Enterprise SaaS•May 10, 2026

AI to reduce Brazilian social security costs (GILRAT/FAP)

Ferramenta de ia que ajude a empresas a reduzir custos previdenciários com GILRAT - FAE - FAP

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Evaluation Scores

6.7/10

GoodOverall Score

Solution

Problem

Features

Market

Revenue

Competition

Risk

Market Analysis

Solution Overview

An AI-powered platform that integrates with a company's payroll and HR systems to analyze accident history and suggest optimizations for GILRAT and FAP rates, reducing social security costs. It provides continuous monitoring and recommendations to prevent overpayments.

Problem Statement

Brazilian companies often overpay social security contributions due to complex and opaque GILRAT and FAP calculations that most HR teams lack expertise to optimize. Traditional consultants are expensive and provide only periodic reviews, missing ongoing savings opportunities.

Key Features

Integrate with payroll and HR systems
Analyze historical accident and claims data
Predict optimal FAP factor adjustments
Generate compliance and savings reports
Provide real-time optimization recommendations
Monitor regulatory changes automatically

Market Snapshot

Market Size: R$ 5 billion (growing 10% annually)
Target Users: Medium-to-large Brazilian companies with over 100 employees, especially those in high-risk industries like construction and manufacturing.
Growth Rate: 10% annually

Monetization Ideas

SaaS Subscription: Monthly or annual tiered subscriptions based on company size and features.
Savings Percentage: Take a cut (e.g., 20%) of the actual savings achieved for clients.
Premium Add-ons: Advanced analytics, custom integrations, or API access for extra fees.
Consulting Setup: One-time fee for initial data setup, calibration, and training.

Competitive Edge

Unlike traditional consultants who offer manual, periodic reviews, this AI solution provides continuous, data-driven optimization with real-time recommendations and automated regulatory updates. The platform scales across clients without proportional headcount increases.

Risk Factors

Regulatory changes in GILRAT/FAP rules could disrupt algorithms.
Data privacy and security concerns when handling sensitive payroll data.
Resistance from HR and legal departments to adopt automated recommendations.

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