A coordinated stack — not a bundle of tools. Each layer feeds the next. Signals informs Maestro; Maestro directs Shipyard; Shipyard's outcomes flow back into Signals. The loop is closed.
The Thrasoz Platform is a coordinated stack — not a bundle of tools. Each layer feeds the next. Signals informs Maestro; Maestro directs Shipyard; Shipyard's outcomes flow back into Signals. The loop is closed.
Ingests 15+ live data sources — Jira, GitHub, Harvest, BambooHR, Slack, Confluence, QuickBooks, New Relic, PagerDuty, AWS Cost Explorer, and more. Computes velocity scores, anomaly alerts, yield diagnostics, and engineer-level productivity signals. This is the score the conductor reads before raising the baton.
The enterprise control plane. Reads Signals, determines what needs to happen, routes work to the right agent or human, and enforces policy. Every AI action flows through Maestro — authenticated, authorized, logged. Operations, finance, HR, and engineering all share one orchestration layer. No developer reaches directly for an AI license; they route through Maestro's control surface.
The SDLC execution engine. Receives work orders from Maestro and executes end-to-end: spec, branch, build, test, review, merge, deploy, validate. Worktree-isolated agents build in parallel across client portfolios spanning ~1,000 repositories, billing the right account automatically. Cross-model validation (Claude + OpenAI) catches regressions before they ship. Production in enterprises today.
The value isn't in giving everyone a Claude subscription. The value is in owning the control plane — routing every AI action through governance, consolidating cost, and making the intelligence layer organizational, not individual.
Instead of 100 individual AI licenses with zero visibility, Thrasoz gives the enterprise a single control surface. Every action is authorized by Maestro, logged, and auditable.
Shipyard tracks token usage per pipeline, per org, per stage. Finance sees exactly what AI is costing — not an amortized guess. Projects are billed precisely.
Maestro routes based on risk, scope, and org policy. High-risk tasks require human approval. Low-risk tasks are auto-authorized. Developers follow the policy — they don't set it.
Every AI action — who triggered it, what it changed, what it cost, and what the outcome was — is logged in the control plane. Compliance and legal have a clear record.
Thrasoz is live today across multiple enterprise clients. The platform is in active development with new capabilities shipping weekly. We're selectively onboarding strategic partners who want to move AI from experiment to operating layer.