Insights & Teardowns
Rigorous analyses on evaluating complex systems, stress-testing multi-million dollar vendor proposals, de-risking M&A technology diligence, and deploying production AI without fragile wrappers.
Over 70% of full software rewrites fail to achieve their stated objectives. This article breaks down the diagnostic framework used to evaluate high-stakes rewrite proposals, uncover root bottlenecks, and identify high-leverage alternatives.
Private equity and growth investors frequently rely on high-level IT questionnaires that miss catastrophic technical debt. This guide outlines how deep code inspection and cloud audits protect deal valuation.
Moving from an LLM prototype to an enterprise production system requires software engineering discipline. This article explains how to audit AI architectures for latency, cost, and hallucination control.
Most technical due diligence checklists are surface-level IT questionnaires that miss the architectural risks that destroy post-close value. This is the actual diagnostic framework Anubis uses on live M&A engagements.
Technical debt is invisible in financial due diligence but highly visible in post-close engineering costs. This guide explains how to identify it before the deal closes.
Internal architecture reviews are limited by the cognitive biases of the team that built the system. This article describes what an independent architecture review actually examines and how it produces different — and more actionable — findings.
Some architectural decisions create risk that is invisible until a business-critical inflection point — a traffic spike, a security audit, or an acquisition. This article identifies the most common silent risk accumulators.
Most "AI readiness" assessments are frameworks for selling AI consulting engagements. This article defines what genuine readiness requires in technical and organizational terms.
Why executive teams get misled by rapid AI prototypes, and the exact systems engineering gaps—from deterministic evaluation to token unit economics—that must be solved before going to production.
Software projects fail in recognizable patterns. This article describes the diagnostic framework for identifying root causes and distinguishing recoverable projects from write-offs.
The decision to rewrite or modernize a legacy system is high-stakes and frequently made on intuition rather than analysis. This framework provides a structured approach to making the right call.
Undocumented legacy systems where institutional knowledge has departed create existential operational risk. This article outlines the forensic process for recovering understanding and developing a safe path forward.