These are recurring problem classes the optimization engine is built for—not a claim of fully packaged vertical suites. Domain-specific libraries remain a limited, evolving surface; the core product is formulate → solve → explain.
Budget, staff, inventory, and compute across competing objectives with hard caps and minimums. The engine searches feasible regions instead of hand-tuning heuristics.
Balance return, risk, and policy limits when objectives conflict. Useful when “best” is a constrained frontier, not a single closed form.
Routing, timetables, and capacity planning where discrete choices and continuous parameters mix. Sematryx targets feasible, high-quality schedules under stated rules.
Regulatory, safety, and operational rules encoded as constraints—not afterthoughts. Explanations help teams justify choices to stakeholders and auditors.
Agents call the same engine via MCP or API: formulate from natural language, solve with hosted compute, return numbers plus narrative the agent can use downstream.
See benchmark evidence and the full product workflow on the pages below.