Ember Robotics

What do they actually do

Ember Robotics makes an edge observability tool for robots and other embedded Linux systems. Teams install an on‑device agent (packaged as a Linux .deb) that records telemetry, sensor streams, and failure events on the robot, runs even when offline, and syncs results to a central cloud dashboard when connectivity is available (site trial).

Their initial product focuses on camera diagnostics. It detects common camera issues (disconnects, frame drops, time‑sync problems), runs image‑quality checks, raises real‑time alerts, and produces historical reports to help QA and field teams investigate problems faster. Public materials also call out ROS and RealSense support in early deployments (YC launch site).

Typical use: install the agent on devices like NVIDIA Jetson, Raspberry Pi, or Intel NUC; auto‑detect sensors or point it at cameras/ROS topics; choose continuous monitoring or test sessions; and review synchronized traces and version‑aware QA results in a fleet dashboard once devices are online. The product emphasizes offline‑first capture and out‑of‑the‑box sensor support to reduce ad‑hoc scripts and manual log collection (trial site).

Who are their target customer(s)

• Robotics engineers building perception and autonomy stacks: They lose time chasing intermittent camera/sensor issues (disconnects, frame drops, time‑sync and image‑quality problems) that are hard to reproduce and require manual log collection; an on‑device agent that captures traces for later analysis reduces this toil. [https://www.emberrobotics.com/][https://www.ycombinator.com/launches/LVR-ember-robotics-camera-diagnostics-for-robots]
• Validation and manufacturing test engineers running QA on many units: They need consistent, version‑aware tests and aggregated failure reports across devices; ad‑hoc scripts and per‑unit checks don’t scale or reveal fleet trends. [https://www.emberrobotics.com/]
• Field technicians and service teams fixing robots at customer sites: They often lack reliable connectivity and on‑device diagnostics, so troubleshooting is slow or requires returning hardware; an offline‑first agent that stores data locally until sync helps. [https://www.emberrobotics.com/trial]
• System integrators deploying multi‑vendor robot systems: They must validate many camera models and pub/sub setups across different embedded platforms and can’t maintain bespoke scripts for each project; support for common Linux platforms simplifies instrumentation. [https://www.emberrobotics.com/trial]
• Camera and sensor hardware vendors supporting field customers: They receive bug reports without reproducible telemetry, slowing root‑cause analysis and firmware fixes; structured camera diagnostics and an SDK enable shareable failure data. [https://www.emberrobotics.com/]

How would they acquire their first 10, 50, and 100 customers

• First 10: Run tightly supported pilots via YC/network intros and direct outreach, install the on‑device agent on a few test units, and deliver one clear repair or preventative action per pilot to prove value and capture a case study (trial YC launch).
• First 50: Productize pilots into templated QA suites, self‑serve trials, and an interactive sandbox to reduce integration time, and sign referral partnerships with camera vendors, ROS integrators, and system‑integration shops to reach clustered deployments (sandbox trial YC launch).
• First 100: Scale through channel and standardized enterprise deals: convert successful pilots into paid fleet contracts and OEM/integrator resell agreements, publish integration guides and case studies, and offer tiered onboarding/support for larger manufacturers and service organizations (site YC company profile).

What is the rough total addressable market

Top-down context:

The broader robotics industry is on the order of tens of billions of dollars (e.g., ~US$45B in 2024), with robotics software estimated around ~US$20B. Adjacent slices relevant to Ember today include machine‑vision (~US$5.6B total, with software a few hundred million) and robot fleet‑management software (~US$1.35B in 2023) (ABI GMI Interact Analysis GIS).

Bottom-up calculation:

Near‑term, Ember maps to camera/vision diagnostics plus fleet QA/diagnostics. Using published estimates, a conservative pool is ~US$1.8B today: ~US$0.5B for machine‑vision software (approximating the “few hundred million” cited) plus ~US$1.35B for robot fleet‑management software (Interact Analysis GIS).

Assumptions:

• Machine‑vision software is approximated at ~US$0.5B based on “few hundred million” guidance; figure rounded for simplicity.
• Overlap between machine‑vision software and fleet‑management software is limited for the near‑term Ember use case; combined figure is directional, not additive across all vendors.
• Adoption depends on per‑device agent + cloud pricing and who buys (OEM, integrator, or end user); install base is not directly equal to software spend.

Who are some of their notable competitors

• Formant: Robot data and operations platform used for fleet telemetry, analytics, and remote operations; competes for fleet monitoring and debugging workflows.
• InOrbit: Robot operations software for multi‑vendor fleets, providing telemetry, monitoring, and orchestration across deployments.
• Freedom Robotics: Fleet management and monitoring tools for robots, including cloud and on‑device software to operate and support heterogeneous fleets.
• Foxglove: Developer tooling for robotics data (logs, sensors, visualization) that teams use to inspect and debug multimodal telemetry during development and testing.
• Memfault: Device observability for embedded systems (crash reporting, metrics, OTA); adjacent competitor for on‑device diagnostics even if not robotics‑specific.