OriginAI platform
One agentic operations layer that understands intent, applies policy, coordinates approvals, and completes work across the systems your teams already trust.
Request understood
Identity + intent
Policy verified
Controls applied
Action completed
Auditable outcome
Operating surfaces
Give employees one intelligent front door while every system of record, ownership rule, and approval path stays authoritative.
Origin Console · shipping today
Self-service infrastructure for every tenant, held behind a plan, a quota check, an approval gate, and an audit trail. Teams pick a cloud account, region, environment, and blueprint modules. Origin previews the exact resource names, checks quota headroom, runs the plan, and waits for an approver before anything is created.
Interactive · Switch cloud and role to see the controls change
Viewing as
Submits deployments and manages blueprint modules
Contoso / prod / westeurope
OpenTofu state · stcontosotfstate/prodRecent activity
All tenants · Azure + AWSSubmitted
Request captured with tenant, region, and module set
Planning
OpenTofu plan runs against the tenant state backend
Approval gate
Plan held until an approver signs off
Applying
Approved plan applied with scoped cloud credentials
App install
Ansible provisions the application on new instances
Deployed
Endpoints published, evidence written to the audit log
The request screen previews every computed resource name and checks quota headroom in the target account first.
Plans wait for a named approver. Bypassing the gate is itself an audited event, so the exception is visible.
Endpoints, database passwords, and SSH keys are limited to super administrators, and every reveal, copy, or download is written to the audit log.
Any plan with deletions lists the exact resource addresses and needs a separate confirmation before it runs.
One operating model
The same operations graph becomes a focused view for FinOps, platform, IT service, and People Ops, without fragmenting the underlying request, decision, action, or evidence.
Interactive prototype · Select a role below
Cloud economics
Tie every forecast change to an owner, service, deployment, and safe remediation path.
Addressable spend
$184K
Illustrative monthly opportunity
Policy coverage
92%
Resources with owner + budget
Actions prepared
27
Rightsize, stop, or expire
Illustrative spend under active governance
$1.24M
Platform modules
Every module shares the same identity, context, policy, approval, execution, and audit layer, so governance gets stronger as you expand.
Service & support
Understand context, search approved knowledge, execute allowed fixes, update ITSM or HRIS, and close the loop in the user’s channel.
Access & provisioning
Create accounts, assign apps and groups, issue devices, or prepare cloud environments with identity verification, approvals, time bounds, and auto-revoke.
Cloud provisioning
Pick a cloud account, region, environment, and blueprint modules. Origin previews computed resource names, checks quota headroom, and reuses the tenant baseline instead of recreating it.
Infrastructure as code
Every change runs as an OpenTofu plan against tenant state, waits at an approval gate, applies with scoped credentials, and hands new instances to app installation as a separate stage.
Code & pipelines
Explain failed builds in plain language, correlate logs and recent changes, draft a fix, and rerun only the stages your policy permits.
Cloud FinOps
Show cost deltas in pull requests, enforce budget guardrails, attribute spend to owners, flag anomalies, and propose rightsizing or shutdown actions.
Governance in the action path
Every request is evaluated through identity, tenant, role, data scope, risk, and policy. Sensitive actions add deterministic approval, short-lived credentials, controlled execution, verification, and rollback evidence.
Select a sensitive action
Policy decision trace
Identity + context
Administrator · approved blueprint · existing baseline
Risk · Standard
Approval gate
Deterministic approval
Named super administrator
Least privilege
Scoped service principal or IAM role
Controlled execution
OpenTofu apply, then app install as a separate stage
Evidence + rollback
Decision, action, result, and recovery path retained
Final disposition
No agent receives standing authority. Execution follows the evaluated path above.
Agent assurance · guardrails + evals
Origin combines deterministic runtime guardrails with continuous evaluations. Access policy controls what an agent may inspect, propose, approve, and execute; eval gates measure whether each version is accurate, grounded, safe, fast, and cost-aware before it reaches production.
Production traces then become regression tests, so reliability improves from observed failures instead of intuition alone.
Continuous assurance loop
Define
Version the agent, prompt, tools, policies, data scopes, and success criteria together.
Evaluate
Run golden tasks, adversarial cases, policy checks, and tool-use simulations before promotion.
Release
Use shadow traffic, canaries, approval gates, and rollback thresholds for controlled rollout.
Observe
Trace intent, retrieval, decisions, tool calls, cost, latency, outcomes, and human overrides.
Improve
Turn production failures into regression cases and promote only versions that clear the gate.
Eval release gate
Regression suite · 1,240 representative and adversarial cases
Safety-critical checks are hard gates, not averages. A candidate with a policy bypass, unauthorized tool call, cross-tenant leak, or unhandled destructive action does not promote even when its overall task score improves.
Illustrative eval view and thresholds. Production suites are configured per customer, workflow risk, policy, and service-level objective.
Runtime guardrails
Models can reason and propose. Deterministic policy, identity, and execution controls decide what is actually allowed to happen.
Identity, tenant, role, data classification, risk, region, and workflow policy determine the available path before a model can call a tool.
The runtime validates the planned action again, issues short-lived credentials, enforces limits, and stops or escalates when evidence changes.
Origin checks the destination system, records evidence, detects policy drift, and keeps a tested recovery path for reversible operations.
Separation of duties
No self-approval
The agent that prepares a sensitive action cannot approve its own plan.
Versioned decisions
Every run records the agent, prompt, policy, tool, and eval versions used.
Measured in production
Overrides, failures, drift, cost, and latency feed monitoring and future regression tests.
Control by design
Identity, role, risk, region, and approval requirements are evaluated before any tool is allowed to execute.
Origin coordinates the systems you already run. It does not create another disconnected source of truth.
Escalations, approvals, exceptions, and rollbacks reach the right owner with complete context and a durable trail.
Shared guardrails
Applied across every workflow.
Designed advantage
Most product categories optimize one step. OriginAI is designed around the complete operating loop, from intent to a verified, governed outcome across systems.
Category-level positioning based on common product architectures; exact capabilities vary by implementation. This framework explains OriginAI's design thesis without relying on named competitors or unsupported market-share claims.
Connect service, HR, identity, cloud, code, delivery, cost, knowledge, and collaboration systems. Filter by operating domain.
Start with one high-value workflow
Map the request, controls, systems, and measurable outcome with our team.
