# vrsai Decision Optimization

> 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.

## Facts

- Endpoint: POST https://api.vrsai.tech/v1/capabilities/decision_optimization/versions/1.0.0/execute
- Price: $0.03/call
- Payment: x402
- Status: unknown
- Last checked: 2026-09-15
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/vrsai-decision-optimization-c0b672a8
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_XHLF_PiSVvW5HJdBdXUNn

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability vrsai-decision-optimization-c0b672a8 -d '<json body>'
```

Example prompt: I have 5 projects, 12 contractors with different hourly rates and skill tags, a $50,000 weekly budget, and each project needs at least one certified contractor — find the lowest-cost valid assignment and tell me how close it is to the mathematical optimum.

## When to prefer this

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.

## Known failure modes

- 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

## 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.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "required": [
  "input"
 ],
 "properties": {
  "input": {
   "type": "object",
   "required": [
    "type",
    "method",
    "bodyType",
    "body"
   ],
   "properties": {
    "body": {
     "type": "object"
    },
    "type": {
     "type": "string",
     "const": "http"
    },
    "method": {
     "enum": [
      "POST"
     ],
     "type": "string"
    },
    "bodyType": {
     "enum": [
      "json",
      "form-data",
      "text"
     ],
     "type": "string"
    }
   },
   "additionalProperties": false
  },
  "output": {
   "type": "object",
   "required": [
    "type"
   ],
   "properties": {
    "type": {
     "type": "string"
    },
    "example": {
     "type": "object"
    }
   }
  }
 }
}
```

## Response schema (JSON Schema)

```json
{
 "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"
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/vrsai-decision-optimization-c0b672a8/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from api.vrsai.tech](https://www.zero.xyz/host/api.vrsai.tech/llms.txt)