Acadify Engineering Team is the technical team behind Acadify Solution’s AI, software engineering, cloud, automation, and product development work. We publish practical, research-informed insights based on our engineering experience across AI systems, LLM applications, software development, cloud infrastructure, automation, AI testing and evaluation, and digital product engineering. Our content is designed to help founders, engineering teams, technology leaders, and businesses understand complex technical topics and make informed decisions about building, deploying, and improving software and AI systems.
A leading insurance company faced significant challenges in their customer routing system, which was experiencing latenc...
Executive Problem Statement & Financial/Operational Risk As AI adoption continues to grow, enterprises face increasing p...
In this critical showdown, we pit AWQ, GPTQ, and FP8 against each other in terms of quantization accuracy degradation. O...
Abstract & Executive Synthesis This research report explores the use of Hybrid Mamba-Transformer MoE architectures for l...
Introduction IntroductionEnterprise AI workloads combine API traffic, retrieval, model inference, data processing, and a...
This guide provides a step-by-step walkthrough of setting up and optimizing vLLM with Ray on multi-GPU clusters for high...
Our client, a leading fintech company, faced a critical bottleneck in their document QA system. The legacy architecture ...
As enterprises rapidly shift from isolated chat interfaces to autonomous multi-agent ecosystems and production RAG netwo...
FlashAttention-3 and FlashDecoding target different parts of transformer inference, so benchmark results should be inter...
This report presents a comprehensive analysis of Hybrid Mamba-Transformer MoE Architectures for Long-Context Retrieval t...
1. Overview 1. OverviewAn assessment should produce an actionable roadmap rather than a label. For each stage, record th...
Evaluation Overview ASR-based AI evaluation turns real voice interactions into structured evidence for measuring transcr...
