Which AI model does IntelliconOps use today?

Blueprint Studio currently includes a direct DeepSeek API integration selected as a cost-conscious option for structured blueprint drafting and revision proposals.

Can IntelliconOps use Claude or OpenAI models?

The authoring contract includes a configurable model gateway. Direct Anthropic Claude and OpenAI adapters are planned, while Codex-oriented engineering workflows can assist developers in creating and refining code-backed blueprint content.

Can AI-generated automation run immediately?

No. Model output is treated as an untrusted draft. It must pass local schema, workflow policy and Ansible validation, then be reviewed, saved and explicitly published before use.

Will IntelliconOps use AI for autonomous cloud operations?

This is planned roadmap work. The proposal is to analyse operational, demand, cost, health and drift signals, recommend responses and permit only bounded, policy-approved actions.

Does AI generate or apply OpenTofu configuration today?

No. OpenTofu execution is available through approved modules, typed inputs and saved-plan approval, but AI selection of the IaC module catalogue and Blueprint-to-OpenTofu generation remain roadmap work. AI must never generate arbitrary HCL and apply it directly.

Will an AI explanation replace the OpenTofu plan?

No. A plain-English explanation is a future assistive feature only. The deterministically parsed OpenTofu plan remains the authoritative information for approval and risk controls.

AI that produces reviewable code

Use LLMs to draft automation. Keep code and controls in charge.

IntelliconOps gives an AI model a bounded, provider-aware Blueprint contract, then turns its response into visible schema, workflow and Ansible code that must be validated and approved before use across AWS, Microsoft Azure or Google Cloud.

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Model output is a proposal

Prompt

Describe the provider, cloud service and desired outcome.

Structured draft

The model returns the required provider-aware Blueprint document.

Code validation

Local rules validate schema, workflow and Ansible.

Human decision

Review the diff, then save and publish explicitly.

Blueprint Studio today

Prompt for a proposal, then work with the code.

The AI proposal is placed into Blueprint Studio’s editors without being saved automatically. Engineers can inspect every generated part and confirm its AWS, Azure or Google Cloud target before accepting it.

Request schema, defaults and UI hints
Ordered provisioning and maintenance workflows
Embedded, validated Ansible configuration
Visible diff, validation results and complete source

IntelliconOps Blueprint Studio with AI revision prompt, blueprint metadata, approval controls and validation status

A model-flexible authoring boundary

Choose the model service without changing the blueprint controls.

Every provider must return the same constrained blueprint structure. The same local validation and human publication process applies afterwards.

Available now

DeepSeek API

The current direct integration provides a cost-conscious route for complete blueprint drafts and revision proposals that follow the IntelliconOps authoring contract.

Boundary: returned JSON remains an unsaved, untrusted draft.

Planned adapter

Anthropic Claude API

A planned Claude integration can use the same prompt, knowledge and output contract for blueprint generation and revision.

Control: provider choice does not bypass local validation or publication review.

Planned adapter

OpenAI API and Codex workflows

A planned OpenAI model adapter can generate structured drafts, while Codex-assisted engineering workflows can help create, test and refine code-backed blueprint content.

Separation: model API authoring and developer coding assistance remain explicit integrations.

AI and OpenTofu

The execution engine is available. AI-driven IaC authoring is not—yet.

IntelliconOps keeps today’s controlled OpenTofu capability separate from the future AI/RAG layer so users know exactly which decisions remain manual and deterministic.

Available now

Controlled OpenTofu execution

An operator chooses an approved module and cloud account, supplies allow-listed values, generates a saved plan and reviews its parsed resource actions before approval.

Versioned AWS and Azure Linux VM module foundations
Deterministic validation, plan parsing and risk indicators
Apply the approved encrypted saved plan

Roadmap—not available today

AI-assisted module selection and explanation

The planned RAG layer will retrieve an approved IaC module catalogue, propose structured Blueprint steps and optionally explain a deterministically parsed plan in plain English.

Select and parameterise approved modules rather than inventing provider resources
Keep policy and risk classification deterministic
Treat AI explanation as guidance, never approval evidence

Non-negotiable boundary: AI cannot invent arbitrary HCL, approve its own proposal or bypass OpenTofu validation and saved-plan review.

Roadmap—not available today

AI operations intelligence

Move from AI-assisted authoring towards policy-bound operational response.

The proposed next layer observes platform and cloud signals, explains emerging issues, recommends a response and acts automatically only where an approved policy permits it.

Observe

Combine job events, health checks, provider metrics, OpenTelemetry traces, cost data, configuration drift and queue demand.

Analyse

Correlate anomalies, identify likely causes, compare blueprint performance and forecast capacity or spend.

Recommend

Propose a remediation, scaling decision, maintenance action or new reviewed blueprint revision with supporting evidence.

Act within policy

Run only allowlisted actions inside approval, cost, capacity, cooldown and recovery boundaries; escalate everything else.

Predictive demand responseScale from measured demand and forecast trends instead of relying only on static thresholds.

Incident analysisSummarise correlated failures, suggest the safest known runbook and open a human review when confidence is insufficient.

FinOps optimisationIdentify idle capacity, cost anomalies and better-fit resource choices without silently changing production resources.

AI with an engineering boundary

Let the model propose. Let validated code and policy decide.

IntelliconOps is designed so that changing the model does not remove review, versioning, validation, approval or auditability.

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