The governed agent platform
Put AI agents to work—without losing control.
EvoPort is where AI agents, people, data, and business systems work together. It plans and runs the job while identity, approvals, budgets, evidence, and audit stay attached to every action.
Every run is controlledIdentity, approvals, budget, evidence, and audit are captured.
EvoPort in plain English
It is the operating layer between your people, AI agents, and business systems.
Not another chatbot. EvoPort gives AI a controlled place to do real work across your organization.
The simplest definition
You give EvoPort a job. Its agents complete the job. Your rules stay in control. You receive the outcome and the proof.
A brief, trigger, schedule, question, or app request.
It plans the steps and assigns agents, tools, and knowledge.
Approved data and integrations let the work move beyond a prompt.
The output arrives with approvals, sources, cost, and run history.
What happens in one run
From request to result, nothing important is invisible.
The same five-step pattern can power a quick answer, a long-running workflow, a scheduled process, or a high-volume batch.
Start with a business request, research question, engineering task, event, schedule, or application action.
The platform breaks the job into steps and selects the agents, skills, tools, knowledge, and run path required.
Agents use approved data and connected systems to research, create, analyze, automate, or deliver the requested outcome.
Identity, policy, permissions, budgets, and approval gates are checked before sensitive actions move forward.
The result arrives with the run history, sources, decisions, costs, exceptions, and audit trail needed to trust it.
What the platform contains
One platform. Five understandable layers.
Each layer has a simple job. Together they turn isolated AI experiments into a reusable enterprise capability.
Apps, workspaces, and solution packs
Role-specific places for teams, clients, and partners to use AI in the context of real work.
EvoGen, agents, skills, tools, and workflows
The multi-agent runtime plans, coordinates, executes, reviews, retries, and recovers the work.
Models, knowledge, and business systems
Use multiple AI providers, 700+ integrations, MCP tools, APIs, webhooks, and agents from other stacks through A2A.
Identity, policy, budgets, evidence, and observability
Controls are enforced inside each run—not added as a report after the work is finished.
Managed or private deployment
Use EvoPort-hosted delivery or engagement-specific private-cloud, isolation, and data-residency patterns.
Governance is not a separate box.It runs through every layer and every action: who asked, what was allowed, which system was used, what it cost, who approved it, and what happened next.
Platform in practice
Designed around the work—not around a model demo.
These representative product concepts show the kinds of governed operating surfaces Nebula9 designs and delivers with EvoPort.
Decision intelligence
Put governed answers inside the work.
Connect operational data, natural-language analysis, human review, and decision-ready outputs in one role-specific surface.
Representative Nebula9 product concept. Final interfaces and capabilities vary by implementation.
Workflow orchestration
Make every handoff and exception visible.
Coordinate actions across systems while approvals, queues, escalations, and evidence stay explicit instead of disappearing inside an automation.
Representative Nebula9 product concept. Final interfaces and capabilities vary by implementation.
Research operations
Move from a question to traceable evidence.
Structure sources, research tasks, review, citations, reusable knowledge, and reporting as an operating workflow—not a one-off prompt.
Representative Nebula9 product concept. Final interfaces and capabilities vary by implementation.Control without friction
Governance happens while the agent works.
Every run answers the questions enterprise teams care about: who acted, what they could access, which actions needed approval, what was spent, what evidence was produced, and how to stop or recover the run.
- Identity and accessSSO, SCIM, roles, and just-in-time access keep every user and agent inside the right boundary.
- Policy and approvalsHigh-impact actions pause for the right person instead of relying on a blanket permission.
- Budget checksModel spend is checked before execution and attributed to the correct tenant, project, and workload.
- Audit and evidenceInputs, sources, tool calls, decisions, approvals, exceptions, and outputs stay traceable.
- Observability and recoveryTeams can see health, latency, failures, usage, cost, retries, and controlled re-runs.
- ContainmentKill switches, egress controls, and scoped connections limit what a run can do when conditions change.
Platform zones
Six zones for the enterprise AI lifecycle.
Each zone maps to a practical adoption motion: build, agent operations, research, transformation, app creation, and runtime operations.
Build zoneEngineering delivery
Plan, build, validate, and ship software, APIs, data, ML, and deployment work with delivery evidence.
Open on EvoPort.aiAgent zoneSpecialist agentsDiscover, onboard, supervise, and improve role-specific agents through a governed enterprise surface.
Open on EvoPort.aiResearch zoneResearch operationsTurn questions, approved sources, evidence, reviews, and reports into reusable research operations.
Open on EvoPort.aiTransformation zoneWorkflow automationAutomate cross-system business workflows with AI actions, approvals, and integration-led handoffs.
Open on EvoPort.aiApp StudioAI app creationDesign, test, and publish AI use cases around real inputs, outputs, channels, users, and review gates.
Open on EvoPort.aiOperationsRuntime operationsOperate AI with observability, reliability context, cost awareness, support cadence, and audit history.
Open on EvoPort.aiDelivery meets platform
Nebula9 engineers the workflow. EvoPort gives it an operating home.
Advisory, engineering, data, integration, change, and operating support remain essential. EvoPort turns that work into a reusable, governed capability when the use case deserves to scale.
- FrameChoose the workflow, value case, risk model, and rollout sequence.
- EngineerBuild the app, agent, automation, research system, integration, or analytics layer.
- ControlAdd access, approvals, review gates, audit evidence, and operating policies.
- ImproveTrack usage, reliability, outcomes, adoption, and the next operating change.
Choose one workflow with clear users, value, data, review needs, and an accountable owner.
Validate the experience, integration, risk controls, and success measures on a focused scope.
Move into production with operating ownership, support, observability, and adoption built in.
Reuse the proven pattern across teams, adjacent workflows, integrations, and platform zones.
FAQ
Common questions
What is EvoPort.ai?
EvoPort.ai is Nebula9.ai enterprise AI platform for building, governing, and operating AI apps, specialist agents, workflow automation, and research systems.
Is EvoPort separate from Nebula9.ai?
No. EvoPort.ai is the product platform layer from Nebula9.ai. Nebula9 provides advisory and engineering services around the platform.
Who should use EvoPort.ai?
EvoPort is built for enterprise teams that need governed AI adoption across business users, technology teams, data teams, and operations.
Enterprise readiness
Security, governance, and procurement questions have a clear review path.
Start with the public security hub, then request the legal and technical material needed for your procurement review.
Review security postureUse the security hub to review hosting, analytics consent, data handling boundaries, and procurement questions before a formal engagement.
Nebula9 scopes workflows around access, approvals, evidence, auditability, human review, and operating ownership.
Where EvoPort is used, the platform fit conversation covers permissions, approvals, observability, audit history, and deployment model.
Formal pricing, DPA, product terms, and security questionnaires are handled through the workshop and procurement review path.
Next step
Book a platform-fit sessionPut one real workflow through the platform test.
In one focused session, map the users, systems, actions, review gates, evidence, operating ownership, and rollout path required to make the workflow production-ready.