We assessed all existing systems, APIs, and integration points, reviewed the existing security and governance posture, and evaluated the AI platform market against the client's specific constraints. From that, we identified ~40 potential AI use cases and produced a full technical architecture recommendation. Commercial all-in-one platforms were explicitly evaluated and rejected for: cost, vendor lock-in, model selection restrictions, data sovereignty concerns, and inflexibility. The open-source choice was the right technical answer, but it required Kablamo to do something that off-the-shelf products don't: build the governance layer properly. In the 9-week build phase, the platform was built on AWS, with Langflow at the core. Kablamo equipped the open-source AI platform with an enterprise-grade governance layer, which includes a secure GitOps promotion pipeline, federated access, and full LLM observability for monitoring cost and usage. This architecture also ensures security through audit logging and controlled integration patterns that prevent non-engineers from accidentally exposing sensitive data. The result was an agentic AI platform that has the speed and accessibility of a low-code tool, and the security posture of an enterprise system. Kablamo pre-built four reusable system integrations (Zoho CRM, Zoho Desk, Amazon Connect, Admin System) and a production ready automation use case - Call Support Reason and Summarization. This use case was deliberately chosen because it creates a strategic, low-risk data baseline on customer call reasons while immediately reducing manual agent effort. It also had a natural roadmap, where the same infrastructure unlocks call QA, live agent assist, and email triage - a clear path for the process team to continue to deliver ROI.