Delomu

AI startups win enterprise customers, then spend weeks of engineer time per rollout: reading the customer's policies, wiring up their systems, writing tests from the customer's history, and arguing about whether the AI is ready. One wrong answer about a refund or a data deletion costs real money and trust. Delomu is an AI forward-deployed engineer. Its headline feature is the risk scan. You upload the customer's own policies; Muse Spark reads them and lists the rules a support AI must keep, each with the exact clause and what breaks if it is not kept. Then it writes the customer messages most likely to make an AI break each rule: day 31 of a 30-day refund window, "my manager already approved it", a deletion request the AI has no tool for. Delomu sends every message to the customer's AI, and Muse Spark judges each answer against the clause and proposes the smallest fix. On the demo bot, one scan took under a minute and found 4 of 8 policy rules broken, including a cash refund promised on a 35-day-old order. Around the scan sits the rest of a rollout: the agent drafts the data mapping, the integration, a test set from past tickets, grouped fixes and a launch report. A person approves, edits or rejects every draft, and every action goes into a hash-chained audit log. Tests re-run when the customer's system or model changes, and the customer gets a readable acceptance report.