The Signal
Technical deep dives on deterministic AI, compliance architecture, and the future of verifiable intelligence.
Garbage In, Garbage Out Is Simple to Understand but Difficult to Execute
Everyone gets the concept of Garbage In and Garbage Out. What's difficult is clearly filtering - what's garbage and what's not.
Phantom Human-In-The-Loop
Every enterprise AI pitch ends with "Don't worry, we have a human in the loop." But human-in-the-loops today feel like performance theater.
Guardrails — Status Quo vs What's Needed
Everyone claims having guardrails for their agents yet pilots get blocked. What do the guardrails do vs what's needed?
Evals Are NOT Audits
Evals are being looked at as the missing piece in the AI stack. For most part, they are. But to close the feedback loop, you need a more deterministic output.
Ontologies: What They Are, Why They Matter Now
The spotlight is on ontologies, semantic layers, and taxonomies. These are familiar to data practitioners but for the rest — overwhelming. A plain-language breakdown.
Neuro-Symbolic AI — A Practitioner's Taxonomy
Five architectures. Six dimensions. A simple framework that starts with your workflow — not vendor pitch decks.
Context Graphs: The Missing Piece from Pilot to Production?
Context graphs capture what people actually do. But without reconciling with SOPs and policy, you're routing around the problem — not fixing it.
CogniSwitch: An Ontology-Governed Approach to Enterprise AI
We are building CogniSwitch for organizations where inconsistent AI gets you fired - or fined. This is how we approached the problem of deterministic, auditable enterprise AI.
The End of Deploy and Pray
After 3 years of ChatGPT, Gartner analysts, investors and most importantly, enterprises are still asking the question they should have asked earlier: when does it all get real?
Intelligence Migration is NOT Possible
Can you export not just your data, but the intelligence layer - all the learned relationships, orchestration logic, and reasoning patterns?
Large Language Models and Knowledge Graphs: A Journey Towards Collaborative Intelligence
A founder who built Expert Systems in 1989 explains why LLMs and Knowledge Graphs need each other - and why vector databases alone won't fix hallucinations.
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Ideas that compound
Our goal of writing is to simply clarify our own thinking. If it helps you build better systems, even better. Every essay here comes from building and deploying systems for healthcare teams. The problems are hard and the solutions we build are specific. That's what makes the lessons worth sharing.
The AI industry is drowning in hype. We write to cut through it.