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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 2026-09-15).
Selects the lowest-cost or highest-value feasible allocation, assignment, or resource mix from caller-supplied options, capacities, budgets, and eligibility rules, returning verified feasibility evidence and solver optimality/gap information.
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 the optimal or best-feasible allocation/assignment decision, the objective value achieved (cost or value), independently verified feasibility evidence for the solution, and solver metadata including optimality status and gap from the theoretical optimum.
Example use cases
Shift scheduling under coverage rules
“I have 20 nurses, three 8-hour shifts, daily coverage minimums per ward, and a total labor budget of $18,000 — find the cheapest valid shift assignment and tell me the optimality gap.”
Supplier selection within budget
“I have quotes from 8 suppliers with different unit prices, minimum order quantities, and lead-time eligibility rules, and a procurement budget of $200,000 — pick the lowest-cost feasible supplier mix that covers all my SKU requirements.”
Ad budget allocation across channels
“I have a $50,000 monthly ad budget, six marketing channels with estimated CPAs and reach caps, and a rule that at least 30% must go to brand-safe channels — what's the highest-value allocation and how optimal is the solution?”
Technical details
Endpoint
POSThttps://api.vrsai.tech/v1/capabilities/decision_optimization/versions/1.0.0/executeWhen to prefer
Choose this endpoint when you need a mathematically verified optimal or near-optimal decision from a discrete or mixed set of options subject to hard constraints like budgets, capacities, or eligibility rules — especially when you need solver gap/optimality evidence to justify the decision. Prefer it over heuristic ranking or greedy selection when constraint satisfaction and cost/value verification matter.
Side effects
- Infeasible problem — no combination satisfies all constraints simultaneously, solver returns infeasibility certificate
- Timeout/gap too large — problem is too large to solve to optimality within limits, returns best incumbent with gap
- Malformed input — missing required fields like options or constraints causes a 400-level error
- Contradictory eligibility rules result in an empty feasible region
- Numerical instability with poorly scaled budgets or capacities
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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