Self-hosted private AI platform
Run inference, company memory, admin, and governance inside your own perimeter — so company-aware AI reaches production without routing a single prompt through an external token meter.
Private inference engine
vLLM, LiteLLM routing, and host-native engines (Ollama, MLX on Apple Silicon) serve open-weight models on your GPUs. Finance plans around capacity, not unpredictable per-token bills.
Install & admin control
Guided topology, preflight checks, smoke verification, reset, rollback, and self-heal — plus one admin plane for users, teams, roles, model access, and runtime health.
Governance plane
One gateway is the single chokepoint: identity, role, per-role model access, and rate limit on every request, written to a hash-chained audit log your security team can verify.
Evidence, not slideware
A real production customer already runs on the released code path. Start with a scoped pilot: customer-controlled deploy, private inference, role-aware access, and an audit trail an operator can review.
Honest about maturity
Built platform foundations today, enterprise hardening now, and deeper governance, audit, and approvals next. We don't pitch roadmap as current capability.
srasta platform path live What Srasta does
One platform: private inference, install and recovery, users and access, and audit evidence — all in your environment. Not a chat app with governance bolted on, and not a thin model proxy.
Host open-weight models on customer-controlled GPUs and route requests through an OpenAI-compatible path that security and finance can reason about.
Use guided topology, preflight checks, smoke verification, upgrade, reset, rollback, backup, and recovery workflows instead of brittle deployment scripts.
Give admins a plane for users, teams, roles, licenses, model access, onboarding, runtime health, and operational handoff.
Capture model, prompt, memory, tool, policy, and admin events so security teams can review evidence instead of trusting screenshots.
Why now
Regulated and security-conscious teams want AI in production, but public token-metered inference, scattered admin surfaces, role-blind access, and disconnected audit tooling create a stack security, finance, and platform teams cannot approve.
The platform thesis
Srasta productizes the whole operating layer: private inference, install control, admin operations, governed memory, policy-controlled tools, and compliance evidence.
The product
Srasta runs in the customer environment, from one Linux node to multi-host and Kubernetes deployments.
Run open-weight models on customer-controlled GPUs, route through an OpenAI-compatible gateway, and replace runaway external token bills with capacity planning.
Scoped retrieval, reranking, and context controls so AI answers with your company's knowledge — not the public internet — with memory behavior you can evaluate.
Install, inventory, topology placement, preflight checks, smoke verification, release identity, reset, rollback, upgrade, backup, and recovery workflows.
Onboard users and teams, assign roles, manage model access, configure licenses, monitor runtime health, and operate the platform without shell folklore.
Audit auth, inference, memory, tools, and admin actions; enforce RBAC and policy; produce evidence for compliance and security review.
See prompt quality, routing decisions, policy outcomes, and runtime health — the operational truth behind every governed response.
Platform layers
Srasta is not a chat UI, a thin model proxy, or an installer. It is the runtime, admin surface, and governance layer around private enterprise AI: every request is scoped, routed, observed, and recoverable.
View deployment guideEvidence, not slideware
Not a staging demo — a real customer operates on the released code path, upgraded canary-first on every tag. The same platform installs in customer-controlled infrastructure, routes private inference through a governed gateway, onboards users and roles, and produces audit evidence for security review.
For buyers, the first motion is a private AI readiness diagnostic or a scoped pilot around one workflow with clear governance, deployment, and cost-control outcomes.
Single-node Compose, guided multi-host Compose, Kubernetes and Helm — with hardware probing, placement, smoke verification, rollback, reset, and cosign-signed, SBOM-attested release bundles.
vLLM on GPU, host-native MLX and Ollama on Apple Silicon, LiteLLM routing, on-box embeddings on arm64, and a curated model catalog with hardware-aware fit.
OIDC + RBAC, forwarded signed identity, a per-role model whitelist, rate limiting, the governed tool gateway, and a hash-chained (SHA-256) audit log with a verify step.
Config history, runtime overview, ingest management, hardware inventory, user onboarding, role grants, backups, upgrades, rollback, and release verification hooks.
Best-fit buyers
The broad market is any enterprise that needs private, governed, company-aware AI. The strongest early buyers have enough compliance pressure to block unmanaged AI, enough cost pressure to question token-metered usage, and enough urgency to run a focused pilot.
Pilot narrative
The strongest pilot proves that Srasta can run a real request through private inference, role-aware access, governed memory, policy-controlled tool execution, and an audit trail an operator can review.
Product roadmap
Srasta is intentionally transparent about maturity. The product is pilot-ready today, the near-term work is about repeatable enterprise hardening, and the longer roadmap compounds into a deeper private AI operating layer.
Customer funnel
The website should drive the same motion as the pitch deck: qualify the private AI need, prove one customer-controlled pilot, then convert successful evidence into an annual platform relationship.
1-2 weeks to map workflow, governance scope, deployment path, cost model, and pilot fit.
4-8 weeks to deploy Srasta, onboard admins and users, run private inference, and prove one workflow.
Turn successful evidence into annual platform subscription, support, and expansion across teams.
Product boundaries
Technical confidence
Srasta keeps the top-level site buyer-focused, but the proof is still visible: security posture, deployment confidence, architecture, operator controls, and implementation-backed documentation.
Contact
Use the diagnostic if you need the governance, deployment, and cost-control plan first. Use the pilot path if you already have a sponsor, workflow, and environment to evaluate.
