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vrsai Decision Optimization
x402
vrsai Decision Optimization is a paid API for AI agents from api.vrsai.tech, paid per call via x402, $0.03/call, status unknown (last checked 2026-10-13).
Solves allocation, assignment, selection, and resource-mix problems to find the lowest-cost or highest-value feasible solution given caller-supplied options, capacities, budgets, and eligibility rules.
Price / call$0.03
Success—
StatusUnknown
How this service works
Choose the lowest-cost or highest-value feasible allocation, assignment, selection, or resource mix from caller-supplied options, capacities, budgets, and eligibility rules, with independently verified feasibility evidence and solver optimality/gap information.
Output
Returns a JSON object with: (1) a status field (e.g. OPTIMAL), (2) the full decision object showing quantities or assignments per option, (3) economics including the objective value and optimization sense, (4) quality metrics including absolute/relative gap and solver optimality rating, (5) evidence including solver name/version, constraint counts, input and model fingerprints for auditability, and (6) diagnostics listing any binding constraints.
Example use cases
Cheapest multi-supplier fulfillment
“I have three warehouses each needing 100 units and two suppliers with different per-unit costs and capacity limits. Find the cheapest allocation of supply across suppliers and warehouses that meets all demand without exceeding any supplier's capacity.”
Budget-constrained project portfolio
“I have a $500k budget and 8 candidate projects each with a cost and an expected return. Which projects should I fund to maximize total return without going over budget? I need to know if the solution is optimal and what the objective value is.”
Shift coverage minimizing labor cost
“I need to cover 5 shifts with workers who each have different hourly rates and availability constraints — minimize total labor cost while ensuring every shift has at least 2 workers assigned. Give me the assignment plan and confirm it's feasible.”
Technical details
Endpoint
POSThttps://api.vrsai.tech/v1/capabilities/decision_optimization?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a mathematically verified, cost- or value-optimal allocation, assignment, or selection decision with proof of optimality (gap = 0) and auditable fingerprints — not just a heuristic or greedy approximation. Ideal when you have structured supply/demand, capacity, budget, or eligibility constraints and need a defensible, reproducible decision. Prefer over custom code when you want built-in feasibility verification and solver metadata without standing up your own OR infrastructure.
Side effects
- Infeasible problem (conflicting constraints or demand exceeding total capacity) — returns INFEASIBLE status
- Unbounded problem (missing bound constraints) — returns UNBOUNDED status
- Malformed input body (missing required fields) — returns HTTP 400 with validation error
- Solver timeout on very large problems — may return a non-optimal solution with gap information
- Ambiguous eligibility rules that cannot be expressed as linear constraints — may silently drop or misapply rules
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"status": "OPTIMAL",
"quality": {
"best_bound": "160",
"absolute_gap": "0",
"relative_gap": "0",
"solver_optimality": "optimal_within_profile_tolerance",
"solution_uniqueness": "unknown",
"semantic_feasibility_verified": true
},
"decision": {
"lanes": [
{
"quantity": "80",
"demand_id": "warehouse_1",
"source_id": "supplier_a"
},
{
"quantity": "0",
"demand_id": "warehouse_1",
"source_id": "supplier_b"
}
],
"problem_type": "allocation",
"source_activations": [
{
"active": true,
"source_id": "supplier_a"
},
{
"active": false,
"source_id": "supplier_b"
}
]
},
"evidence": {
"solver": "highs",
"solver_version": "1.15.2",
"ruleset_version": "decision_optimization_r1",
"input_fingerprint": "v1:23bf4e29934aad481275e4bf38cb45617bbbe664ad3dd5d771af2fe3d4d1c938",
"model_fingerprint": "v1:392d8ab317c91b7339a92c2b9fa4b2b30f326f2175955d6fa362c4b57287c5a5",
"constraints_checked": 3,
"constraints_violated": 0,
"decision_fingerprint": "v1:43b9ac81c64b8f95e46837a5103ac13d8f27ad829b32ee06463079bf0be83b81"
},
"economics": {
"sense": "minimize",
"objective": "minimize_total_cost",
"objective_value": "160"
},
"diagnostics": {
"binding_constraints": []
},
"problem_type": "allocation",
"machine_action": "CONSUME_OPTIMAL_DECISION"
}
}Tags
x402
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