Our editorial policy is the foundation of our team. We investigate whether an asset is built on reliable and defensible software architecture. Or if it is siloed within a proprietary stack that represents a long-term technical debt. We examine the server-side implementations and client-interface protocols to determine if the asset can survive the inevitable model migrations.
Our mission is to provide high-integrity technical due diligence and evaluations where innovative AI infrastructure and startups have become blurred. Our editorial policy forms the foundation of our commitment to maintain the caution of an investor. By our 2026 standards, an evaluation is not a surface-level inspection of features; it is a structural technical audit. We prioritize information gain over generalities. We ensure that every asset we evaluate is subjected to a proprietary testing and evaluation framework that anchors our analysis in verifiable data and current AI and LLM research.
Editorial Policy: Processes
The core of our evaluation process begins with Model Context Protocol (MCP) Compliance. Our editorial team investigates whether a software asset is built on standardized MCP-native architecture. MCP allows the asset to integrate seamlessly into broader enterprise ecosystems. Or if it is within a proprietary stack that represents a long-term technical debt. We examine the server-side implementations and client-interface protocols to determine if the asset can survive the model migrations or merger.
To address the industry-wide challenge of AI reliability, we implement the RAGrecon (Groundedness) Score. This process involves a deep-layer audit of the asset’s Retrieval-Augmented Generation (RAG) architecture. We don’t just take a founder’s word for it; we perform partitioned reasoning tests. We segment input/output pairs into verifiable units. By utilizing a Dual-Model Consensus, where two distinct LLMs judge the output of a third. We calculate a groundedness percentage that reveals the true hallucination risk. The evaluation is for buyers in regulated sectors where a single ungrounded response could lead to liability.
Furthermore, we also evaluate the Learning Velocity of an acquisition target through DraftRL metrics. This evaluation focuses on the Reinforcement Learning from Human Feedback (RLHF) loops and the Chain-of-Draft reasoning capabilities of the software. Based on user corrections, we analyze proprietary feedback data to see how quickly a model’s reward system improves over time. An asset with high DraftRL scores indicates a proprietary data moat. This moat widens with usage. On the other hand, a low score suggests a stagnant system.
Editorial Policy: Academic Research
Our reporting is anchored in a synthesis of market data reports and academic research. We bridge the gap between AI breakthroughs and acquisitions. By citing institutional research, we provide our readers with a technical debt ledger that quantifies the cost of remediation. Our editorial process ensures that when Block Article issues a buy recommendation, it is backed by a multi-layered audit and research. We conduct our research to protect investor capital and promote a more transparent AI marketplace.
Acquisition Disclaimer: All content on BlockArticle.com is for informational purposes only and does not constitute financial, legal, or investment advice. Business acquisitions involve significant risk. We strongly recommend performing your own due diligence and consulting with licensed professionals (attorneys, CPAs, and brokers) before entering any transaction.