Requestador

AI Answers That Make Sense

Centralize. Validate. Convert.

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What is Requestador?

Requestador is your AI-powered middleware that transforms how your business interacts with AI. Define your own API-like prompts, apply validation rules, and return AI responses in the format you actually need.

Whether you're building a chatbot, product recommendation system, or data processing pipeline — Requestador ensures your AI answers are accurate, structured, and business-compliant.

Key Features

Centralized AI Prompt Definitions

Define prompts and manage parameters like you would with any API.

Smart Response Validation

Add custom logic to verify AI outputs before passing them on.

Catalog-Based Mapping

Match AI outputs to predefined business values (e.g., map "0.5L" to "Half-Litre Bottle").

Retry & Reformat on Failure

When validation fails, retry or transform responses automatically.

Organization-Aware Access Control

Define endpoints and catalogs per organization. Users belong to one org only.

Use Cases

Product Data Extraction
Smart Price Suggestions
SKU & Attribute Matching
Intelligent QA with Business Logic
AI-Driven Form Fillers

Who is it for?

SaaS Companies

E-commerce Teams

Product Data Managers

Business Analysts

Developers working with LLMs

How It Works - API-Like Prompts

Define your AI interactions like API endpoints. Set parameters, validation rules, and expected outputs - all in one place.

Example: Product Data Extraction Prompt
POST /api/prompts/extract-product-data
{
  "prompt_template": "Extract product details from: input_text",
  "parameters": {
    "input_text": "required",
    "format": "json"
  },
  "validation_rules": [
    "price_must_be_numeric",
    "category_must_exist_in_catalog"
  ],
  "retry_on_failure": true
}
Validation & Retry Logic
When AI returns invalid data, Requestador automatically retries with refined prompts or applies transformation rules. 
// If AI returns "0.5 liters" but catalog expects "Half-Litre Bottle"
mapping_rule: {
  "0.5L" → "Half-Litre Bottle",
  "500ml" → "Half-Litre Bottle",
  "0.5 liters" → "Half-Litre Bottle"
}

Real Prompt Examples

E-commerce: Smart Price Suggestions
Generate competitive pricing based on product details and market data 
"prompt": "Given this product: product_name with features features, suggest optimal price. Market range: price_range"
"output_format": { "price": "number", "confidence": "0-100" }
"validation": "price must be within market_range ±20%"
Data Processing: SKU Matching
Match incoming product data to existing SKU catalog with fuzzy matching 
"prompt": "Match this product to existing SKU: product_description. Available SKUs: sku_catalog"
"output_format": { "matched_sku": "string", "confidence": "0-100" }
"fallback": "If confidence < 80%, flag for manual review"
Customer Support: Intelligent QA
Answer customer questions using business knowledge base with accuracy validation 
"prompt": "Answer customer question: question using only information from: knowledge_base"
"validation": [
  "answer_must_reference_knowledge_base",
  "no_hallucinated_information",
  "confident_or_escalate"
]

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