Cloudnonic designs, builds and runs AI agents and LLM integrations for companies that want a process automated end to end rather than a demo. We work on n8n and Dify for workflow and support agents, and in Python with LangChain, LangGraph and LlamaIndex when the agent is part of a product. Every agent ships with an evaluation set, guardrails, logging and a senior engineer who is accountable for it in production.
A prototype that worked on ten examples goes live without an evaluation set, and quality is judged by whoever complains first.
Silent errors in customer-facing answers, a rollback after the first incident, and a team that no longer trusts automation.
No agent goes to production without a test set from real cases and a quality threshold agreed with the business owner.
The agent is pointed at the decision that needs a person, while the repetitive data handling around it stays manual.
Wrong decisions at scale and no measurable time saved.
We map the process first and automate the deterministic steps completely. The model is used for the narrow judgment calls it is good at, with a handoff path.
The model provider changes behaviour, a connected API changes its schema, and the agent degrades with nobody watching.
Weeks of bad outputs before anyone notices, and a rebuild instead of a fix.
Every agent has logging, quality metrics and a named engineer on call, as part of the engagement, not as an add-on.
Most agent work starts as a fixed-scope first agent with a measurable target, then continues as part of a product-team or embedded engagement once it has proven itself. If you already have an engineering team, we build alongside it and leave the evaluation and operations setup in your hands.
An embedded team · What this prevents: A six-month hiring cycle for a skill you need this quarter, and a vendor that works outside your process and leaves no trace in your repos.
The two engagement models →Chosen per project. We pick what your team can run after us, not what is fashionable.
n8n
Dify
Python
LangChain
LangGraph
LlamaIndex
FastAPI
PostgreSQLPostgreSQLFastAPILlamaIndexLangGraphLangChainPythonDifyn8nFive shapes an agent takes in practice, with the problem each one solves and what it changes for the business.
Transform repetitive business processes into intelligent, self-executing workflows that connect your entire tech stack—delivering instant productivity gains and operational efficiency.
Deploy production-ready AI agents that understand your business context, make intelligent decisions, and integrate seamlessly with your existing enterprise infrastructure.
Purpose-built AI agents engineered specifically for your unique business requirements—delivering maximum ROI through tailored automation that grows with your company.
AI-powered customer service agents that handle complex inquiries, reduce support costs, and deliver personalized experiences that increase customer satisfaction and retention.
End-to-end intelligent automation that monitors, analyzes, and optimizes your business processes—delivering immediate cost savings and operational excellence.
Case studies in this scope. Every figure comes from the study behind it.
Python
React
AWSPythonReactAWS
n8n and Dify for workflow and support agents, because they give your team visibility and let you change a step without a deployment. For agents that are part of a product we build in Python with LangChain and LangGraph for orchestration and tool use, LlamaIndex for retrieval, and the model APIs of OpenAI, Anthropic or an EU-hosted provider directly, with our own evaluation code.
Yes. We can run retrieval and most processing inside your cloud account, limit what reaches the model to what the task needs, and use providers with EU data residency or self-hosted models where the data classification requires it. The data flow is documented before the build.
With an evaluation set built from real cases before launch and re-run on every change, plus production metrics on quality, latency and cost per run. The threshold for going live is agreed with the business owner, not set by us.
A first scoped agent with evaluation and operations typically takes four to eight weeks. Pricing is a fixed scope for that first agent and a monthly retainer if the work continues. Every engagement starts with scoping before a quote.
Every service on this page is delivered with our AI-Native process: specs as the source of truth, agents in the pipeline, and a named engineer signing off at staging.
How we build with AI →