What does agent-ready data mean?

Every field has a named owner, a plain-language definition, known freshness, and a log of who reads it. Then an agent can reason over the data instead of guessing, and every answer traces back to its source.

Do we need all our data in order before starting?

No. You need order in the data the first flow touches. A narrow flow with clean definitions beats a broad project with unclear fields. The rest gets structured as more flows go live.

What is the Diamond Layer, and why isn't bronze, silver, gold enough?

The medallion layers describe how raw data is technically refined. The Diamond Layer adds meaning: definitions, relationships, source references, and usage rules. That's the difference between data that is stored and data you can reason over.

How do you handle sensitive data during development?

We build and demo against synthetic data shaped like your production data but with no real personal information. Production data is only used in your own environment, with logged access.

Where does the data live, and who owns it?

In your own environment, your own tenant, your own pipeline. You own the data, the models, and the code we write. We hand over documentation, not a dependency.

How long does a data assessment take?

Three weeks is typical for a company with 50 to 500 employees: source inventory, ownership and definitions, quality measurement, and a prioritised sequence. The output is a decision, not a platform recommendation.

Data Intelligence · Foundation

When is your data ready for agents?

Data is agent-ready when every field has an owner, a plain-language definition, measurable freshness, and logged access. Without that layer, an agent inherits your data errors and scales them. The order is sources, ownership, definitions, quality, governance, then automation.

Test your data maturity → Diamond Layer

Last updated 2026-09-03. The test runs in your browser, no answer is stored.

Foundation · Diamond Layer

Your data is strata. We build the layer that reasons.

Bronze, silver and gold are standard. The Diamond Layer on top is semantic, source-referenced, and ready for agents to work over.

Square stack of data layers, cold cyan duotone. — Layers aren't a metaphor. They're architecture.

Four layers

From source systems to an agent-ready layer.

Layer 01
Step 01
Data migration and consolidation
From scattered systems and spreadsheets to one governed, traceable data estate. In your tenant, through your pipeline, with full lineage.
Outcome: one source of truth per field, and no agent reading a spreadsheet nobody owns.
→ Test your maturity
Layer 02
Step 02
Diamond Layer, agent-ready architecture
Bronze, silver and gold are standard. We build the layer above: semantic, governed, and defined well enough to reason over.
Outcome: the agent stops guessing what a field means.
→ How the Diamond Layer is built
Layer 03
Step 03
Data contracts and governance
Owner, definition, freshness, quality metric and limits, written per field before the flow is automated.
Outcome: an answer that holds up in an audit, not an explanation after the fact.
→ The data contract template
Layer 04
Step 04
Data science and synthetic data
Forecasting, classification and prioritisation with tracked metrics and versions. Development and demos against synthetic, not production, data.
Outcome: models you can trace and tests that never expose customer data.
→ How we handle data and models

Decision basis

What every seat in the leadership team needs to decide.

CEO
Question
Can we grow without growing headcount?
Decision
Data becomes its own workstream, not an appendix to an AI project.
Cost
A three-week assessment and a named internal owner.
Payoff
Decisions made on numbers that mean the same thing across the company.
Cost of waiting
Every agent built on unclear data becomes debt you pay off later.
CFO
Question
What does it cost, and what do we get back?
Decision
Cost per unit is measured before automation, not after.
Cost
Two weeks of measurement before anything is built.
Payoff
A calculation that holds up in the boardroom, before and after.
Cost of waiting
Without a baseline, impact can only be claimed, never proven.
COO
Question
Does quality hold as volume grows?
Decision
The quality definition is written before the flow is automated.
Cost
Half a day per flow with whoever actually reviews the output.
Payoff
Fewer manual rescues and a clear point where the flow stops itself.
Cost of waiting
Quality gaps get found by the customer instead of by the flow.
IT Director
Question
Can we defend this in an audit?
Decision
Lineage, logging, and access control are in from day one.
Cost
Work in your own environment, your own pipeline, no shadow platform.
Payoff
Traceability from source to decision, and an answer that holds up.
Cost of waiting
Governance added after the fact costs more and traces less.

Role	Question	Decision	Cost	Payoff	Cost of waiting
CEO	Can we grow without growing headcount?	Data becomes its own workstream, not an appendix to an AI project.	A three-week assessment and a named internal owner.	Decisions made on numbers that mean the same thing across the company.	Every agent built on unclear data becomes debt you pay off later.
CFO	What does it cost, and what do we get back?	Cost per unit is measured before automation, not after.	Two weeks of measurement before anything is built.	A calculation that holds up in the boardroom, before and after.	Without a baseline, impact can only be claimed, never proven.
COO	Does quality hold as volume grows?	The quality definition is written before the flow is automated.	Half a day per flow with whoever actually reviews the output.	Fewer manual rescues and a clear point where the flow stops itself.	Quality gaps get found by the customer instead of by the flow.
IT Director	Can we defend this in an audit?	Lineage, logging, and access control are in from day one.	Work in your own environment, your own pipeline, no shadow platform.	Traceability from source to decision, and an answer that holds up.	Governance added after the fact costs more and traces less.

A curved metal mesh catching warm light.

The foundation

The layers beneath the surface decide what the agent can answer.

Data

Architecture

The Diamond Layer on top of medallion.

Bronze, silver and gold are standard in modern data estates. The Diamond Layer is what we add: semantic, governed, and agent-ready.

Medallion is the standard. The diamond layer is what makes data agent-ready.

→ Layer by layer, and what breaks without them

The flow

From source to decision, no black boxes.

Every link is verifiable. You can trace a decision all the way back to the source data and the rule that made it.

Source systems
HRIS, ATS, payroll
Diamond Layer
Governed, traceable
Agent
Deterministic sketch
Decision
With evidence

The chain is not a black box. Every link can be verified.

→ The data contract that makes tracing possible

Proof · Pilot data

PILOT DATA

One

governed layer instead of scattered extracts

production data exposed in development

100%

lineage from source to agent

Figures come from our own pilot environments, not from a named customer delivery.

Questions

Frequently asked questions about agent-ready data

What does agent-ready data mean?: Every field has a named owner, a plain-language definition, known freshness, and a log of who reads it. Then an agent can reason over the data instead of guessing, and every answer traces back to its source.
Do we need all our data in order before starting?: No. You need order in the data the first flow touches. A narrow flow with clean definitions beats a broad project with unclear fields. The rest gets structured as more flows go live.
What is the Diamond Layer, and why isn't bronze, silver, gold enough?: The medallion layers describe how raw data is technically refined. The Diamond Layer adds meaning: definitions, relationships, source references, and usage rules. That's the difference between data that is stored and data you can reason over.
How do you handle sensitive data during development?: We build and demo against synthetic data shaped like your production data but with no real personal information. Production data is only used in your own environment, with logged access.
Where does the data live, and who owns it?: In your own environment, your own tenant, your own pipeline. You own the data, the models, and the code we write. We hand over documentation, not a dependency.
How long does a data assessment take?: Three weeks is typical for a company with 50 to 500 employees: source inventory, ownership and definitions, quality measurement, and a prioritised sequence. The output is a decision, not a platform recommendation.

Eight shortcuts into the library.

Buy it as a package

Data becomes answers in the Insight engine.

Once the data is in place, the Insight engine makes it queryable, month after month.

When the plan exists and nothing is live yet

Not sure where your data stands? Start with an assessment.

Three weeks, a named internal owner, and a decision in hand. Price range and scope are covered in the intro call.

→ Data Readiness Book a data assessment

Next step

Half an hour is enough to decide whether this is worth doing.

Book an intro call

A few lines about your situation is enough for us to answer.

See all packages

Two weeks, three months, or a build your team owns. Pick the pace.

Read about trust and governance

What is stored, for how long, and who may read it.

When is your data ready for agents?

Your data is strata. We build the layer that reasons.

From source systems to an agent-ready layer.

Data migration and consolidation

Diamond Layer, agent-ready architecture

Data contracts and governance

Data science and synthetic data