Delivery OS: AI-Run Consultancy Platform Architecture
Kunde: Confidential — Enterprise Technology Client
Designed the end-to-end architecture for an AI-operated consultancy platform — a system where a client signs up, uploads their documents, and walks out with a proposal. If they sign, the platform plans the work, staffs it, runs it, and keeps them informed — with one ledger tying every line of the contract to the code that satisfied it.
6-week engagement
Platform architecture delivered
5 end-to-end
Engagement stages designed
< 4 hours
Time to proposal (post-build target)
2 mitigated upfront
Fatal risk categories addressed
4 phases
Roadmap phases with go/no-go criteria
Die Herausforderung
The client needed to transform a traditional consultancy into a software-operated business. No existing tool connected contractual commitments to the code that satisfied them: Jira does not know what was promised in the SOW; accounting systems do not know which commitment a timesheet burned against. That invisible gap is where consultancy margin quietly erodes. The client also needed a safe framework for AI-driven client communications — one that could automate routine updates without creating contractual or reputational exposure.
Die Lösung
Edwyz designed Delivery OS: a five-stage engagement platform (Intake to Commercial to Plan to Deliver to Operate) built around a single engagement ledger. Every entity — from source documents through requirements, commitments, work items, assignments, and artifacts — carries a traceable link to the one that caused it. This enables two queries no existing tool can answer: Did we ship what we sold? and Did we make money doing it? The architecture uses an explicit state machine with specialist AI agents inside each state, rather than a free-running agent loop. For client communications, a tiered autonomy framework was designed — agents earn the right to send messages autonomously based on measured performance history, with a hard ceiling ensuring that commitments, delays, and incidents are never automatable. A four-phase roadmap was delivered, each phase independently useful and generating the data the next phase needs.