Autonomous
End-to-end execution with no human in the loop. The right call when the cost of a wrong decision is bounded and the system has earned the trust.
Semi-Autonomous
Agents act unless a guardrail fires, then escalate. Approve once, run forever — until something changes.
Assisted
Recommendations, not actions. The model surfaces options and shows its work; the human picks. The default for the high-stakes case.
Trust Layer
Evals, observability, governance
Eval suites you can show to a regulator. Traces for every step of every run. Approval workflows, RBAC, and full audit trails — built into the platform, not bolted on.
Eval → Deploy → Observe → Govern → Promote
Agents
Multi-Agent Orchestration
Decompose work across specialized agents. The orchestrator handles state, conflicts, retries, and tool access — so you write the goal, not the plumbing.
Models
Classical + LLM
XGBoost where it wins, LLMs where they win. RAG over enterprise data, function calling, and custom fine-tuning — all in one runtime.
Production from day one
Every model, every agent, every workflow runs through the same governance, observability, and approval spine. No demoware that can't survive scrutiny.
Custom eval suites
Regression detection
A/B + shadow deploys
Run-level tracing
Cost and latency budgets
Quality metrics by use case
RBAC and approval workflows
SOC 2, HIPAA, GDPR posture
Full audit trail