Thinking in
Systems & Context
In-depth explorations of enterprise context management, AI-powered workflows, and the engineering patterns that make complex software actually work. Written by practitioners, not theorists.
BLORPBLORP Foundations
Foundations is the architecture pattern and reference implementation BLORPBLORP deploys to make enterprise AI projects ground out, scope cleanly, trace end-to-end, and integrate with the rest of your stack. Five pillars, one product.
Grounded retrieval
Most enterprise AI projects fail because the model answers from training data instead of your actual documents. Grounded retrieval is the discipline that fixes that.
Context management
A grounded model still needs to know who is asking, what they are allowed to know, and what they said in the previous turn. Context management is the layer that handles all three.
Multi-model routing
A single AI provider is a single point of failure, a single price ceiling, and a single capability ceiling. Multi-model routing fixes all three.
Provenance + observability
When the AI tells a customer something, you need to be able to show — months later — exactly why. Provenance and observability are how Foundations makes that possible.
Mesh integration
A grounded, traced, scoped AI that lives in a tab nobody opens is still a failed deployment. Mesh integration is what makes the AI a participant in your actual workflows.
The Foundations-First Approach to Enterprise AI
From the road, both houses look identical. Only when the storm rolls in does the difference become visible. Most enterprise AI is built on sticks — context scoping, compliance attenuation, audit trails, and hallucination posture are the four bedrock layers that determine whether your system survives audit, regulator, or breach.
Enterprise Change Management with AI: A Practical Guide
AI doesn't just automate change management workflows — it fundamentally changes what's possible. From predictive risk scoring to automated impact analysis, here's how to build an AI-powered change management practice.
Requirements Management Without the Spreadsheet Hell
Requirements scattered across emails, docs, and spreadsheets are the root cause of failed projects. Here's how to build a requirements management system with traceability, versioning, and AI-powered gap analysis.
Building a Change Request Database That Actually Works
Most change request systems are glorified spreadsheets. Here's how to build one with structured lifecycles, approval workflows, impact analysis, and AI-powered context — so changes stop falling through the cracks.
Context Management vs. Knowledge Management: What's the Difference?
Knowledge management and context management solve different problems with different architectures. Understanding the distinction is critical for any organization adopting AI.
How to Build an AI Context Engine for Your Organization
A step-by-step technical guide to building a context engine that connects your organizational knowledge to AI systems — from ingestion pipelines to MCP server integration.
What Is Enterprise Context Management?
Enterprise context management is the discipline of structuring, curating, and delivering organizational knowledge so that both humans and AI systems can reason about your business accurately. This is the definitive guide.