Around the platform are forward deployed engineers who have spent their careers inside companies like yours: data platforms, ERP migrations, monthly closes, IT stacks carrying twenty years of decisions. That mix, data engineering, business, finance and the real operational stack in the same people, is rare, and it is exactly what these deployments ask for. They work alongside your teams, or in their place.
Full deployments take a bit of time. So we start on a narrow scope, get it running in production, and widen only then. Nothing is rebuilt along the way: every stage runs on what the one before it put in place.
You have a clear, critical data problem, or you want your data in order so that it becomes usable by AI. We scope that first perimeter with you and deploy Filiament on it: the systems involved, and the ontology and rules that let them agree. The point of this stage is to crack the problem, and to leave the foundation for the next one.
Once the case is deployed we check that it holds in production, then take it up to full load. Where the same systems exist in several instances, we extend to them one after another, until the whole perimeter runs on the layer rather than a pilot corner of it.
We connect further systems to Filiament, one at a time. Which ones, in which order, and for which outcome and which teams, is decided together as we go, and that arbitration is part of what we bring. Each connection makes the company's data more coherent, and brings the ground closer to the point where agents can work on it.
An ERP replacement, a new subsidiary, a build-up: those come back as projects. But they start from the connectors, the ontology and the rules that are already there, so they go faster and cost substantially less than the first one did. We govern the project, Filiament governs the data, and the data layer stays maintained. That is our commitment.
We work with your teams, or with the providers you already use, so that AI can plug into Filiament: read the governed data from outside, work with it inside the layer, and write results back into the operational systems connected to it. By that point the context is already modelled, governed and traceable, which is what makes it safe to hand over.
The same engineers work on all three. The first two come with any deployment: the modelling upstream, then the build and the run. The third goes further, where we take the data subject as a whole and bring the roadmap instead of waiting for it.
Map the existing stack, data flows, definitions, business rules, ownership, and operational pain points. Define the minimum ontology and a deployment scope tied to an immediate business outcome.
This work can open with an audit, short or longer depending on how much of the stack has to be mapped, or start straight at the modelling when the ground is already known.
Connect the systems, build and maintain the semantic model, implement the workflows and controls, and support adoption, without burying your data team or requiring you to build one.
Then we stay. Connectors are maintained as source systems change, transformations are adjusted as issues surface, and the scope grows with the company: new instances, new entities, new systems.
For companies that want a standing data office without building one, the same team sits inside your IT organisation and takes the data subject as a whole: Filiament, but also the data warehouse, the BI layer, group-wide governance, and the next use cases.
Proactive rather than on request: we bring the roadmap, arbitrate it with you, and answer for it.
We pick a problem that crosses a few systems, model only the context it needs, and leave something the next scope can build on.
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