Assemble Labs provides an AI-driven IDE plugin that automatically links firmware code with hardware schematics and test equipment to generate and run diagnostic experiments. The system captures measurements, performs root‑cause analysis, and can apply and verify code fixes on‑prem or in air‑gapped environments, accelerating embedded firmware development.
Funding
Funding not disclosed
Founders
Product
Problem
Embedded firmware teams spend weeks manually correlating schematics, datasheets, and test equipment outputs, leading to slow debug cycles, missed power regressions, and delayed product releases.
Solution
Assemble Labs delivers an autonomous AI agent that operates directly within the developer’s IDE and interfaces with existing lab instruments such as oscilloscopes, logic analyzers, power profilers, and JTAG debuggers. The agent parses the full firmware codebase, extracts hardware context from schematics and component datasheets, and generates hypotheses about potential failures. It then designs and executes hardware experiments—reflashing the target, capturing measurements, and analyzing results—without human intervention. Based on the data, the AI provides root‑cause diagnoses, suggests corrective code changes, and can automatically apply fixes and re‑verify the build. All inference runs on‑prem or in an air‑gapped environment, ensuring zero data egress and compliance with strict security policies. This closed‑loop workflow compresses the firmware development lifecycle by an order of magnitude while keeping the engineering team in their existing toolchain.
Target Audience
The platform is aimed at embedded systems teams developing IoT devices, automotive ECUs, consumer electronics, and industrial controllers who need rapid firmware iteration and tight hardware‑software integration.
Features
- Native IDE plugins (VS Code, CLion, etc.) that surface schematic‑aware code suggestions and real‑time diagnostics.
- Plug‑and‑play drivers for major test equipment vendors (Saleae, Tektronix, Keysight, Rohde & Schwarz, Fluke, Yokogawa).
- Continuous power‑regression monitoring on every firmware build with automated baseline comparison and alerting.
- AI‑driven hypothesis generation and experiment orchestration: automatic reflashing, stimulus execution, and measurement capture.
- Root‑cause analysis that cross‑references firmware, hardware schematics, and component datasheets using large‑language‑model reasoning.
- Autonomous fix‑and‑verify loop that can apply suggested code patches, rebuild, and re‑run validation cycles.
- On‑prem or air‑gapped deployment options with support for bringing your own licensed LLM (GPT, Claude, Llama, etc.).
- Automatic driver and peripheral initialization code generation tailored to the target MCU and connected components.