Pressure without foundations
AI features already promised to the business, on an architecture and a data layer nobody has checked.
Services
AI Implementation
Readiness, architecture, and delivery for enterprise AI: the foundations proven before the build, the use cases chosen on evidence, and the first ones running inside the systems you already operate.
Start a conversationThe problems that bring teams to us
Mid-market and growth-stage teams under real pressure to ship AI, without a cohesive architecture, connected data, or governance to ship it on.
Systems that never meet
Fragmented tools, legacy infrastructure, and data siloed in the places it was first written.
No one to validate the plan
Investment decisions being made without the in-house experience to say whether the roadmap holds.
Five capabilities, one AI foundation
A structured readiness assessment, and the implementation support to act on what it finds — one team across data, architecture, and delivery.
AI readiness assessment
A structured review of architecture, data maturity, and governance, scored so you know where you actually stand before committing budget.
Data readiness
Pipelines consolidated, sources centralized, and the security and access model settled before a model is pointed at any of it.
Architecture & integration design
The target architecture drawn, integration points named, and a path to it from the systems running today.
AI platform & Microsoft alignment
Azure, Microsoft 365, OpenAI, and Claude capabilities matched to the work in front of you, so you buy what you will actually use.
Use case evaluation & mapping
Candidate use cases scored on value and feasibility, then ordered into a backlog your engineers can pick up.
Where the products plug in
PromptVault governs what the AI is allowed to see once it is live; RepoAudit keeps the code it writes under review. The foundation and the guardrails go in together.
How an engagement runs
Three phases, each leaving you something you keep.
- 01
Discovery
Architecture, data maturity, and governance assessed against the AI work you actually want to do.
- 02
Blueprint
A sequenced roadmap, plus the short list of high-leverage fixes worth doing immediately.
- 03
Activate
Quick wins deployed and a base your team can keep building on without us.
Accelerators and frameworks we bring
Ingestion & pipeline playbooks
Proven patterns for getting data in and scaling it, already run in environments like yours.
Azure reference architectures
Published configurations adapted to your estate rather than drawn from a blank page.
G360 AI Readiness Toolkit
The assessment instrument itself: scoring model, evidence checklist, and the scorecard you keep.
How we measure success
The measures we agree on at the start, and report against at the end.
A validated AI readiness score
A technical backlog aligned to real use cases
Data pipelines that are reliable and observable
Stakeholders clear on the next AI feature
“They’ve helped us move at our own pace, which has benefited us.”
Frequently asked questions
Do we need our data in order before we start?
No. Data readiness is part of the assessment, and the pipeline, centralization and access work is part of the delivery that follows.
Can this start with the assessment only?
Yes. The readiness assessment stands on its own, and the score, the gaps and the roadmap are yours either way.
Ready to build what’s next?
Start with a readiness assessment and leave with a score, a roadmap, and the fixes worth doing first.