A technical breakdown of how we use Payload CMS and the Vercel AI SDK to build AI-native FinSureTech applications at InnoPeak—from prompt management and structured outputs to embeddings, background AI workflows, and production-grade introspection for advisory tools.

At InnoPeak, we use a schema-first approach for Finly’s GraphQL API, relying on GQLGen to generate models, enums, and resolver stubs while keeping the focus on a clean, expressive graph. To cut down on duplication, we introduced a complementary code-first layer that generates model and enum definitions automatically—allowing us to concentrate on shaping the rest of the graph.

In Finly, we implemented real-time notifications using PGNotify as a lightweight Pub/Sub system. This powers our notification center, ensuring it updates automatically when new entries arrive and triggers a toast notification for immediate feedback. In this post, I’ll walk through how we set it up in Go and provided our client with a GraphQL subscription.

See how we improved our frontend Docker build times by 50% using the Drone Docker plugin and smart caching of dependencies and intermediate stages, making our CI/CD pipeline faster and more efficient.

At InnoPeak, we recently launched Finly’s new landing page, built with Next.js, PayloadCMS, and HeroUI. To ensure a smooth deployment, we leveraged Drone CI, Terraform, and Kubernetes, along with PostgreSQL and DigitalOcean Spaces for storage. In this post, we’ll walk through how we set up our infrastructure and automated deployment.