the AI is. It's measured by whether robotics and AI help people and improve the operation.
