Governed AI infrastructure

The control plane for enterprise AI execution.

Srasta lets organizations run private inference, bespoke memory intelligence, governed tools, identity, audit, and operator workflows inside infrastructure they control.

Get trial license → Talk to us
Self-hosted by default OpenAI-compatible gateway Single-node to Kubernetes
Srasta srasta control plane live
Identity Finance analyst role: governed-ai-user
Model policy approved: local 30B
Memory boundary fund docs only
Execution route, retrieve, tool call gateway enforced, event logged
12:03:18 model route approved
12:03:21 memory read scoped
12:03:27 tool action recorded
Private inference vLLM, LiteLLM routing, Ollama fallback
Governed memory hybrid knowledge access, reranking, compression
Operator truth plans, inventory, verify, reset, rollback
Audit posture identity, RBAC, API keys, compliance collateral

The deployment gap

Enterprise AI is no longer blocked by models. It is blocked by control.

Regulated and security-conscious teams want AI in production, but unmanaged model endpoints, scattered knowledge, role-blind access, and disconnected tooling create a stack security teams cannot approve.

LLM usage is hard to audit across teams and tools.
Company knowledge is scattered across documents, tickets, chats, code, and workflows.
Generic retrieval does not become institutional intelligence by itself.
Tool execution can bypass policy without a governed path.
Operators inherit brittle scripts, dashboards, gateways, and model servers.

The shift

The durable enterprise AI layer is the operating system around the model.

identity policy approved models memory boundaries tool controls audit deployment recovery

Srasta productizes that governed layer so useful company-aware AI work happens under enterprise control, with evidence.

The product

A self-hosted governed AI platform for serious enterprise execution.

It runs in the customer environment, from one Linux node to multi-host and Kubernetes deployments.

01

Gateway and model routing

OpenAI-compatible entry point, private inference, model routing, rate limits, and approved-model access.

02

Memory intelligence

Company-aware retrieval across internal knowledge with intent routing, hybrid access, reranking, and context controls.

03

Governed tools

Policy-aware tool execution through a controlled gateway instead of unmanaged agent actions across enterprise systems.

04

Operator control plane

Install, inventory, placement, health, verification, reset, rollback, upgrade, backup, and recovery workflows.

05

Identity and access

OIDC, RBAC, forwarded identity, API keys, model access controls, and team-aware boundaries.

06

Audit and compliance posture

Audit logging foundations, controls collateral, policy profiles, incident response, key rotation, and recovery guidance.

Platform layers

One governed execution path from user intent to enterprise evidence.

Srasta is not a chat UI, a thin model proxy, or an installer. It is the runtime and operator surface around enterprise AI: every request is scoped, routed, observed, and recoverable.

View deployment guide
ExperienceAdmin UI, APIs, CLI, managed clients
Control PlaneInstall, inventory, plans, verify, reset, rollback
Execution PlaneGateway, model routing, tool gateway, policies
Membrane RuntimeStructured memory, commits, drift, rehydration roadmap
Governance PlaneRBAC, approvals, audit, tenancy, controls
Evaluation and ObservabilityPrompt quality, memory behavior, policy decisions, compliance rules, model routing, and audit evidence

What exists today

Run it on your own infra in 15 minutes.

The current platform is already shippable: a 30-day enterprise trial license, a one-line installer, single-node Compose to multi-host to Kubernetes — all self-serve, no sales call required.

Get trial license → Or talk to us about a pilot

Deployment paths

Single-node Compose, guided multi-host Compose, Kubernetes and Helm, hardware probing, placement, smoke verification, rollback, reset, and runtime health.

Private AI runtime

vLLM private inference, LiteLLM routing, Ollama fallback, TEI embedding path where supported, model catalog metadata, and mixed-model routing foundations.

Governance surfaces

OIDC, RBAC, forwarded identity, API keys, rate limiting, tool gateway, managed-client provider endpoints, audit writers, and compliance documentation.

Admin operations

Config history, runtime overview, ingest management, hardware inventory, users, roles, backups, upgrades, rollback, hardening status, and release verification hooks.

Seed-stage wedge

Regulated-adjacent teams need AI governance now.

The broad market is any enterprise that needs private, governed, company-aware AI. The near-term wedge is teams with enough compliance pressure to block unmanaged AI, but enough urgency to evaluate quickly.

Regional banks Boutique asset managers Mid-cap insurance Specialty pharma Regional health systems Regulated fintech, healthtech, legaltech

Demo narrative

Useful company-aware AI work, under enterprise control.

The strongest demo proves that Srasta can route a real request through role-aware model access, governed memory, policy-controlled tool execution, and an audit trail an operator can review.

Try it yourself → Explore the pilot path
  1. 01Admin configures role-based model access.
  2. 02User asks a regulated-workflow question.
  3. 03Srasta uses company memory and an approved model.
  4. 04Tool execution runs through the governed path.
  5. 05Audit feed records the event.
  6. 06Operator sees health, topology, and release identity.

Roadmap to defensibility

Governance as the product, not a bolt-on feature.

Srasta starts with a single gateway and audit chokepoint, customer-owned infrastructure, explicit operator workflows, role-aware access, and runtime truth. The roadmap compounds that into a governed AI operating layer.

Near-term seed demo
  • Live audit feed
  • Role-based model whitelist
  • Governed agent demo scenario
  • Architecture and security collateral
Post-seed enterprise depth
  • Canonical audit schema and tamper-evident event store
  • Operator approvals and step-up authentication
  • Membrane runtime for governed memory and rollback
  • SIEM export, retention, legal hold, compliance evidence automation

Engagement model

Start with a focused design-partner evaluation.

The pitch motion is deliberately practical: deploy in a customer-controlled environment, prove the governance thesis, and turn the result into a reference, testimonial, or LOI if the pilot lands.

Honest boundaries

Current

  • Self-hosted product posture
  • Pilot-ready platform foundations
  • Compliance collateral and audit foundations
  • Single-node, multi-host, and Kubernetes deployment paths

Roadmap

  • Canonical audit event store
  • Full membrane runtime
  • Signed customer-grade release distribution
  • SOC2 and vertical compliance attestations

Contact

Bring enterprise AI under control.

We are looking for design partners with real AI workflows, security pressure, and a reason to prove governed execution quickly.

Srasta

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