Methodology

Three phases.
One decision each.

Each phase ends in a decision you control: the value is proven before anything gets built, and what is built is proven before it scales.

001

Problem

Set direction.

StrategyBusiness viability
  • Business case for the platform: implementation costs, expected gains and running costs.
  • Use cases refined and prioritised, with build, buy or reuse decided per component.
  • Insourcing versus outsourcing assessed case by case.
Application layerUser desirability
  • Interviews with the people doing the work today: where the manual hours sit, and which exceptions matter.
  • Target operating model defined: what you own and run, including IP ownership.
Data layerTechnical feasibility
  • Target architecture that connects the relevant source systems.
  • Technology, integration and capability requirements assessed; the central assumptions validated.
After the sprint, you have
  • The AI platform blueprint on one page: the architecture drawing that works in a board room.
  • A component map with a clear build, buy or reuse call per component.
  • An integration approach for your source systems, including what stays standardised.
  • Two to three prioritised use cases with preliminary business cases.
  • The operating model: what you own and run, and what Heyra or vendors carry.
  • A three-year roadmap tied into your system landscape, and a fixed price for the prototype phase.
Which enables
  • An investment case to the board, with the numbers and the method behind them.
  • The insource versus outsource question settled before any capital is committed.
  • A go or no-go on the prototype with everything on the table: a no-go still leaves the blueprint, roadmap and business case with you.
Outcome

A validated design: architecture, operating model and business case, with a committed prototype price. A go or no-go decision you control.

002

Prototype

Make it real.

Adoption
  • Review workflows with a human in the loop, shaped with the users from week one.
  • A shared UX definition that later use cases inherit, with knowledge transfer running throughout.
Application layer
  • Platform foundation: orchestration, data and document ingestion, monitoring, audit and governance.
  • The first use case built on top of it, running on live data.
Data layer
  • Integration with the real source and operational systems, including write-back, feeding master data and analytics.
  • Governance, security and operating processes established in your own cloud environment.
Outcome

A working platform plus the first use case running on your own data, and a scaling decision made on evidence.

003

Production

Make it solid.

Adoption
  • Rollout across teams and regions. Your team runs the platform and leads new use cases, with Heyra on call rather than in the loop.
Application layer
  • New use cases need only their own domain rules and integrations.
  • Platform improvements only where a use case demands them.
Data layer
  • Hardening in daily operation: uptime, monitoring, security and audit.
Outcome

Use cases shipping on a platform you own and run, with the cost of each next case falling.

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Contact

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you operate.

Tell us where you’re stuck. If operational AI can change the outcome we’ll show you how, and if it can’t, we’ll say so.

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