Dedicated environments, keys, and tenant boundaries.
Edge AI for industrial campuses
Edge AI computing,built into the campus.
Customer-controlled AI infrastructure that coordinates compute with campus energy, close to the data that matters.
Compute stays inside the feasible site power envelope.
Production workflows replace commodity GPU-hours.
One operating layer. Four systems kept accountable.
Tulkun connects secure infrastructure, site energy, governed software, and delivery teams while existing safety systems keep authority.
Customized AI utility
Dedicated compute, storage, identity, and approved cloud capacity shaped around the campus.
Dedicated environment
Customer-managed keys
Isolated tenant boundaries
Catalyst coordination
Flexible compute follows the feasible envelope supplied by the professional microgrid EMS.
Compute scheduler
↔
EMS authority
Forward Deployed Engineering
Domain and platform engineers turn infrastructure into measured, governed production workflows.
See the first workflows
Life sciences first
Keep sensitive work close. Move decisions faster.
Start with persistent, proprietary workloads and a defined acceptance metric. Scale only after the pilot proves value.
Discovery workspaces
Secure environments for model development and research data.
Scientific pipelines
Molecular design, protein design, omics, and imaging.
Grounded knowledge
Citation-backed tools over approved internal sources.
Grow from real workloads to campus capacity.
Begin with evidence, prove a narrow deployment, then add infrastructure in step with contracted demand.
Assess
Map workloads, data, software rights, security, and available power.
Pilot
Run a paid 6-12-week scope against an agreed acceptance KPI.
Scale
Add purpose-built capacity when measured demand justifies it.
Start with the workload that matters most.
We will define the campus constraints, a credible first scope, and the proof needed to move forward.