Product decisions, architecture walkthroughs, and regulatory analysis, without marketing speak. Written by the team building Opendome.
Plenty of people use MCP every day without knowing what it is. What the Model Context Protocol is, how it works under the hood, its advantages and limitations, and the best practices you need to use it without losing control of your data.
Giving AI chat to every employee saves each person a few minutes, but that time does not turn into company results. The return appears when AI works on processes, which means redesigning them, measuring them and giving agents the business data under control.
"The agent has guardrails, so the data is protected." That sentence conflates two things operating on different planes. Guardrails watch what the agent says; they don't govern what it accesses. Only structural, reproducible access control protects the data — because it isn't statistical: it's deterministic.
With agents that hold no seat and open no screens, per-user licenses stop making sense. Salesforce, SAP, and Microsoft are shifting to consumption pricing: charging for every operation an agent runs against the data. The problem is that you generated that data, and now operating on it costs.
Business memory already exists: it lives in the data. The only memory that should belong to the agent is procedural.
The proprietary software a company uses every day is itself built, on the inside, on open source. The hyperscalers build their products on open source. It isn't the risky option — it's the most solid, most audited foundation for a sovereign data infrastructure.
There's no enterprise AI strategy without centralizing, relating, describing, and protecting the data. They aren't four good ideas to choose among. They're four conditions, and missing one invalidates the rest.
Connecting an agent to too many MCPs doesn't make it more capable: it makes it more expensive and less reliable. Half its capacity goes to loading tools, it chooses worse the more it has, and what we call "hallucinations" is almost always a data gap filled with inference. The key isn't more access: it's better data.
Deploying AI in your company raises four questions: how do you control who accesses which data? How much will you spend on tokens? How do you keep hallucinations out? Where does your data end up? This whitepaper sets out a model for deploying AI safely, designed for the European regulatory framework, with sovereignty over your data and governance of every access.
Download the whitepaperPlenty of people use MCP every day without knowing what it is. What the Model Context Protocol is, how it works under the hood, its advantages and limitations, and the best practices you need to use it without losing control of your data.
AI agentsGiving AI chat to every employee saves each person a few minutes, but that time does not turn into company results. The return appears when AI works on processes, which means redesigning them, measuring them and giving agents the business data under control.
AI agents"The agent has guardrails, so the data is protected." That sentence conflates two things operating on different planes. Guardrails watch what the agent says; they don't govern what it accesses. Only structural, reproducible access control protects the data — because it isn't statistical: it's deterministic.
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