The engineering works. The demos impressed. Customers signed.
Now the real problem starts.
- A machine fails at a customer site and you're debugging it from HQ with partial logs
- A customer calls asking why utilization dropped this week and you need 45 minutes of Slack archaeology to answer them
- Your best field engineer is a single point of failure for institutional knowledge about your entire fleet
- You have 4 customer deployments and no unified view of health across any of them
The engineering was never the bottleneck. Sustainable deployment is.
Introducing Ember: Fleet Observability for Machines in Production
Ember is the operations layer for fleets of programmable machines in production — whether that's robots, PLC-controlled industrial equipment, or any system that emits telemetry.
It sits above your control stack and answers the questions every scaling operations team struggles with:
- Which machines are actually usable right now, across all sites?
- Why did this machine fail, and what led up to it?
- Who should fix it, and do we have the part?
- Did the repair actually work?
- What do I tell the customer?
Ember turns fragmented telemetry, tickets, and tribal knowledge into a closed-loop system for operating machines at scale.
What Ember Changes
Teams using Ember move from reactive firefighting to structured operations:
- Debugging time: hours to minutes
- Unplanned downtime: drops. You're fixing root causes, not chasing symptoms
- Machine utilization: increases. Equipment stops sitting in ambiguous "someone's looking at it" limbo
- Customer uptime questions: answered in minutes, not a 45-minute Slack archaeology session
- Fleet health reports: generated automatically, not compiled manually before every customer call
A Day in the Life with Ember
For the VP of Operations / Program Manager
You open Ember before the customer call. They want to know why uptime was lower last week across their Memphis and Atlanta sites. You pull the cross-site fleet report directly: six incidents, four resolved, two traced to the same motor batch. You have a clean answer and a remediation plan before the call starts.
Leadership asks you to justify the field ops headcount. You pull the monthly operations summary: mean time to repair down 40%, unplanned incidents down 28%. It's not anecdote. It's data with a trail.
A new customer deployment is three weeks out. You check fleet health baselines on existing sites to understand what capacity you actually have. The answer is in Ember, not in someone's head.
For the Field Deployment Lead
Robot 7 at the Chicago site throws a motor fault. You're at HQ. Ember surfaces the full incident automatically: motor temp trend over the last 6 hours, the firmware version running, two prior incidents on the same actuator. You're not starting from scratch and you don't need to be on-site.
You check if the failure correlates with the config push deployed yesterday across three sites. Ember's timeline shows the config delta alongside the health signal across all three. It does. You catch a fleet-wide issue before it takes down more robots. You've done this from a laptop in eight minutes.
A customer is asking why one of their robots has been unavailable for two days. You pull the full history: when it was flagged, what the diagnosis was, what part was ordered, where it is in the repair queue. You send them a summary. The conversation is five minutes, not a fire drill.
For the Robotics Platform Engineer
You're the person the custom internal tool was built around. When it breaks, you fix it. When someone needs data, they come to you. Today, they're going to Ember instead.
A field engineer flags a pattern: three different robots at two sites failing with similar symptoms after the latest firmware push. You pull the cross-fleet incident view in Ember, filter by firmware version, and confirm the correlation in minutes. You file a rollback with actual evidence.
Someone asks you to verify that repairs from last week actually held. Ember's health verification records show post-repair status for every robot. You send the link. You didn't have to run a script, pull logs, or remember where anything was.
Total repair-to-verification time: 20 minutes per incident. Without Ember: typically 2 to 6 hours of emails, guesswork, and waiting, plus the knowledge leaving when you do.
See work orders and health verificationFor the Robotics Engineer
You're ready to test your latest path planning algorithm and need a robot. You open Ember and check the fleet. Robot 3 is healthy and available. Robot 5 is checked out by another team until 11:30. Robot 8 is flagged with a sensor degradation issue. You book Robot 3 for a 2-hour test window. No Slack thread. No walking around the lab asking who has what.
Before you start, you check Robot 3's recent history in Ember. No open incidents, no recent hardware swaps, firmware is on the version you expect. You're not going to spend your test session debugging someone else's residual issue.
Test complete. You close out your booking in Ember and schedule a post-test maintenance window. You noticed the battery was running slightly warm during the session. Ember logs your note, flags it to the next person who tries to book the robot, and creates a lightweight work order so a technician can take a look before it goes back into rotation.
How Ember Works: The Operational Loop
1 Real-Time Fleet Health Monitoring Across All Sites
Ember continuously ingests telemetry across your entire fleet, whether it's one site or twelve:
- Motor temps, CPU load, battery health
- Sensor signals and connectivity
- Firmware/software versions
You get a unified view: which robots are usable, which are degraded and why, how long issues have persisted and whether the pattern is spreading. Works across mixed fleets, not tied to any vendor or autonomy stack.
Explore the fleet dashboard2 Automated Incident Detection With Full Context
When something goes wrong, Ember doesn't just alert you. It captures the full diagnostic picture:
- What changed
- How fast it changed
- What version was running
- Whether this component has failed before, on this robot or others
No more "Motor overheated." You get why it happened, and whether it's an isolated incident or a fleet-wide signal.
See fleet health and incidents3 Work Orders That Actually Help
Every incident becomes a structured work order with context pre-filled. Field service engineers can start immediately or schedule repairs across sites, log time, parts, and cost, and close only after health verification passes.
Ember integrates with Jira, ServiceNow, Asana, Slack, and Teams. Your workflow doesn't change. It gets smarter.
See work orders4 Parts Inventory That Matches Reality
When a robot fails, Ember tells you which part is needed, whether it's in stock, where it is, and what it's compatible with. Inventory is tied directly to work orders with automatic deductions, a full audit trail, and low-stock alerts.
No more fixing one robot and breaking another due to missing parts.
See parts inventory5 Resource Scheduling and Health Gates
Ember acts as the source of truth for robot availability. Reserve robots via calendar across sites, prevent double-booking, and block checkout if robot health is degraded. After use, compare pre vs. post health and automatically flag damage or regression.
Full traceability: who used the robot, when, in what condition, and at which site — effectively a git blame for hardware.
Why Existing Tools Break at Scale
Most teams that hit ~30 robots have already tried to stitch something together. Usually it looks like: Notion for docs, Linear or Jira for tickets, a custom Grafana dashboard for telemetry, and a spreadsheet for parts.
This works until it doesn't, and it stops working fast:
- Grafana shows the symptom, not the cause. You see the spike, but not what changed, what version was running, or whether the component has failed before. Every incident starts from scratch.
- Jira and Linear weren't built for hardware. You can't tie a ticket to a specific robot, a specific failure mode, or a specific part that needs replacing. There's no health gate blocking closure until the robot actually passes.
- CMMS tools are built for factories. Static assets, fixed maintenance schedules. Not machines that are reconfigured, re-tasked, and redeployed across customer sites.
- The internal tool your best engineer built in 2023. It works okay. It's unmaintained. Nobody wants to touch it. You're one resignation away from losing institutional memory of your entire fleet.
The result: data is fragmented, debugging is manual, and operations don't scale past the people who hold the context in their heads.
Ember exists in the gap between all of these — purpose-built for fleets where the hardware is programmable, the configurations change constantly, and failures cost you customer relationships.
The Scaling Inflection Point
At 10 machines, one engineer can hold it all in their head. At 30, that engineer becomes a single point of failure. At 100, they become a bottleneck that's costing you customers.
Every team on the path from pilot to production hits this wall. The question isn't whether. It's whether you build around it with duct tape, or put a real operational layer in place before you're already on fire.
Built for Teams Where the Hardware Is Always Changing
Whether you're running field deployments of robots, managing PLC-controlled equipment across plants, or iterating in lab environments, the pain is the same: configurations change, machines get shared across teams and sites, and failures are never simple.
- "We can't reproduce what happened during yesterday's test run." Ember logs the full state of every robot before, during, and after each session: software version, config, health metrics, who had it and when.
- "Three teams are sharing 8 robots across two sites and nobody knows who has what." Ember's scheduling system gives every team visibility into robot availability, blocks conflicting reservations, and creates an ownership record for every session.
- "We don't know if today's failure is a software regression or hardware degradation." Ember tracks both. Correlate a firmware push with a new failure pattern, or separate mechanical wear from a config change that went wrong.
- "We're debugging a customer site failure remotely with incomplete logs." Ember gives field leads full incident context: state, history, versions, without needing to be on-site.
Teams shipping robots, industrial equipment, or any programmable machines to customers don't need a CMMS. They need an operational memory for their fleet.
Who Ember Is For
Field Deployment Teams
Managing machines across multiple customer sites simultaneously, often remotely. You need visibility not just at each site but across all of them, and you need to answer a customer's question in five minutes, not five hours.
Platform & Controls Engineers
You own the tooling stack — whether that's a robotics platform, PLC programs, or industrial control systems. You're drowning in custom scripts and tribal knowledge. Ember is what that internal tool was supposed to be: maintained, scalable, and not dependent on you personally.
VPs of Operations
Being asked by leadership to justify uptime numbers you can barely measure today. Ember gives you the data layer to measure, report, and improve — and to have credible answers when customers ask.
Heavy Machinery & Industrial Equipment Teams
Running fleets of PLC-controlled systems, heavy equipment, or industrial machines across sites. Your CMMS wasn't built for equipment that gets reconfigured, updated, and redeployed. Ember was.
Teams Scaling Past ~30 Machines
Where one engineer's mental model stops being enough and you need a real operational layer before the next customer deployment puts you underwater.
Frequently Asked Questions
We already have telemetry (ROS, PLC diagnostics, SCADA). Why do we need Ember?
Your existing telemetry tells you what the machine is doing right now. Ember tells you what's happening across its full life: past failures, who used it, what version or config was running, what parts were replaced, what customer site it's at. It also closes the loop: from anomaly detection, to work order, to repair, to health verification. Your telemetry layer doesn't do that.
How does this work with our existing CMMS?
Most CMMS tools are designed for static industrial assets. Ember integrates with them (or replaces the robotic portion) by adding robot-specific context: telemetry-driven work orders, health gates on closure, and traceability across software/hardware changes. If you're already using a CMMS for facilities, Ember handles the fleet layer specifically.
Does Ember replace fleet orchestration?
No, it complements it. Orchestration moves robots. Ember keeps them operational.
What's the data privacy model?
Ember runs on your infrastructure or in a dedicated cloud tenant. Your telemetry data doesn't leave your environment unless you configure it to. We support air-gapped deployments for teams with strict data controls.
What types of systems does Ember support?
Any system that emits telemetry: robots (AMRs, AGVs, arms, drones), PLC-controlled industrial equipment, heavy machinery, kiosks, and more — via MQTT, gRPC, REST, OPC-UA, or IoT platforms. Ember is not tied to any vendor, autonomy stack, or control system.
How fast can we deploy?
Most teams are up and running in days, not months.
The Bottom Line
Teams don't fail because they can't build the machine.
They fail because they can't operate them reliably at scale — across sites, across customers, across the growth that happens after the demos close.
Ember turns fleet operations into a closed-loop system:
Detect. Diagnose. Fix. Verify. Improve.
The engineering was never the problem. Now the operations don't have to be either.
Show us your last failure.
We'll show you what caused it, how long Ember would have taken to diagnose it, and how to prevent it across every site next time.
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