“AI everywhere” messaging is sure to feature strongly at Mobile World Congress Barcelona 2026. But if you’re building Physical AI—AI that acts in the real world, through people, robots, devices, and workflows—there’s an inconvenient reality: your models are only as good as their understanding of where things are, right now, and how that context changes.
In enterprise verticals like healthcare, “good enough” location (room-level Wi-Fi triangulation, periodic BLE pings, badge taps) often isn’t good enough. Current state-of-art Real-Time Location Services prove their worth in many cases, but when seconds matter, when workflows cross departments, when assets vanish into the invisible gaps between systems, the missing ingredient is spatial truth.
That’s where danalto’s FiLo comes in: a UWB-enabled, high-performance, real-time fine location service designed to bring precise, reliable, low-latency location context to the systems that run critical operations—unlocking a step-change in Physical AI capability.
Physical AI: from inference to intervention
Most enterprise AI today is descriptive (dashboards), predictive (forecasting), or generative (content and copilots). Physical AI goes further: it closes the loop between sensing, reasoning, and acting.
To do that, it needs three things:
- Continuous, trustworthy signals (not just occasional updates)
- Real-time context (not yesterday’s logs)
- Actionable integration into operational systems (not a standalone map)
Location is the backbone signal that connects people, assets, zones, events, and policy. Without it, Physical AI becomes an “indoor guesser” rather than an operational engine.
Why UWB—and why now?
Ultra-Wideband (UWB) is having a moment for a reason: enterprises are pushing automation deeper into safety-critical and efficiency-critical domains. They need indoor positioning that is:
- Precise enough to distinguish bedside vs. hallway, clean vs. dirty utility, Zone A vs. Zone B
- Fast enough to support real-time alerts and workflow triggers
- Reliable enough to be trusted by clinicians, security teams, and operations leaders
Danalto FiLo is built to turn that UWB foundation into a high-performance location service: a real-time stream of location events and context that applications can consume to drive decisions.
Think of it as the location layer for Physical AI—not just “dots on a map,” but a continuously updated model of movement, proximity, dwell, and transitions.
What “high-performance RTLS” really means in practice
When people hear “RTLS,” they often picture a floorplan view that’s useful in demos but underwhelming in operations. FiLo targets something different: operational-grade location as a service.
That includes capabilities like:
- Low-latency eventing: location transitions that can trigger actions immediately (not minutes later)
- Real-time accuracy at scale: reliable tracking across busy environments with many moving entities
- Rules and context: zones, geofences, proximity, dwell time, and motion states that reflect real workflows
- Integration-first architecture: location isn’t the UI—it’s the signal delivered to your applications, workflows, and AI agents
This is the shift from “tracking” to contextual control.
Healthcare: patient safety needs instant awareness
In hospitals, location is not a convenience feature—it’s a safety mechanism.
1) Patient safety: rapid response and prevention
Consider scenarios like:
- A high-fall-risk patient exits a bed at night
- A vulnerable patient moves toward an unsafe area
- A patient under observation leaves a permitted zone
Physical AI can help—if it has immediate, trustworthy context. FiLo can provide:
- Zone-based alerts with context (who, where, directionality, dwell)
- Proximity intelligence (e.g., patient near stairwell, restricted corridor, or exit)
- Workflow triggers (notify the right team, open the correct task, log the event)
Instead of relying on periodic checks, you can build always-on safety guardrails that reduce adverse events and response times.
2) Asset-finding: fewer searches, more care
Hospitals lose time and money searching for:
- infusion pumps
- wheelchairs
- portable monitors
- specialty beds
- diagnostic devices
The cost isn’t just replacement—it’s clinician time, delayed care, and cascading inefficiency. FiLo enables:
- Real-time asset discovery (find the nearest available item now)
- Utilization insights (what’s idle, what’s overused, what’s bottlenecking)
- Process automation (auto-route retrieval tasks, auto-escalate if assets remain missing)
With Physical AI, the system doesn’t just show where something is—it can orchestrate the next best action to get it where it needs to be.
From RTLS to Physical AI: the capability jump
The major step-change is this: FiLo makes location machine-consumable at operational speed. That’s what turns AI from “analysis” into “agency.”
Here are a few patterns we’re seeing enterprises move toward:
Location-aware AI agents
Agents that can answer and act on questions like:
- “Where is the nearest clean infusion pump to Ward 4?”
- “Which patient has been in a high-risk zone for more than 90 seconds?”
- “What assets are stuck in transit longer than expected?”
Closed-loop workflow automation
Rules plus AI can:
- create a ticket automatically
- notify the correct team based on role and proximity
- confirm resolution via location change (e.g., asset returned to storage)
Spatial operational intelligence
Not just heatmaps—causal visibility:
- Where do delays begin?
- What paths correlate with incidents?
- Which zones create friction?
This is how enterprises turn movement into measurable performance improvements.
Enterprise verticals beyond healthcare
Healthcare is one of the clearest examples, but the same Physical AI patterns apply across verticals:
- Manufacturing: tool tracking, WIP flow, safety zones, autonomous material movement
- Warehousing & logistics: dock-to-stock velocity, forklift proximity safety, asset utilization
- Airports & venues: staff dispatch, equipment readiness, incident response coordination
- Energy & utilities: worker safety, restricted areas, critical equipment availability
Wherever operations are physical, dynamic, and time-sensitive, real-time location becomes a strategic signal.
What to look for at MWC 2026
If you’re attending MWC Barcelona 2026 and evaluating “AI for the enterprise,” a useful filter is:
Can this AI system reliably know what is happening in the physical world—right now—and trigger the right action?
If the answer is “not really,” you’re missing the spatial layer.
FiLo is built to provide that layer: UWB-enabled, high-performance, real-time location as a service—so enterprises can build Physical AI that is safer, faster, and operationally grounded.
The bottom line
Physical AI doesn’t start with prompts. It starts with signals—and for enterprise operations, location is one of the highest-value signals you can instrument.
With FiLo, danalto is helping enterprises move from:
- manual searching → automated discovery
- delayed response → real-time intervention
- best-effort estimates → spatial truth
- AI insights → AI-driven action
If you’re at MWC 2026 looking for the infrastructure that makes Physical AI real in healthcare and beyond, it’s time to look at the location layer.