Your systems stay in place. Filiament connects their data, captures your business logic, and turns it into shared operational context, ready for people, applications, and AI.
A shared layer over the stack you already have.
Systems keep exchanging data in both directions, and the flows between them are routed through Filiament rather than wired to each other. The same layer serves everything built on top. AI agents are one consumer among several.
Your business already produces enormous amounts of data. But its meaning is scattered across ERP configurations, spreadsheets, transformation pipelines, reporting models, and people's heads.
Systems store records, they don't store context. They do not know how the business fits together, why two numbers differ, which rule applies, or what changed over time.
Filiament gathers that scattered logic into one place, governed and shared, where it is useful for today's operations and available later for whatever you build on top: AI agents, applications, BI.
Your context and business rules are specific to your company. We work them out with your teams, and the platform makes them run.
Your business runs on rules. Some live in ERP configurations. Some in spreadsheets. Some in transformation pipelines. Some only exist in people's heads.
Filiament captures those rules in a shared semantic layer: explicit, governed, and usable by every system, workflow, and agent.
Customers, products, invoices, entities, employees and opportunities aren't just records. Filiament keeps track of how they relate, which definitions apply, what changed, and what can be done with them.
Filiament does not replace the systems that run your business. It makes their data coherent, reusable, and actionable across the company.
Each system is connected once, through a maintained connector, without rebuilding the stack around it. It is then mapped onto the shared ontology: the core business objects and their relationships, modelled once with your teams, then reused across reporting, workflows and applications.
ExampleAn acquired company arrives with its own ERP. It is mapped to the shared ontology once, and every report and workflow picks it up.
Definitions, rules and history live in the layer instead of being scattered across tools, so systems can be compared continuously, financial and operational alike. Past states are preserved, differences are explained, and every change is traced back to its source, instead of the truth being rebuilt at month-end.
ExampleA margin figure changes after the close. Filiament shows what changed, where it came from, and how it propagated.
Detect changes, apply explicit rules, route exceptions and keep systems synchronized. Validated actions then reach the operational systems themselves, with a record of what was applied and by whom.
ExampleA customer record changes in the CRM. The rule that decides which systems must follow is explicit, applied, and traced.
Let AI work with structured definitions, relationships, history and permissions, rather than hallucinating across disconnected databases and documents. The same governed context serves your teams and your applications, so an agent never works from a different version of the business.
ExampleAn agent asked about revenue by customer gets the modelled definition of each, rather than whichever table it happened to find.
Nothing is automated before the context is explicit and governed.
Three mid-market groups, each with a problem that ran across several systems.
Corrections kept arriving after each close: a rebilled invoice, a reclassified cost, a late entry. Finance needed the figures as published, operations needed the ones the business runs on today, and the systems only ever kept the latest state. Every gap between the two was rebuilt by hand.
Now every state is kept, and any close can be reproduced at its date with each change traced back to its source.
Read the case →Two legacy ERPs on one side, seventy instances of an accounting system on the other. Invoices created in the ERPs did not always reach accounting, and some were never collected. Between sales administration and the subsidiaries, 50 to 60 people opened a ticket every time two records disagreed.
Now one shared ontology holds the reference between them, and writes the same records back into both sides.
Read the case →Collections up to 6 months out were forecast in 25 macro-driven files, filled in by hand. Each subsidiary matched invoices to projects by its own rule, and a single corrupted file could stop the financial steering of a subsidiary.
Now the matching rules are written down once, and a control application fed through the API has replaced the spreadsheets.
Read the case →Start with the one that crosses the most systems. We scope it with you, model only the context it needs, and get it running in production before going any further.
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