DAQC Robotics

Building the infrastructure between AI and the physical world.

RoboDAQC
Datasheet in. Verified integration out.

AI can write the code. RoboDAQC helps it make the hardware actually work.

Connect a device, give the agent its documentation, and RoboDAQC provides the hardware access, feedback and verification needed to integrate it against the real system.

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agent ⇄ robodaqc · imu bring-up
  1. understanddatasheet.pdf → register map, I²C addr 0x6A
  2. acti2c-1 @ 400 kHz · probe 0x6A → NACK
  3. observebus scan → device ACKs at 0x6B (SA0 pulled high)
  4. correctupdate address · re-read WHO_AM_I → 0x6C ✓
  5. verifyaccel/gyro stream · 500 samples within spec ✓
  6. reusedriver + ROS 2 node + verification record saved
Illustrative session: understand → act → observe → correct → verify → reuse.

The problem

Every new device becomes another integration project.

Sensors, actuators, controllers and payloads come with different protocols, libraries, firmware, quirks and failure modes.

  • CAN
  • RS-485
  • UART
  • SPI
  • I²C
  • USB
  • Ethernet
  • vendor dialects
Engineers still have to work out

Is the problem the code, the bus, the wiring, the component or the documentation?

And sometimes the hardware does not even behave the way its datasheet says it should.

Why it matters

Integration time is engineering capacity.

Early discovery has surfaced examples of:

  • 2–3 wksspent integrating a custom UAV payload — inside a roughly four-week project.
  • ~60%of the relevant project delay attributed to integration in that workflow.
  • 40–50%additional hardware spend estimated in another R&D environment, from unreliable or non-conforming components.

These are individual discovery examples, not market-wide statistics.

They point to the same problem: the physical-software boundary is expensive.

RoboDAQC · AI integration, verification & qualification

Give AI access to physical reality.

RoboDAQC sits between the AI agent and the hardware. It lets the agent:

  1. 1

    Understand

    documentation

  2. 2

    Act

    on real interfaces

  3. 3

    Observe

    device and electrical behaviour

  4. 4

    Correct

    failures

  5. 5

    Verify

    the result

  6. 6

    Reuse

    what worked

The goal is not just generated code.

The goal is an integration you can trust.

Hardware

One interface to the hardware underneath the robot.

  • CAN FD
  • RS-485
  • I²C
  • SPI
  • UART
  • GPIO
  • USB
  • Ethernet

Multi-protocol hardware access, physical observability and a developer environment built for AI-assisted engineering.

When deeper debugging is needed, instrumentation gives the agent evidence from the actual system instead of forcing it to guess.

Capabilities are exposed over MCP, so the intelligence isn't trapped in the hardware: as models improve, the physical interface doesn't have to be rebuilt.

Technical architecture →

Diagram: an AI agent connects over MCP to RoboDAQC, which connects to devices over CAN FD, RS-485, I²C, SPI, UART, GPIO, USB and Ethernet, and returns evidence of what they do. AI agent any frontier model MCP RoboDAQC execute · safety · observe HAL safety instrumentation CAN FDRS-485I²CSPIUARTGPIO motor sensor payload controller evidence + USB · Ethernet · vendor-specific dialects
Architecture sketch. Compact hardware in development.

More than driver generation

Verify the hardware too.

A driver can compile perfectly while the system still fails.

RoboDAQC is designed to compare software assumptions with actual device behaviour. That creates more than code. It creates evidence.

evidence / imu-bringup (example)● verified
  • what workeddriver v3 · 500/500 samples in spec
  • what failedv1 address NACK · v2 scale factor
  • behaviourmeasured timing, levels, responses
  • configurationbus, rate, address, wiring verified
  • vs datasheetaddress strap differs from documentation

The loop

Discover. Integrate. Verify. Qualify. Reuse.

  1. Discoverthe right component.
  2. Integrateit into the system.
  3. Verifywhat it actually does.
  4. Qualifythe device, configuration and supplier.
  5. Reusethe result instead of starting again.

Who it's for

Built for teams that repeatedly make unfamiliar hardware work.

  • Robotics & UAV OEMsintegrating customer-specific payloads, sensors and actuators.
  • Systems integratorscombining components from multiple vendors and protocols.
  • Hardware R&D teamsbringing up, debugging and qualifying new electronics.
  • Controls & autonomy teamsneeding visibility into the physical system beneath their software.

If your engineers repeatedly bring new sensors, actuators, controllers, payloads or instruments into a working system, RoboDAQC is being built for you.

The bigger opportunity

Integration is the wedge. Verified physical knowledge is the opportunity.

Every completed integration can leave behind reusable knowledge about a real device. At scale, that becomes a machine-readable layer of:

  • drivers
  • measured behaviour
  • compatibility
  • calibration
  • failure modes
  • qualification evidence
  • simulation models

A knowledge layer for the physical world.

Today

In active development.

  • Developer workbench & core hardware-access stackBuilt · hardening
  • Compact carrier & instrumentation hardwareIn development

We're looking for engineers and teams with real hardware integrations to test against.

Bring us a difficult device.

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