Local AI
Firmware AI that stays inside your walls.
Most embedded teams can't paste schematics and source into a public chatbot. Flash is local-first by design: the analysis runs on your machine, and the model can too.
The problem
Schematics, firmware, datasheets, and logs are often the most sensitive IP a company owns — bound by NDAs, export rules, certification, or plain air-gapped reality. For a large share of embedded work, sending that to a public AI API simply isn't an option.
Generic assistants assume the cloud and assume you'll describe the board by hand. Flash assumes the opposite: your data stays put, and the hardware context comes from your project automatically.
The approach
Local models
Built for private models running on your infrastructure — not a thin wrapper around someone else's cloud API.
Deterministic context
A no-LLM pipeline turns schematics, code, logs, and specs into structured engineering context before the model runs.
No external APIs required
Run in private cloud, on-prem, or fully air-gapped. Sensitive hardware data never has to leave your network.
Hybrid when you choose
Reach for a cloud model only when you decide to — and even then with compact context, not raw project dumps.
The boundary
Schematics · firmware · logs · datasheets
Deterministic context engine
Local model
Cloud model
off by default
Air-gapped mode: no outbound connectivity at all — not even the optional line above.
Go deeper
Deterministic context
How raw files become structured, model-ready context.
Token efficiency
Why compact context costs less per task, local or cloud.
Performance metrics
Local + Flash vs frontier cloud models, measured.
Private deployment
Private cloud, on-prem, and air-gapped options.
Security boundary
What stays inside your network, and why.