Chiri blog
Ideas, research, and practical guides.
49–60 of 78 articles
What an Ontology Actually Is, and Why It Decides AI Output Quality
What an ontology actually is inside Chiri, why it decides AI output quality, and CTO Matthew Wimberly's story of consolidating 153 data sources down to five.
The Trust Ledger
Trust in leadership has dropped from 25% to 19% since 2019, Josh Bersin finds, and AI deployed without visibility for workers will speed that decoupling up.
Per-Seat Pricing Is Back
Commodity pricing like GLM-5.3 Flash cuts a heavy AI workload from $225-$700 at frontier rates to single dollars, making flat per-seat pricing viable again.
The Completion Standard
Learn how a clear completion standard and an independent validator help teams check AI agent results.
Accountability, Not Capability
A Google Cloud survey finds 79% of tech leaders name security, governance, or operations, not model quality, as the top blocker to scaling AI inference.
Why Everyone Is Talking About an AI Harness, and What You Need to Know
Learn what an AI harness does and how it controls agent work. Use these questions to assess vendor access, security, and oversight.
Cheaper Tokens, Bigger Bills
Learn why AI bills can rise as token prices fall. Track cost per completed task to understand spending.
The Seat Survived. The Margin Didn’t.
Explore why AI can preserve software subscriptions while shifting value toward providers that deliver business results.
Where Digital Twins Come In: From a Wastewater Plant to Company Memory
Matthew Wimberly explains how digital twins and connected company knowledge support business continuity.
What Real AI Agent Governance Looks Like in Practice
Matthew Wimberly explains five AI governance practices, including data controls, access rules, and cost limits.
Pods, Not Departments: How Chiri Structures AI Delivery Work
See how Chiri pairs engineers with business operators to deliver AI work. Learn how weekly demonstrations keep teams involved.
Knowledge Graph vs Vector Database vs Ontology: Which Layer Decides AI Output Quality?
Knowledge graph, vector database, ontology: two are storage and one is meaning. What each does, which failure each causes, and the five checks to run before you build.