What metrics would you use to evaluate Thought Machine's smart contract engine within Vault?

Introduction

Evaluating the success of Thought Machine's smart contract engine within Vault requires a comprehensive approach to product metrics. This core component of Vault's banking platform demands careful consideration of technical performance, business impact, and user satisfaction. I'll follow a structured framework that covers core metrics, supporting indicators, and risk factors while considering all key stakeholders.

Step 1

Product Context

Thought Machine's smart contract engine is a critical component of their Vault core banking platform. It enables financial institutions to create and execute complex financial products and services using a flexible, code-based approach. Key stakeholders include:

1. Banks and financial institutions (primary customers)
2. End-users of banking services
3. Thought Machine's development and operations teams
4. Regulatory bodies

The user flow typically involves:

1. Product managers and developers at banks defining financial products using Thought Machine's proprietary language, Vault Product Language (VPL).
2. The smart contract engine interpreting and executing these definitions within the core banking system.
3. End-users interacting with these products through various banking channels.

This engine is central to Thought Machine's value proposition of providing a highly flexible, cloud-native core banking platform. It differentiates Vault from traditional core banking systems that often rely on rigid, predefined product structures.

In terms of product lifecycle, the smart contract engine is in the growth stage. While it's a mature component of Vault, there's ongoing development to expand its capabilities and optimize performance as Thought Machine scales its customer base.