Pressure without foundations
AI features already promised to the business, on an architecture and a data layer nobody has checked.
Services
AI consulting and implementation services
G360 provides AI consulting and implementation services for enterprise teams. We assess whether your architecture, data and governance are ready, design how AI fits into your systems, and deploy generative AI on Azure, OpenAI and Microsoft 365, from the first use case to production.
Start a conversationThe problems that bring teams to us
Most teams come to us under pressure to ship AI before the architecture, data and governance are ready for it.
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
Our AI implementation services start with a readiness assessment and continue through delivery, with one team across data, architecture and engineering.
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.
Generative AI use case evaluation
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
Every enterprise AI implementation follows the same three phases, and each one leaves you with something to 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
What do your AI consulting services include?
An AI readiness assessment, data readiness work, architecture and integration design, platform selection across Azure, Microsoft 365, OpenAI and Claude, and use case prioritization. We then implement the first use cases with your team.
Do you only advise, or do you also build?
Both. Many AI consulting engagements end with a roadmap. Ours continue into implementation, so you leave with working AI and a foundation your team can build on.
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.
Do you help with Microsoft Copilot deployment?
Yes. We help teams prepare data, permissions and governance for Microsoft 365 Copilot, and connect Copilot and Azure OpenAI to the systems they already use.
How do you keep sensitive data safe in generative AI projects?
We set up security and access controls before any model touches your data. For live AI use, PromptVault replaces sensitive data with secure tokens before it reaches the model.
Ready to build what’s next?
Start with a readiness assessment and leave with a score, a roadmap, and the fixes worth doing first.