Alloy Robotics

The easiest way for agents to use your robotics data 🤖

[Alloy Robotics @AlloyRobotics]

Robotics teams are drowning in their own data.

Terabytes of images, logs, and 10 kHz time-series.
99% of it never gets looked at.

Incident reports take days.
Root-cause analysis is manual.
Most teams burn 50–80 % of engineering time just trying to find the failure.

We built Alloy to end that.

Alloy is the first platform that lets you search years of robot data in natural language, in seconds.

“Show me every gripper slip when IMU Z-accel > 4 g in salty air" → exact video clips + sensor slices + logs instantly.

No labeling. No scripting. No 1.3 GB MCAPs in Slack.

Tesla & Amazon built this internally.
We're bringing that level of observability to the rest of the world.

We've raised $4.5M pre-seed from Blackbird, Airtree, Skip, Xtal and angels at Tesla, Waymo, OpenAI.

And we're just getting started.

[Joe Harris @_joe_harris_]

never been a more exciting time to be in robotics.

some insights from building @AlloyRobotics:

0:00:00 – engineer → atlassian → eucalyptus → alloy
0:09:30 – why now: cost curves + reusable rockets
0:10:45 – vertical farming post-mortems: unit economics, reliability, scale errors
0:13:40 – reliability in the physical world: from 99% to 99.999%
0:19:00 – operator-to-robot ratio as the core unit economic lever
0:23:30 – common data primitives (vision, time series, logs) + ROS-driven formats
0:26:15 – a robot produces more data in one minute than any LLM can hold in context
0:29:35 – edge AI tailwinds: jetson-class hardware, cheaper LiDAR + IMUs
0:30:20 – VLAs: perception → plan → act (and why smoothness matters)
0:42:15 – ideal customers: high throughput, real deployments, cloud telemetry
0:45:50 – robotics as a deflationary lever

[Joe Harris @_joe_harris_]

everything a robotics company needs to go from 0→1:
- one real customer problem with hard ROI, not a demo
- one hero workflow you own end-to-end (automation + human-in-the-loop fallback)
- simulation for fast iteration (NVIDIA Isaac, MuJoCo, Drake, Genesis)
- a single testbed robot you can abuse every day
- ROS 2 (+ Zenoh) + production-grade drivers and health checks
- a reproducible OS image and reliable OTA (Mender, Balena, Airbotics)
- logs and bags shipped to searchable storage (mostly S3 + custom scripts, Alloy)
- a tight feedback loop between operators, support, and engineering (Slack threads and prayer)

everything you need to go from 1→n:
- fleet management, task dispatch, and traffic control (Formant, InOrbit, Open-RMF, Cogniteam)
- secure connectivity with per-robot identity (Husarnet, Tailscale, Starlink backhaul, mostly custom)
- edge compute and onboard inference (Jetson, Hailo, Luxonis OAK, AMD Kria)
- observability wired into incident response and root-cause analysis (Nominal, Sift, Alloy)
- labelled data, evals, and a versioned ML pipeline into production (Roboflow for CV, W&B for tracking, custom ETL)
- safety cases, audit trails, and compliance your biggest customer will sign off on (mostly custom)
- multi-robot ops playbooks and a real on-call rotation (mostly tribal knowledge)
- a data layer that turns every mission into training signal (Alloy + internal glue)

there is variance across industries but these categories are broadly true for modern robotics teams

[Joumana @JoumanaElomar]

Brand to web for @AlloyRobotics's launch.

The bolt mark is a nod to how robots were originally assembled. Every part of Alloy's presence feels precise, modular and engineered.

[Joe Harris @_joe_harris_]

the modern robotics infrastructure stack:

- simulation and testing (NVIDIA Isaac, Gazebo, MuJoCo)
- connectivity and comms (Wi‑Fi/5G/LTE/Starlink VPN backhaul)
- fleet management and monitoring (Formant, Open‑RMF integrations)
- hardware abstraction and drivers (ROS 2, micro‑ROS, vendor SDKs
- edge compute and onboard AI (NVIDIA Jetson, Qualcomm RB series)
- deployment and OTA updates (Ubuntu Core, Balena, Airbotics)
- safety, compliance, and kill switches (mostly custom built)
- visualisation and replay (Foxglove, Rerun, RViz)
- CV/ML model training and labelling (Roboflow, Scale AI)
- data upload from robot to cloud (Alloy)
- data logging, storage and management (mostly S3, Alloy)
– data pipelines and model delivery (custom ETL, CI/CD, registries)
- observability and root cause analysis (metrics, traces, Alloy)
- the AI-native data layer with MCP/API (Alloy)

[Joe Harris @_joe_harris_]

Advanced Navigation just became Australia's FIRST deep tech unicorn with their $158M Series C.

We've been working behind the scenes with their validation team ...

To cut down 1 day of analysis down to 10 minutes.

This is the future of hardware debugging 🤖

Case study is live now:
https://t.co/teXGOsEi3c

[Joe Harris @_joe_harris_]

Today, we’re launching Try Alloy.

The future of robotics is an AI that uncovers the insights hidden in your logs.

Ask any complex question, and get detailed answers with references from your data. Live now:

[Joe Harris @_joe_harris_]

2026 is a terrible time to build a robotics company.

Imagine building a software company without AWS, Stripe, or GitHub. Everything from scratch. That's robotics today.

Most robotics teams waste their first 6–12 months rebuilding the same data infrastructure, telemetry, internal tools, pipelines. None of it is core IP.

I started @AlloyRobotics to SPEEDRUN all of it. A modern stack so robotics teams can iterate 10x faster than their competition.

If you're building in this space, lets talk.

[Alloy Robotics @AlloyRobotics]

@lukas_m_ziegler Also using ROS here - would love to be added to the list🤖

[Joe Harris @_joe_harris_]

I left a $600M ARR company to build robots.

But after speaking with 30+ robotics founders, they all confessed the #1 reason robots fail:

Data overwhelm.

Its costing the industry billions, and here's why we raised $4.5m to change that. 🧵