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AI Dynamic Pricing API

Revenue Optimization Elasticity Modeling Risk-Aware Pricing Audit-Ready
Operated by Spyface Tech Company, LLC • 30 N Gould St Ste N, Sheridan, WY 82801 USA • Support: hello@spyface.com

What dynamic pricing really is

Dynamic pricing is not “increase price when demand is high”. It is the problem of choosing a price that maximizes expected profit under uncertainty.

The AI Dynamic Pricing API computes optimal prices using elasticity, demand confidence, refund risk, and inventory pressure.

Primary outputs

  • Optimal price (with bounds)
  • Expected conversion probability
  • Expected margin
  • Volatility & regret risk
  • Price explanation codes

1) Overview

Pricing decisions compound. A bad price does not just lose a booking — it distorts demand signals and future forecasts.

2) Why Rule-Based Pricing Fails

Legacy pricing

  • If demand ↑ → price +10%
  • If inventory ↓ → price +5%
  • Hard-coded caps

Blind to elasticity and regret.

Spyface pricing

  • Models price–demand curve
  • Accounts for uncertainty
  • Optimizes expected profit

Stable, explainable outcomes.

3) Elasticity Modeling

Elasticity is learned continuously per product, route, destination, and window. 


P(conversion | price) =
  sigmoid(
    α
  - β * price
  + γ * urgency
  + δ * availability_confidence
  )

Models degrade gracefully when data is sparse.

4) Pricing Objective


maximize:
  price * P(conversion)
- expected_refund_cost
- volatility_penalty
- long_term_regret

The optimizer prefers stable revenue over short-term spikes.

5) Constraints

ConstraintDescriptionExample
Price bounds Business or regulatory limits $80 ≤ price ≤ $220
Parity Channel consistency No undercut vs partner rate
Volatility Max allowed price delta ≤ 8% per 24h
Ethics No surge in emergencies Medical travel freeze

6) Endpoints

MethodEndpointPurpose
POST/v1/pricing/optimizeCompute optimal price
POST/v1/pricing/simulateSimulate price scenarios
POST/v1/eventsSend realized outcomes

7) Sample Schema

{
  "request_id":"req_price_001",
  "product":{
    "type":"HOTEL_ROOM",
    "id":"rm_991"
  },
  "context":{
    "destination":"BCN",
    "lead_time_days":12
  },
  "constraints":{
    "min_price":80,
    "max_price":220
  }
}

8) Code Example (Python)


import os, requests

resp = requests.post(
  "https://api.spyface.com/v1/pricing/optimize",
  headers={
    "Authorization": f"Bearer {os.environ['SPYFACE_API_KEY']}",
    "Content-Type": "application/json"
  },
  json=payload
)

data = resp.json()
print("Recommended price:", data["price"])
print("Expected margin:", data["expected_margin"])

9) Controls & Governance

Enterprise-grade safeguards

  • Price explanation & audit logs
  • Manual override hooks
  • Shadow pricing for testing

Pricing decisions must be reversible and defensible.

Why this API wins

  • Optimizes profit, not clicks
  • Handles uncertainty explicitly
  • Safe to automate at scale

For revenue architecture reviews: hello@spyface.com