AI architecture
We map the real workflow, data flows, permissions, model and tool boundaries. Then we choose an integration and deployment approach that fits your operations and constraints.
OUTPUT / system map · design decisions · phased planWe design, build and secure AI for real operations. We find where manual work and missed signals cost you, test an AI observer alongside your team, then engineer only what proves useful.
We keep your operation in place. We first attach a read-only AI observer, test its work against reality and expand its role only where the evidence supports it. The diagram below follows one fictional production order through every stage.
An operator compares the order, machine event and instruction before release. The check takes time, and a missed signal can cause rework.
Nothing yet. We map the real handoff, its owner and the current cost.
How often the check happens, how long it takes and where mistakes or delays start.
Your team makes every operational decision.
We map the actual systems and permissions, define a baseline and choose a measurable decision. The diagram is a fictional example, not a client system.
One workflow, a defined test, a written evaluation and a go / improve / stop recommendation. Scope, schedule and price are agreed before access.
The pilot can end there. If it proves useful, we can architect and engineer deeper integration, with security review at every increase in authority.
Shadow Pilot is a way to begin, not the limit of our work. We can enter at architecture, engineering or an independent security review — and take responsibility for how the parts fit together.
We map the real workflow, data flows, permissions, model and tool boundaries. Then we choose an integration and deployment approach that fits your operations and constraints.
OUTPUT / system map · design decisions · phased planWe build connected assistants around real tasks, test ordinary and difficult cases, and add traces, approvals, fallback and operational monitoring.
OUTPUT / working system · evaluation · runbookWe inspect data exposure, prompt injection paths, identity and tool permissions, approval points, provider dependencies, logs and recovery — then retest the agreed fixes.
OUTPUT / evidence-linked findings · fix plan · retestWe are an emerging European team working across AI architecture, engineering and security. Our method connects models to the whole operating system around them: people, data, software, permissions, decisions and recovery. A project can start with a Shadow Pilot, an architecture design or an independent audit.
No. The observer has no write permission. Any later ability to propose or execute actions requires a separate design and approval.
We agree data flows, retention, provider access and deployment before connecting anything. Private, hybrid and hosted approaches depend on your requirements.
We record misses and false alarms against real outcomes. A failed evaluation keeps authority limited and tells us whether to improve or stop.
That depends on the workflow, access and test cases. We send a written scope, schedule and price before the pilot starts.
Tell us what repeats or goes wrong, roughly how often, and which systems your team checks. We will discuss the boundary, success measure and a written pilot scope.
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