Data products for the agentic era
Forge data products as containers of context.
AI decides at machine speed. It needs context. FLUID Forge compiles your data products — open source, governed, agent-native.
Field guides
Four briefings on agentic-ready data.
Short, plain-spoken papers on data products and the FLUID standard — written for the boardroom and the engineering team alike.
The shape of the problem
Four beats. One conclusion.
FAST.
Decisions in the AI Era happen at machine speed.
CONTEXT.
AI can keep up — if it has context.
PRODUCTS.
Context lives in data products. Self-contained. Governed.
FORGE.
FLUID Forge — the open-source compiler.
The signature tool
Map your Periodic Table.
Every industry has a finite set of data products it needs. We've mapped them. Five minutes. No login.
Proof
Runs everywhere. For every industry.
One FLUID contract compiles native to every major cloud — and the data-product pattern maps cleanly onto every sector.
How we work
Four phases. One working day to first prototype.
- 01
Before
Walk in with your sector's map.
- 02
Workshop
First prototype in a day.
- 03
Build
Parallel. No big-bang risk.
- 04
Turnkey
Teams own the methodology.
Who's behind this
We built the standard.
Founded on one observation: agentic AI would stall — not because the AI wasn't ready, but because the data wasn't.
Founder · Standard architect
Jeff Watson
Practitioner of enterprise data architecture across telco and public sector. Author of FLUID Enterprises. Currently building the standard the agentic era runs on.
- Open standard. No transformation that depends on lock-in.
- Methodology + tech. Architecture without ownership fails.
- Speed without shortcuts. Parallel makes months out of years.
- Diagnostic first. 30 minutes. If we can't help, we say so.
FAQ
Data products, answered.
What is a data product?
A data product is a self-contained, governed unit of data with a defined owner, a contract, quality guarantees and documentation — built to be discovered and consumed without asking the team that produced it. It treats data as a product, not a byproduct.
Why do AI agents need data products?
Autonomous AI agents act at machine speed and can't pause to ask a human what a field means or whether a number can be trusted. Data products give agents the context — schema, semantics, lineage, freshness and policy — they need to act safely and correctly.
What does Agentics Transformation do?
We help enterprises turn raw, scattered data into governed, agent-ready data products — using the open FLUID standard and the forge-cli compiler, plus a hands-on methodology that gets a first prototype live in a single working day.
How is this different from a data warehouse or a data lake?
A warehouse or lake stores data; neither makes any single dataset a trustworthy, owned, contract-bound product. Data products sit on top of the platform you already run and add the ownership, contracts and discoverability that agents and analysts actually need.
How quickly can we see results?
The engagement is built around speed: a 30-minute diagnostic call, then a workshop that produces a first working data-product prototype in a single day — with your team owning the methodology by the end.
Now is the moment
The window is open. Not for long.
AI capability is commoditising. Data architecture is the moat.