AI delivery guide · Cloudpeakify
One workflow.
A useful answer. A controlled next step.
Your service team keeps searching the same documentation and rewriting the same explanations. An AI pilot should prove whether it can help that process—not how many tools it can connect.
The support workflow below is an illustrative pilot scope, not a claim about a deployed customer system.
Start where the hand-off is clear.
Choose one request type, one team and an approved knowledge collection. The assistant retrieves permitted material, drafts a response with source links and proposes a category or next task. A named reviewer accepts, edits or rejects the proposal. The pilot must never imply that a draft was sent or a proposed task was completed.
Grounding connects model output to source information and can reduce invented content. It does not remove the need for checking. Google Cloud: grounding overview.
A bounded implementation
Agree on these four deliverables.
01 · One end-to-end path
An authorised request, approved retrieval, a cited draft and a visible reviewer decision. Connect the existing service desk or CRM instead of creating a second system of record.
02 · A permission boundary
Allowed records, fields and operations; separate read and write capabilities. The backend validates model-proposed arguments and authorisation before any approved action.
03 · Repeatable evaluation
A versioned set of representative questions, expected evidence and failure cases. Review individual errors alongside agreed quality, response-time and cost measures.
04 · An operator handover
Known limitations, a named owner, access and retention decisions, cost assumptions, failure handling and a disable/rollback procedure. A pilot decision is separate from a production release.
Function calling returns a proposed function and arguments; the application handles execution. A model response is not authorisation. Google Cloud: function calling.
Bring the process owner—not only the documents.
The client supplies a reviewer, a permitted sample dataset, document owners, current response examples and an accessible test integration. Agree which roles may see which records, which information may leave the environment and how long test evidence may be retained.
Begin with synthetic or appropriately de-identified cases. Select cloud, private or hybrid placement from those constraints; do not treat a particular model or hosting choice as a guarantee of compliance.
Test the uncomfortable cases before the polished demo.
- A permitted request produces a draft with traceable supporting sources.
- A denied record remains unavailable even if a prompt asks for it.
- Missing or conflicting evidence produces a qualification or human hand-off.
- An injected instruction in a document cannot grant access or trigger a tool.
- A repeated event does not create duplicate tasks; a timeout remains visible.
- Every external action remains blocked until the agreed approval is recorded.
Agree the pass criteria before implementation and compare against the existing manual process. Google’s evaluation workflow likewise separates a use-case dataset, metrics, generated responses and inspection of the results. Google Cloud: evaluation overview.
These checks are a proposed acceptance contract, not achieved accuracy figures or evidence of customer savings.
From scope to pilot
Describe one repetitive process worth improving.
Cloudpeakify’s AI Agent Architecture Plan defines the workflow, data and tool map, placement, controls and evaluation plan. A pilot follows an agreed scope; production engineering and operation are separate stages.
Need the cloud foundation first? Read the Google Cloud decision guide or return to all guides.