Scope and Readiness
Confirm workflow, data boundaries, private inference target, admin scope, infrastructure path, compliance concerns, success metrics, and pilot scope.
Private AI Platform Pilot
Prove private AI operations in your own environment.
If you need inference economics, governance, deployment, admin, and cost-control clarity before a pilot, start with the Private AI Readiness & Cost-Control Diagnostic. The pilot path below is for teams with a sponsor, workflow, and environment ready for scoped engagement.
Srasta pilots are built for regulated-adjacent teams that need private inference, user onboarding, company-aware AI, audit evidence, and a clear production decision path without handing every prompt to an external token-metered endpoint.
Pilot thesis
Useful AI work on private infrastructure, managed by admins, governed by policy, with evidence.
Private inference engine Customer-controlled deployment Admin onboarding and role access Governance, audit, and compliance evidence
Who it is for
Teams with real governance pressure and a real AI workflow.
Best-fit pilots have a motivated executive sponsor, a concrete workflow, and enough security or compliance pressure that unmanaged AI cannot move into production.
Regional banks Boutique asset managers Mid-cap insurance Specialty pharma Regional health systems Regulated fintech, healthtech, and legaltech
Pilot flow
Four phases, one production decision path.
Controlled Deployment
Deploy Srasta in the customer environment, verify the install, configure model routing, memory scopes, admin roles, and tool policies.
Workflow Validation
Run the selected workflow through private inference, governed retrieval, controlled tools, audit, and operator paths.
Executive Readout
Review workflow outcomes, inference economics, prompt and memory evaluations, policy behavior, admin adoption, audit evidence, operator health, and expansion fit.
What the pilot proves
The platform is evaluated as product behavior, not slideware.
The pilot should show whether Srasta can run useful AI inside company context while keeping inference, users, memory, tools, and evidence under enterprise control.
- 01Admin configures role-based model access.
- 02User asks a company-specific regulated-workflow question.
- 03Srasta retrieves scoped company memory and routes to an approved private model.
- 04Tool execution or policy action runs through the governed path.
- 05Prompt, memory, policy, and compliance-rule evaluations are reviewed.
- 06Admin, audit, and operator views show who used what, what happened, and whether the runtime is healthy.
Pilot outputs
What both teams should know by the end.
Inference fitCan private models meet the workflow quality, latency, and cost profile?
Governance fitCan the security and compliance story be reasoned about?
Workflow valueDoes company-aware AI improve the target workflow?
Admin fitCan teams onboard users, assign roles, and manage model access cleanly?
Operational fitCan operators deploy, verify, observe, recover, and upgrade it?
Commercial fitIs there a path to annual platform subscription and expansion?