Let AI handle repetitive enterprise work,
with approvals and audits in one seamless flow.
ARI converts natural-language requests into actions, routes them for human approval, and publishes the results.
Clevi designed ARI to turn AI pilots into real business processes.
AI experiments never make it into day-to-day operations
Chatbots and AI experiments keep multiplying, but without links to real workflows, they remain siloed. Each tool is managed separately, and pilots stall without organisation-wide governance.
You cannot trace or audit what AI has done
There is no record of which documents AI used, how it reached a decision, or who approved it. That makes AI tools difficult to use under regulatory or internal audit requirements.
AI can execute high-risk actions without oversight
When AI can call APIs, modify external systems, or deploy code, control becomes difficult. Enterprises cannot accept a system that performs high-risk actions without human review.
AI loses your organisational context
Internal documents, policies, terminology, and work history do not carry over to AI, so teams must explain the context every time. Organisational knowledge never compounds into an AI asset.
Long-running AI work is impossible to follow
For AI tasks that take several minutes or longer, you cannot see intermediate states or pinpoint where an error occurred. That is not a dependable production model.
Receive request
Lock criteria
Execute
Human approval
External action
Publish result
Approval controls
High-risk actions never run without human review. Set approval policies by task type and access level, and automatically block unapproved work.
Audit logs
Audit every AI action at any time. ARI records the complete execution history from request to result in a traceable format for internal audit and compliance.
Knowledge accumulation
Manage context as an organisational asset. Internal documents, work history, and decision evidence accumulate so AI can work from knowledge unique to your organisation.
Server-mediated endpoint
ARI brokers code-execution requests on the server, so clients never need direct access to the execution environment.
Workspace Guard
Policies limit which files and systems ARI can access while operating as a coding agent.
Approval for risky actions
High-risk commands proposed by ARI run only after human review, giving your organisation direct control over the scope of automation.
Verifiable deliverables
ARI stores execution evidence and history with every piece of code, document, and output it creates, so provenance is always available.
Remote file experience
ARI works on files in remote environments while an intuitive interface lets users review and edit the results.
Memory write-back controls
Policies govern what ARI may learn or write to memory during a task, preventing unintended context contamination.
Adapter independence
ARI is not tied to a particular LLM or coding platform. Workflows remain intact when you change the underlying model or execution environment.
Production operability
Monitor task states, errors, and retry history at production depth, and operate AI agents without a black box.
Report automation
Workflow automation
Code deployment
ARI is not another agent. It is an operations, access, and audit control plane that lets multiple agents and tools run safely inside enterprise workflows.
| Alternative | Strength | Limitation | What ARI adds |
|---|---|---|---|
| General chatbot / RAG | Fast Q&A and document answers | Limited execution, approval, external actions, and publishing | Unifies retrieval, execution, approval, and publishing |
| IDE coding assistants (Copilot, Cursor) |
Improves individual developer productivity | Often disconnected from enterprise workflows, remote endpoints, and audit | Brings coding work into Clevi access, approval, evidence, and publishing flows |
| Autonomous agents (OpenHands, Devin) |
File editing, shell access, testing, and long-running coding tasks | Agents tend to own their own memory, tools, and session state | ARI uses only the execution plane, while the server manages policy, memory, approvals, and artefacts |
| Agent frameworks (LangGraph, AutoGen) |
Makes it easy for developers to code agent flows | Requires custom implementation of operational UI, ACLs, domain data, and endpoint queues | Complements them with built-in Clevi operations |
| RPA / traditional workflows | Structured process automation and system integration | Weak at unstructured retrieval, natural-language reasoning, and code changes | Combines LLMs, AgentLoop, Toolset, Memory, and Code Agent with explicit workflows |
* This comparison reflects typical product characteristics. Specific capabilities may vary by version and configuration.
ARI does not create another agent. It is a control plane that lets existing AI agents and tools run safely within enterprise workflows.
It brings your existing AI tools, external coding agents, and internal systems together in one traceable run.
Its adapter-independent design avoids platform lock-in and connects to ERPs, document systems, collaboration tools, code repositories, and more.
For example, actions that exceed the criteria your organisation has defined — such as modifying an external system, accessing specific files, or exceeding a monetary threshold — automatically move to a human review step.
It can be configured so enterprise data and execution history never leave the internal network.
It is well suited to financial institutions, public-sector organisations, and enterprises with strict security policies.
You can integrate the records with internal audit tools or view and export them directly.
They can automate repetitive document, analysis, and approval work without complex AI development.
Platform engineers configure adapters where technical integration is required.
The implementation period depends on the scope of systems being integrated and the size of the organisation; please confirm the specific schedule through an implementation inquiry.