Deterministic AI workflows. The flow is the spec.

Most “agentic” AI makes the model the orchestrator: one prompt in, any of many paths out. That’s non-determinism as a feature — and it’s why so many pilots never reach production.

• Every run can take a different path — non-determinism as a feature
• Opaque decisions; end-to-end testing is impossible
• Token usage — and cost, latency — vary wildly
• Most enterprise solutions: “Elasticsearch with extra steps”

SACER is workflow-as-orchestrator with LLM-as-capability. The flow definition is the execution plan, and the model is invoked only where human-like judgment is needed — the glowing step in the schematic.

• The flow is the spec — same input, same result every time
• Every step is explicit, logged, and auditable
• The LLM is bounded to the steps that need judgment
• Version it, reproduce it, prove it — on a closed network

Deterministic by default, secure by design, and built to outlast any single model. Six guarantees that make SACER safe to put in production.

• Deterministic core — YAML workflows define execution
• MCP-native — plugs into your stores, databases, and tools
• Secure by design — on-prem, air-gapped, no data egress
• Model-agnostic — tomorrow’s models drop in, no lock-in

SACER ships proven on day one. The engagement is people-led: senior ML practitioners embed with your team to build the integrations and hand over a capability you own, audit, and extend.

• The engine — SACER, deterministic at the core
• People-led — integration & training by senior ML practitioners
• Adopt — add it to your own stack
• License — offer it to your clients