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Version: 3.1.0

Architecture

A Subtle but Important Distinction​

Not all data platforms fail for the same reasons. The difference is rarely about capability alone — it is about where responsibility for complexity sits.

Side-by-side comparison of quadrant-led platforms vs outcome-led architectures

In many modern architectures, complexity is pushed onto the organisation:

  • Data meaning is resolved downstream
  • Governance is enforced after ingestion
  • Quality is monitored, not guaranteed
  • Cost control relies on discipline, not design
  • AI stability depends on constant retraining

These platforms are powerful, but they assume a level of architectural maturity, operational rigour, and ongoing engineering investment that most organisations underestimate.

By contrast, an emerging class of architectures takes the opposite stance:

  • Meaning is stabilised before data is exposed
  • Governance is embedded directly into the data fabric
  • Only curated, decision-ready data is stored analytically
  • Costs are constrained structurally, not operationally
  • AI models remain stable as source systems evolve

The distinction is subtle, but the business impact is not.

In the first model, success depends on continuous effort.
In the second, success depends on architectural intent.

Why This Matters to Architects​

Architects are often asked to justify decisions in terms of:

  • Market leadership
  • Feature completeness
  • Ecosystem maturity

But the business ultimately judges architecture by different measures:

  • How fast insight is delivered
  • How predictable costs remain over time
  • How resilient AI initiatives are to change
  • How easily the organisation adapts without re-engineering

Architectures that internalise complexity may look simpler to buy — but they are harder to live with.

Architectures that absorb complexity by design tend to look quieter in the market, but louder in results.

The Counter-Quadrant Question​

Rather than asking:

“Which platform leads the market?”

A more useful question is:

Where does complexity live — and who pays for it over time?

That question rarely appears in analyst graphics.
It appears very clearly in operating costs, delivery speed, and AI outcomes.

Solution Architecture​

This video gives you an overview of the CryspIQ® solution architecture and how things hang together.

Data Model​

This video gives you an overview of the CryspIQ® Data Model and how it works.

Functional Flows​

The diagram below provides the functional flows for the end to end concepts of the CryspIQ® application.

Functional Flows

Azure Components​

The diagram below provides the key Azure components that are required by CryspIQ® for your own business's instance.

Azure Components

This provides and indicative understanding of what azure components you will need and what azure costs could be incurred.

Co-existence​

Using your existing Raw or Staging layer, you can map the required Data from the existing message structures into the CryspIQ® Data model. This enables you to leverage the flexibility of Data model, measure Data Quality and use Natural Language to access your Data. This will provide immediate productivity benefits to your business and enable you to reduce costs.

This provides a visual picture of how CryspIQ® could co-exist alongside your existing Data Platform:

Co-existence