xflow

Introduction                                     

xflow makes governed metadata operational, so every data product is context-enabled from day one and every AI agent has business meaning it can act on.

Website (xflow)       Video Demo (Link)

Collibra governs the meaning of enterprise data. xflow converts that governed meaning into AI-ready executable context. The combined Collibra plus xflow stack delivers semantic models and AI workflows that are traceable, explainable, and trustworthy by design, from the moment data products are published.

We are the first platform on the Collibra Marketplace purpose-built to make governed metadata operational as AI context. Not a catalogue, not an AI governance tool. The context layer that activates your investment in Collibra for the agentic era.

Why This Matters Now

Enterprises have built deep data governance estates. Definitions, lineage, policies, ownership: all curated, all catalogued, almost all of it locked inside the governance tool. That investment was designed for human oversight. It was never designed to be consumed by machines.

AI changes the assumption. Unlike people, AI agents cannot tap a colleague on the shoulder, read between the lines of a policy, or infer what a field means from the name of the report it appears in. They execute against whatever context they are given, and nothing more. In regulated financial services this hits hardest. Every major regime, BCBS 239, Solvency II, IFRS 17, FINREP, COREP, assumes a human interpreter in the chain. AI removes the interpreter without removing the regulatory obligation.

The bottleneck for enterprise AI is context, not compute, and not the model. Collibra already holds the raw material. xflow is the platform that turns it into context AI can consume, at runtime, under governance, with an audit trail.

How xflow Unlocks Value from Collibra

1. Deliver ROI with Data Products

Today, a data product in Collibra is typically defined, documented, catalogued, and then consumed somewhere else. The consumer, whether a human analyst, a reporting process, or an AI agent, has to reconstruct the meaning, rules, and lineage outside the governance tool. The data product is discoverable. It is not operational. Consumers rebuild the logic, and the ROI on the data product stalls at the catalogue boundary.

xflow makes data products context-enabled from the point of publication, so the ROI on every data product compounds rather than leaks:

• Executable context attached to the data product. Every data product carries its business logic, transformation rules, policy constraints, and cell-level lineage as structured, machine-readable context. Consumers do not reinterpret, they inherit.
• The digital twin as the data product. xflow builds an executable model of how the data product is produced, not just a description of what it contains. Consumers can simulate, validate, pre-check a transformation before it runs, and post-hoc prove it ran as governed.
• Semantics that execute, not just document. Collibra’s business glossary and semantic model tell you what a term means, who owns it, and where it is governed. xflow turns those governed semantics into a digital twin consumed by agentic AI at runtime.
• Audit-ready evidence as a byproduct. Regulators, internal audit, and risk committees get evidence on demand. The governance workflow and the business workflow become one track, not two. Every governed asset generates traceable downstream value.

The data products you publish from xflow to Collibra become the data products AI can act on, not just read about, and the ROI case on data products finally shows up in production.

2. Operationalize AI Context

• AI agents call xflow for governed calculations, rules, and lineage, not unstructured prompts or tribal knowledge. Every AI decision is grounded in the same governed context the business owns and maintains in Collibra.
• Context governance is built in. Every context asset xflow produces is versioned, traceable, auditable, and governed with the same lifecycle controls as the underlying metadata. Context itself becomes a managed, compliant artefact, which is what the next wave of AI regulation (EU AI Act, SR 11-7 model governance, algorithmic transparency requirements) will demand.
• Native fit with MCP and agent protocols. xflow serves execution-grade context over the emerging agent standard, so any Collibra customer’s AI stack can consume governed context without rebuilding their agent layer.
• Human-AI collaboration by design. Business users, data teams, and AI agents work on the same governed context. Humans contribute the interpretation only they carry. AI contributes speed and consistency. Every output is traceable to the governed source.

Collibra governs the meaning. xflow delivers the meaning to AI, trustworthy, traceable, and live, so AI ambition converts into AI value in production.

3. Comply with Regulations

Regulated financial services is where xflow proves itself first. Every major regime, BCBS 239, Solvency II, IFRS 17, FINREP, and COREP, assumes a human interpreter in the chain. AI removes the interpreter without removing the obligation. The governance investment that Collibra customers have already made is the raw material for compliance, but it was never designed to be consumed by a machine, or to generate evidence on demand.

xflow activates Collibra’s governed metadata as the compliance control plane:

• Regulatory digital twin. An executable model of how a regulatory return, risk calculation, or finance close actually runs, built on Collibra’s governed definitions, lineage, and policies. Query any cell, explain any movement, pre-validate a transformation before it runs, prove post-hoc it ran as governed.
• Cell-level lineage at scale. FINREP and COREP across 3,500+ data points, traced back to source without parallel validation packs. 70% faster supervisory query response.
• Evidence packs on demand. BCBS 239 credit risk aggregation, Solvency II QRT reconciliation across 60+ templates, IFRS 17 actuarial-to-finance reconciliation. Every output traceable to the governed source. Explainability is produced as a byproduct of execution, not reconstructed under audit pressure.
• Policy-aware AI under regulation. Governed context grounds risk models, credit decisioning, claims AI, and compliance agents. Direct fit with SR 11-7, EU AI Act Article 13 (transparency), and the evolving algorithmic accountability regimes.

Collibra holds the policy, lineage, and definitions. xflow turns them into the evidence, simulation, and runtime control regulators expect, from the same governed source.