Policy engine
Define access, compliance, and routing rules once and apply them everywhere requests run.
Enterprise AI Governance
Govern AI requests across your systems, data, and teams.
StrataOne sits in the request path and applies identity, policy, retrieval, workflow, and cost controls before answers reach employees or customers.
Gateway
One entry point for prompts, tools, and models
Controls
Identity, policy, approvals, and retrieval in one layer
Connectors
GitHub, Jira, Slack, Snowflake, ServiceNow, and more
Operations
Budgets, quotas, citations, and traceability by default
- Identity and policy
- Enforced on every request, tool call, and retrieval step.
- Retrieval and workflows
- Grounded in enterprise permissions with review and approvals where needed.
- Spend and auditability
- Measured continuously with exports, routing, and quality controls.
Why StrataOne
A single control layer across models, data, and workflows
Most teams assemble AI governance from separate identity, retrieval, workflow, and cost tools. StrataOne keeps those decisions in one place so control logic is consistent across internal copilots, embedded assistants, and automated agents.
Permissions-aware retrieval
Connect enterprise systems safely so answers reflect user access and approved sources.
Workflow orchestration
Insert reviews, approvals, and escalation paths into sensitive AI tasks without custom glue code.
Cost and quality controls
Route requests by budget, latency, and quality targets while keeping a clean audit trail.
Core Capabilities
Coverage that matches how enterprise AI actually gets deployed
The platform is built for teams that need control at more than one layer: identity, data access, workflow execution, and financial accountability.
Identity
- SSO and role enforcement
- User-level prompt and tool controls
- Team-specific routing policies
Data
- Approved connector access
- Permissions-aware retrieval
- Filtering and source citations
Workflow
- Review queues and approvals
- Escalation paths for sensitive tasks
- Audit-ready execution logs
Economics
- Budgets and quotas by team
- Model routing by spend targets
- Usage, latency, and quality reporting
How It Works
Every request follows the same governed path
The operating model stays simple: requests enter one gateway, get checked against identity and policy, retrieve only what is allowed, run through the right workflow, and leave a usable record behind.
- Incoming request Users, internal tools, and product surfaces call StrataOne instead of talking directly to models.
- Policy evaluation Identity, team, data, and workflow rules are applied before any context is retrieved or any model is chosen.
- Retrieval and routing Connectors, budgets, and quality rules determine what context is available and where the request runs.
- Workflow execution Reviews, approvals, and escalations are inserted only where they matter, without breaking the user flow.
- Audit and reporting Each response can be traced by source, policy outcome, spend, and operator action.
Measured Outcomes
Outcomes teams track after rollout
35%
lower model spend from policy-based routing
2.6x
faster workflow throughput with approvals in line
100%
traceability across governed requests
22%
higher answer quality from retrieval and citations
Connectors
Teams and systems commonly connected
GitHub
GitLab
Jira
Confluence
Slack
Google Drive
Notion
ServiceNow
Snowflake
Datadog
Splunk
Okta
Use Cases
High-value assistants, governed by default
StrataOne is typically used where teams need useful answers and operational control at the same time.
Engineering knowledge assistant
Answer questions across repositories, incidents, and architecture docs with source citations and permission-aware retrieval.
Compliance and audit assistant
Centralize evidence collection, policy checks, and review history across teams, models, and workflows.
Internal operations copilots
Support support, security, and IT teams with governed workflows that include human approval where risk is higher.
Pricing
Pricing aligned to rollout stage and governance depth
Exact pricing depends on deployment model, connector footprint, and review requirements. The structure below reflects how teams usually buy.
Starter
For small teams proving ROI with one governed workflow.
- AI gateway and 3 connectors
- One production workflow
- Usage and basic cost reporting
- Basic human review step
Growth
Best Value
For multiple teams standardizing policy, approvals, and spend controls.
- Up to 10 connectors
- Workflow templates and review queue
- Budgets and quotas by team
- SSO and policy routing
Enterprise
For regulated and large organizations with stricter deployment and audit requirements.
- Private deployment options
- Advanced policy controls
- Advanced review and escalation rules
- Audit exports and retention controls