Smart Mobile House designs, builds and deploys AI agents and secure production AI systems for enterprise organizations. Our agents connect to your existing systems through approved tools, with human approval steps, least-privilege access, audit logging and evaluation before and after release. The same engineering applies to everything we deliver: cloud architectures on Azure, AWS, and Google Cloud with identity, private networking, RAG ingestion, and observability engineered in from the start. Every engagement ends with a working system and the runbooks your team needs to operate it.

Our core services:

• AI Agent Development — custom AI agents taken from an agreed use case through architecture, integration, testing, deployment and monitoring
• AI Agent Consulting — use-case assessment, feasibility review with a small prototype, build-versus-buy advice, architecture and governance review, and a phased roadmap
• AI Implementation Services — end-to-end cloud AI architectures, deployed into your environment and handed over with documentation and runbooks
• Enterprise RAG Implementation — retrieval pipelines built on your data, with evaluation suites and audit trails so you can trust what the system returns
• AI Benchmarking — measuring agents and models against your own workloads, so decisions rest on evidence from real use rather than vendor benchmarks
• AI Adoption, AI Automation, and AI Consultation — helping teams move from pilots to production with clear scope and measurable outcomes

Security and governance run through all of this work. Because many of our clients operate in regulated industries, access control, data boundaries, and auditability are designed into the architecture from day one. We build in your cloud accounts and your repositories. AI agent projects are delivered under a licence, with terms set in the contract.

The company was founded in 2012. Since then, our engineering team has delivered software for financial services, manufacturing, and telecommunications enterprises across Europe and globally, working mostly with mid-market and enterprise clients. That delivery history shapes how we approach AI today: the same discipline around security, documentation, and measurable results, applied to a newer class of systems.

We also publish our engineering work as open source: azure-governed-rag, a governed RAG reference on Azure with approval gates and audit logging; Fraud Decision Lab, an open benchmark of AI decision models against XGBoost for fraud triage; and two MCP servers for Polish public registries, regon-mcp and vat-whitelist-mcp.

If you need AI agents or AI systems running in production — behind your identity provider, inside your network, with evidence that they work — we would be glad to build them with you.

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