⭐ Platform Features
Everything you need to turn AI apps and agents from experiments into governed, enterprise-grade workflows.
🧱 At-a-glance
Governance by design
Tenant isolation, RBAC roles, SSO, audit logs, and policy controls ensure agents are safe by default.
Time-to-value
Publish pre-built or custom agents in hours, not months. Reuse connectors, prompts, tools, and data pipelines.
Observability & ROI
Prometheus + Grafana dashboards for usage, latency, cost, and outcomes—prove value, not vibes.
Integrations
Connect Slack, Salesforce, Google Workspace, vector DBs, data lakes, and more.
Vendor-neutral LLMs
Bring your own model or mix providers, with per-tenant policies and routing.
☸ Control plane & governance
Central agent catalog
• One place to see every internal AI agent
• Ownership, status, and tenant association
• Rich metadata: purpose, inputs, outputs, risk level
Policies & approvals
• Define who can deploy, update, or deprecate agents
• Model & tool allowlists per tenant
• Optional review flows for higher-risk use cases
RBAC & SSO
• Integrate with existing SSO / IdP
• Fine-grained roles for hub admins, tenant admins, builders, and viewers
• Clear separation of duties between platform, security, and business teams
🏢 Multi-tenant architecture by design
Hub / Tenant separation
• Hub as control plane for configuration, policies, and catalog
• Tenants as isolated runtimes and data planes
• Clear boundaries for multi-brand or multi-client deployments
Per-tenant runtime & storage
• Separate containers / services per tenant where needed
• Tenant-specific Postgres schemas and object storage locations
• Support for dev / test / prod environments per tenant
Branding & configuration layers
• Tenant-specific branding, routes, and feature toggles
• Config layering for global defaults, environment overrides, and tenant overrides
• Keep one platform, serve many experiences
🧠 AI platform stack
Orchestration & guardrails
• Standard contract for all agents
• Controlled access to models and tools
• Controlled rollout strategies for new agents
Enterprise memory
• Per-tenant vector stores and document repositories
• Support for structured and unstructured data
• Consistent patterns for data governance and retention
Tools & integrations
• Shared connectors to file stores, databases, and internal APIs
• Reusable Python tools across agents
Evaluation & observability
• Prometheus metrics for latency, usage, and errors
• Grafana dashboards per tenant, agent, and environment
🛠️ Developer & builder experience
Simple packaging model
• Build agents as standard Python/Streamlit apps
• Package them as zips with manifests and assets
• Let AI Pipeline handle deployment, routing, and configuration
Local → cloud parity
• Run and debug agents locally
• Use the same packaging format for dev, test, and production
• Avoid "works on my laptop" surprises
Observability baked in
• Standard logging and metrics hooks for every agent
• Easy drill-down when something goes wrong
Future: ecosystem & marketplace
• Curated vertical agent packs (HR, Support, Finance, etc.)
• Partner-built agents with certification and rev-share
• One platform to discover, vet, and run new capabilities
Ready to explore the full platform?
See how the control center, tenant runtimes, and observability layers come together to support real enterprise workflows.