A Scalable AI Framework for SMEs and a Novel Metric: Proactive Adaptation Rate (PAR)

Year 2025, Volume: 1 Issue: 2, 155 - 171, 28.07.2025

Alp Par

https://doi.org/10.26650/d3ai.1716061

Abstract

This paper presents Kolay.AI, a scalable AI framework designed to address the key challenges faced by SMEs, including cold starts, model drift, data privacy, and limited computational resources. Its novel contribution is the Proactive Adaptation Rate (PAR) metric, which enables AI systems to anticipate market shifts and make preemptive adjustments. The framework leverages FFT, LSTM, ARIMA, and Linear Regression models on real-world data from 40 SMEs across diverse sectors. Through federated learning and modular service architecture, it delivers secure, high-performance AI solutions without compromising data privacy. Evaluation metrics (RMSE, MAE, MAPE, PAR) demonstrate significant improvements in forecasting accuracy and operational efficiency. Results show that SMEs using Kolay.AI achieved 15% 20% better cash flow, reduced operational downtime, and maintained alignment with dynamic market conditions. The PAR metric proved critical for early anomaly detection, enabling optimised inventory management and risk mitigation. By shifting the focus from reactive to proactive AI evaluation, this work advances SME-centric AI adoption. The framework’s integration of federated learning and PAR-driven monitoring sets a foundation for future research on adaptive AI in resource-constrained environments.

Keywords

AI Forecasting , SMEs , FFT , LSTM , CRM

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