Hybrid Deterministic + AI SaaS

2) Constraints

• • Core business logic must remain deterministic
• • AI advisory layer cannot mutate authoritative records directly
• • Performance budgets and p95 latency commitments enforced

3) Architecture

AIAS designed a deterministic transaction engine with a separate AI advisory service. Reach provided go-to-market alignment, Zeo delivered platform engineering, and Settler packaged deployment controls.

4) Tradeoffs

• • Strict validation adds latency overhead but preserves deterministic commitments.
• • Human approval on high-impact actions slows throughput while reducing governance risk.
• • Separate advisory services increase deployment complexity but isolate failure domains.

5) Governance model

• • Immutable audit logs track recommendation-to-action lifecycle.
• • Explainability artifacts are included in customer success workflows.
• • Known limitations are documented in customer-facing governance docs.

6) Performance outcomes

• • Maintained deterministic latency profile under increased load
• • Expanded product capability without breaching reliability SLAs
• • Improved enterprise confidence through transparent guardrails

7) What we would do next

• • Architecture supports federated deployments for regulated clients
• • Deterministic core simplifies future model upgrades
• • Shared ecosystem patterns keep Reach and Zeo services aligned