NeuroCompute · RLaaS
Learn how NeuroCompute is developing secure, AI-assisted access to physical laboratories—and how to discuss a demonstration or research partnership.
NeuroCompute is developing a cloud access platform for remote experimentation on real laboratory equipment. Its Remote-Laboratory-as-a-Service (RLaaS) approach connects researchers, educators and R&D teams with instruments operated by independent laboratories. The long-term goal is a consistent way to discover equipment, prepare experiments and receive measured results without relocating devices.
Explore the EcosystemRLaaS means providing controlled, network-based access to physical laboratory equipment as a service. Instead of requiring every researcher to travel to a laboratory or buy the instrument, a participating lab can expose approved experimental capabilities through a shared access layer while retaining local control of the device.
Explore the EcosystemThe platform is designed for experiments on real, physical equipment and for collecting measurements from that equipment—not simply simulated results. Simulation or dry-run validation may support experiment preparation, but it is different from actual device execution. Public access depends on which instruments and pilots have been connected and approved.
Read about the technologyThe architecture is intended to support different classes of programmable laboratory equipment, including memristive and neuromorphic devices, electronic measurement instruments, FPGA boards, wireless test equipment and other specialized setups. Camera parameter control is a planned conference demonstration. These are examples of the platform’s scope; they are not a claim that every category is currently available as a public service.
Explore the EcosystemNo. The federated model keeps physical instruments at the participating laboratory. A local computer or gateway mediates authorized experiments, and the laboratory remains responsible for the equipment, its operational policies and safety limits.
Read about the technologyStart by contacting the NeuroCompute team. We would discuss the device, its documentation, available control interfaces, a suitable low-risk experiment and the laboratory’s approval requirements. A pilot would then define the gateway integration, allowed operating parameters, access policy, test procedures and results handling before wider use is considered.
Contact the teamThe proposed architecture combines device-specific capability limits, pre-execution validation, authorized-user permissions and a local laboratory control layer. Critical interlocks and time-sensitive protection must remain on site rather than depend on cloud response time. Some experiments may require an operator’s explicit approval. Exact safeguards are confirmed for each instrument before a pilot.
Read about the technologyThe planned workflow supports device-specific scripts and AI assistance for preparing them from equipment documentation. Researchers should be able to inspect and approve generated instructions before execution. The amount of coding required will depend on the instrument, experiment template and maturity of the particular integration; no universal no-code interface is being promised today.
Read about the technologyYes, education is a target use case. Remote experiments on real instruments could support laboratory courses, online learning, demonstrations and collaborative assignments. Integrations with learning-management systems such as Moodle are part of the roadmap, so universities should discuss course needs and pilot arrangements with the team rather than assume an existing turnkey LMS integration.
Explore participationNeuroCompute is being designed with API, Python, MCP and Model Hardware Standard (MHS) integration routes in mind. Authorized agents could eventually discover permitted device capabilities, prepare jobs and retrieve results. Agent activity must be constrained by permissions, budget limits, approvals, audit logs and laboratory-enforced safety. This is a roadmap direction, not a claim of unrestricted autonomous production access.
Read about the technologyParticipating laboratories retain their physical equipment and background intellectual property. Ownership, access to experiment data, publication rights, acknowledgement, confidentiality and commercial use must be set out in the agreements for each collaboration. NeuroCompute should not imply that all experimental results automatically become public or belong to the platform.
Explore participationNeuroCompute is developing its platform and initial demonstrations and research pilots. General public availability, device inventory and pricing should not be presumed without a specific offer. Potential arrangements include approved research access, pilot projects, reservations, usage-based experiments and, where eligible, grant-supported programmes. Contact the team to discuss the current options for your use case.
Contact the teamRemote access can help multi-institution projects share equipment, run repeatable benchmark experiments, train students and document results across partners. NeuroCompute welcomes discussions about research-infrastructure contributions and pilot work packages. Participation in any specific grant or consortium depends on formal approvals and programme eligibility; no funding award or institutional endorsement is implied.
Explore participationRather than exposing a general-purpose remote computer or an instrument’s screen, the proposed RLaaS platform provides a consistent experiment lifecycle across independent laboratories. It combines declared device capabilities, validation, scheduling, telemetry, reproducibility records and laboratory-level control. The available functionality still depends on the particular hardware integration and pilot stage.
Read about the technologyWhether you manage instruments, conduct research, teach students or build new technologies, let’s discuss a practical remote-lab pilot.