COMMAND CENTER Streamlining Commerce with Shopify Migration and UX
Custom AI agents deployed
Time saved per workflow
Faster time to completion
Client satisfaction score
Faster project delivery
Reduction in AI operating costos YoY
Months in continuous internal operation
- THE CHALLENGE
Two years ago, our teams were running six different AI providers across isolated workflows. Project managers generated deliverables from OpenAI in one format while another team produced the same work from Claude in a completely different format. Lifecycle marketing ran Klaviyo automations through one pipeline and Bloomreach campaigns through another. Sales built agents nobody else could see. Every department was moving fast. Nobody was moving together.
The output was inconsistent. The governance was nonexistent. Sensitive client data was flowing through consumer-grade tools with no data protection guarantees. We tried buying our way out. We adopted every AI feature from every platform we used. Within months, the result was clear: a fragmented disaster. Each tool's AI worked in isolation. None of it talked to the rest. The build-vs-buy question answered itself. Buying AI tools is the easy part. Making them work together as a governed, organization-wide system is the hard part.
What We Built
Command Center is the AI operating platform we built for ourselves. Not as a product for market. We built it because we needed it. Every architectural decision was driven by an internal problem, not a feature someone thought would sell well.
The platform provides multi-model AI access (Gemini Pro, Gemini Flash, Claude Sonnet, and others) through a single governed interface running on Google Cloud enterprise infrastructure with Vertex AI. It connects to the systems our teams already live in through MCP integrations: Jira, Confluence, Klaviyo, Bloomreach, HubSpot, and Google Analytics. Knowledge bases built on RAG architecture through Llama Index Cloud allow agents to draw on document collections, call transcripts, and client data. Looker Enterprise dashboards are embedded directly in the platform, with data replicating into BigQuery. Role-based access control through Auth0 governs who sees what. The platform runs on Google's ADK framework with A2A protocol connectivity to Gemini Enterprise.
The most important capability: any teammate, regardless of technical background, can build and deploy custom AI agents. This moved the bottleneck away from engineering and put AI directly in the hands of the people closest to the work.
What 80 Agents Taught Us
Over eighteen months, our team built and deployed more than 80 custom agents. Sales Ninja became the institutional knowledge layer for the entire organization, loaded with our service taxonomy, case studies, client history, and competitive positioning. Anyone can ask it who we are, what we do, and how we approach specific challenges. Brand voice agents built for specific clients generate on-brand copy across lifecycle marketing workflows. A project management agent connected to Jira lets PMs create and flesh out tickets using project documentation without switching tools. An internal knowledge agent answers routine operational questions from Confluence, reducing the load on leadership.
Deploying the technology was the straightforward part. Getting 100+ people to change how they work was the real project. Adoption was uneven. Some teammates went back to ChatGPT because the habit was already formed. We ran structured enablement sessions, assigned champions within each practice area, and tracked adoption metrics. We also learned what happens when AI adoption moves faster than AI literacy: some team members generated work from call transcripts without reviewing the output, producing results disconnected from the actual documentation. We built quality gates into every workflow. The AI produces a draft. The human reviews and validates. The output goes to the client.
The lesson we carry into every engagement: the technology is a prerequisite. The transformation is an organizational project.
project summary
Our team reclaims a quarter of their time on every AI-integrated workflow. Project delivery accelerated by 22%. Strategy documents once taking a week now take days. Ticket creation once consuming an afternoon happens in minutes. Client satisfaction sits at 98% because the humans directing the work have more time to think, review, and refine. AI operating costs dropped 40% year over year even as token consumption doubled. And Command Center outperforms Gemini Enterprise on identical models for our use cases, because our architecture injects relevant documents and context directly into the prompt. Architecture matters more than model selection.
