AI Architecture & Implementation
Connect AI to your business data with clear definitions, controlled access, and a plan to operate it.
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Build Beyond the AI Demo
Cloud Data Consulting helps teams design and implement AI analytics on their existing data platforms. Our work connects Snowflake, business definitions, BI models, and assistant access into an architecture your team can understand and maintain.
Start with an architecture decision, a struggling pilot, or an implementation need. We define the data foundation, access boundaries, and evaluation process around the business questions you want to answer.
Where We Help
Readiness and use-case assessment
Identify the decisions AI should support, inspect the underlying data models, and agree what a useful answer looks like. Surface definition and ownership gaps before adding an AI interface.
Semantic architecture
Decide where business definitions belong across the warehouse, BI tools, and AI experience. Reconcile existing measures, define joins and calculation rules, and avoid maintaining competing versions of the same KPI.
AI infrastructure and access design
Design agent and MCP connections, identity boundaries, roles, environment separation, and compute controls. Document the flow of questions and results, including any external assistant services.
Implementation and integration
Build semantic views, verified queries, and the agreed agent or assistant integration in your environment. Adapt deployment patterns to your Snowflake account and existing delivery practices.
Evaluation and release discipline
Create representative question sets and test answer quality, ambiguous requests, and access boundaries. Establish a review process for changing definitions, model instructions, and integrations.
Operations and team enablement
Define who reviews failures, approves KPI changes, monitors usage, and owns releases. Provide runbooks and training so your team can operate and extend the implementation.
From Design to Delivery
Assess the starting point
Review your use case, data models, BI definitions, and identity requirements. You get a readiness assessment and a prioritized set of decisions.
Agree the architecture
Choose where definitions live, how tools connect, and who owns each boundary. You get a documented design, tradeoffs, and an implementation scope.
Implement and evaluate
Build the first use case and evaluate it against agreed questions. You get versioned configuration, an evaluation baseline, and an operating handoff.
We can join at the stage you need. If warehouse modeling is the blocker, we address it through dbt consulting before expanding the AI layer.
Why Cloud Data Consulting?
An AI answer can fail because of a metric definition, a data model, an access boundary, or agent behavior. We work across those layers, combining warehouse engineering with semantic-model design, verified queries, and evaluation.
Our delivery patterns come from hands-on Snowflake agent deployment and semantic architecture work. We adapt those patterns to your existing systems and document the decisions your team will need to own.
Choose Your Starting Point
Need a defined implementation package? Governed AI Analytics brings together agents, controlled MCP access, verified KPIs, and evaluation for an agreed business area.
Need ongoing ownership of priorities, investment, and delivery? Our Fractional Head of Data & AI service provides program leadership alongside the technical work.
Give Your AI Work a Clear Architecture
Bring your use case or current pilot. We’ll identify the next technical decision and the work it requires.
Schedule a discovery call