Research
Truth, source, autonomy, and efficiency are design requirements, not slogans. This is the worldview behind adapterOS — and the operators we want testing the instrument on real document work, especially offline, multi-step review jobs.
This is not a mailing list. If there is fit, we reach out with access details and the workloads we care about most right now.
Four requirements for truthful local AI
Each principle is a design requirement the instrument has to meet. Follow each into the briefings and its deep page.
Workloads we want tested
Follow-up document review
Same packet, second question — does the workspace avoid rebuilding context from scratch?
Offline or air-gapped use
Facilities where outside AI services are off limits or monitored.
Compliance review paths
Teams that need a readable record for security, legal, or accreditation — not just a chat answer.
Operating cost reality
Local hardware, energy, and whether the workflow is cheaper than shipping context to the cloud repeatedly.
What beta partners receive
- Invitation to run adapterOS on your own machine against real work
- Direct line for edge cases — especially degraded answers and boundary failures
- Credit in research notes when you want to be named; anonymity by default
Install and product-loop detail: adapteros.com/how-it-works. Lighter signup: connect.adapteros.com.
Apply for beta / research
Tell us your environment constraints, the document job you would test, and what would count as a useful result for your reviewers.
Start a research conversationStart with a fixed-scope field deployment
One workflow, your environment, hardware included — roughly 4–8 weeks from kickoff. Local, offline-capable, and priced by scope — not by the token. You leave with a review record you can show security and compliance, whether or not you proceed.