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.
AI that produces reviewable code
Use LLMs to draft automation. Keep code and controls in charge.
IntelliconOps gives an AI model a bounded blueprint contract, then turns its response into visible schema, workflow and Ansible code that must be validated and approved.
Book a demonstrationModel output is a proposal
Prompt
Describe the cloud service or change.
Structured draft
The model returns the required 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 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
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.
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.
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.
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, Azure 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 rollback 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.
Explore Python and Ansible executionSee IntelliconOps in action
Give every cloud request a clear, controlled path.
Tell us how your teams manage infrastructure today. We will show you how IntelliconOps can simplify the work.
Book a demonstration