Secure OpenClaw in Azure for private, production-ready agents
Secure OpenClaw is the product foundation for teams that want private agent workflows running in Azure without building the operating model themselves. The first two product patterns are a personal assistant that plans, schedules, and reminds with live context, and a research agent that keeps gathering, consolidating, and questioning source material until it can return detailed answers.
What we sell
The offer starts with two defined agent products rather than a generic platform sale. The personal assistant takes a task, breaks it into smaller chunks, reviews the chunks with the user, schedules the work on its own calendar, and later reminds the user with current status, risks, and upcoming work. The research agent runs a longer research loop over internet sources, consolidates what it finds, asks what is still unclear, and repeats until detailed answers can be produced.
These first two use cases only become credible when the agent can operate with private task context, current work status, and controlled access to external data sources without pushing the operating model into an unmanaged public AI workflow.
Use case one
A personal assistant can take a task, break it into smaller chunks, review that plan with you, schedule the work on its own calendar, and when it reminds you later it can include current context such as completed tasks, items at risk, and what is due next.
Use case two
The research agent is designed for longer-running work. It gathers information from the internet, consolidates the data, asks questions over what it has learned, and repeats the cycle until detailed answers can be provided instead of a shallow first-pass summary.
Secure environment
Secure OpenClaw is meant to run these workflows in an Azure environment where identity, secret handling, persistence, and telemetry are part of the product boundary rather than an afterthought added later.
Technical implementation
Each customer environment gets its own Azure resource group, Container Apps runtime, Key Vault, storage, and telemetry path. The Technical page holds the architecture, security, and runtime details for technical buyers who want to inspect the operating model.
One resource group per environment.
Managed environment plus public gateway app.
Secrets and access are handled centrally in RBAC mode.
Pairing state and config survive revisions.
Logs and traces flow to Log Analytics and App Insights.
Optional Azure model service with privacy and residency kept explicit.
How we do it
The selling logic is simple: Secure OpenClaw is the operating substrate, not the whole story. We standardize the Azure operating model so the product conversation can stay focused on what the personal assistant and research agent actually need to do.
Repeatable delivery
We use a known Azure pattern so onboarding a client starts from a supportable baseline instead of from a bespoke infrastructure experiment.
Commercial position
GPU vendors and foundation model providers want to sell core infrastructure to IT. CraftingData sells workflow automation to the business teams who need domain-specific execution, control, and follow-through.
Representative delivery experience
Review a representative delivery example showing the kind of production-minded automation, reporting, and systems thinking that informs how we build supportable platforms.