AI & Workflowsshare, don't depend.
Productive AI doesn't live inside a demo. It settles into the way teams work. Our role: share with your teams the workflows we've proven in production, so they become autonomous, on the agent, on the model, on the method.
Productivity, autonomy, independence. We share with your teams the AI agent workflows and open-source models we use in production to multiply development, design and architecture across your projects.
Three disciplines, one integrated team.
AI agent onboarding
We share with your teams the AI agent workflows we use every day, engineering, design, modelling, architecture. The goal isn't to adopt a tool, it's to adopt a method: framing intent clearly, reading the output, correcting the trajectory. Whatever the most sophisticated LLM sits behind it, productivity comes from the harness and the method.
- Onboarding on an autonomous agent tool (Claude Code, Codex)
- Prompting and iteration methods
- Productive team workflows (engineering, design, architecture)
- Impact measurement on project velocity
Open-source models
Productive AI shouldn't depend on a single vendor. We share with your teams how we evaluate, select and deploy open-source models, Llama, Mistral and their variants, on your own infrastructure. Independence from large vendors, cost control at scale, compliance with data constraints in regulated sectors.
- Open-source model evaluation and selection
- Llama, Mistral, fit-for-purpose
- Local or sovereign-infrastructure deployment
- Vendor independence and cost control
Mapping agent context
An AI agent's harness is only as good as its context. Our approach: structure a context file that works as a map, not a manual. A short entry point, clean references to specialised docs, a layout organised by recurring tasks. A principle echoed by AI leaders: it's better to give an agent a map than a thousand-page instruction manual.
- Context blueprint tailored to your codebase
- Map of conventions and entry points
- References to specialised docs (≠ everything-in-one)
- Continuous iteration on the harness
Frequently asked questions
What kinds of AI workflows does Movira design?
The studio designs workflows to automate repetitive business tasks (document classification, data extraction, response generation), internal conversational assistants and recommendation systems. The technology is chosen by need, among Claude, GPT or open-source models.
Does Movira integrate open-source models?
Yes. The studio evaluates and integrates open-source models such as Llama, Mistral or Qwen when privacy, cost or latency constraints justify it. Deployment can be self-hosted or via specialised providers.
How are AI workflows secured?
Movira applies the principle of least privilege on model access, isolates sensitive data, and sets up application-level guardrails (output validation, auditable logs). Critical prompts are versioned alongside the code.
Can the studio connect AI to existing business tools?
Yes. Movira integrates AI models with client business tools (CRM, ERP, internal knowledge base) via APIs or via standard protocols such as the Model Context Protocol. The goal is to surface AI inside the tools teams already use.
Are AI workflows measured in production?
Yes. Every deployment includes performance metrics (usage rate, time saved, user satisfaction) and quality metrics (hallucination rate, error rate). The data feeds the next iterations.
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