<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Rick Huang — Applied AI Architect, Business Lead</title><description>Rick Huang — Applied AI Architect, Business Lead. Independent Advisor to FlowVision. Applied AI grounded in reliable data and measurable business outcomes.</description><link>https://www.hzy230051.com/</link><language>en</language><item><title>Green pipeline, wrong numbers: data quality at every layer</title><link>https://www.hzy230051.com/tech/data-quality-at-every-layer/</link><guid isPermaLink="true">https://www.hzy230051.com/tech/data-quality-at-every-layer/</guid><description>A pipeline that ran is not a pipeline that is right. Part 2 of a series on data foundations for enterprise AI: the checks that run at every layer, the SLA on the gold layer, and who gets told when something breaks.</description><pubDate>Sat, 10 Oct 2026 00:00:00 GMT</pubDate><category>data quality</category><category>data pipelines</category><category>dbt</category><category>data observability</category><category>enterprise AI</category><category>data foundations series</category></item><item><title>Before you build the agent: working backwards to your data pipeline</title><link>https://www.hzy230051.com/tech/before-you-build-the-agent/</link><guid isPermaLink="true">https://www.hzy230051.com/tech/before-you-build-the-agent/</guid><description>An agent is only as trustworthy as the numbers under it. Part 1 of a series on data foundations for enterprise AI: getting every channel&apos;s data in, and modeling it so it agrees with itself.</description><pubDate>Wed, 07 Oct 2026 00:00:00 GMT</pubDate><category>data pipelines</category><category>ingestion</category><category>e-commerce</category><category>enterprise AI</category><category>data foundations series</category></item><item><title>Data models agents can trust</title><link>https://www.hzy230051.com/tech/data-models-agents-can-trust/</link><guid isPermaLink="true">https://www.hzy230051.com/tech/data-models-agents-can-trust/</guid><description>Analysts fill gaps in a data model with tribal knowledge; AI agents fill them with confident guesses. Four ideas, with real Salesforce examples, for models an agent can answer from correctly.</description><pubDate>Wed, 30 Sep 2026 00:00:00 GMT</pubDate><category>data modeling</category><category>agents</category><category>Salesforce</category><category>semantic layer</category><category>data quality</category></item><item><title>Migrating a chart of accounts without breaking a single report</title><link>https://www.hzy230051.com/tech/chart-of-accounts-migration/</link><guid isPermaLink="true">https://www.hzy230051.com/tech/chart-of-accounts-migration/</guid><description>When finance re-cuts the general ledger, every dbt model, metric and dashboard downstream is exposed. A playbook: crosswalks, dual-running, zero-tolerance reconciliation, and treating the account hierarchy as a data contract.</description><pubDate>Wed, 23 Sep 2026 00:00:00 GMT</pubDate><category>chart of accounts</category><category>general ledger</category><category>dbt</category><category>finance data</category><category>migration</category></item><item><title>Certifying data models with an agent in the loop</title><link>https://www.hzy230051.com/tech/certifying-data-models-with-an-agent/</link><guid isPermaLink="true">https://www.hzy230051.com/tech/certifying-data-models-with-an-agent/</guid><description>A production pattern for deciding whether a dbt model is fit for finance reporting: deterministic gates first, sandboxed tests against production data, ticket traceability, and a coding agent used only for triage.</description><pubDate>Sun, 19 Jul 2026 00:00:00 GMT</pubDate><category>dbt</category><category>data governance</category><category>Claude Code</category><category>BigQuery</category><category>agents</category></item><item><title>Turning regulatory PDFs into agent-ready packs</title><link>https://www.hzy230051.com/tech/regulatory-pdfs-to-agent-packs/</link><guid isPermaLink="true">https://www.hzy230051.com/tech/regulatory-pdfs-to-agent-packs/</guid><description>Models are bad at 60-page government PDFs and good at well-shaped data. A recipe for converting one into the other, worked through on the public US product-safety eFiling documentation.</description><pubDate>Sun, 05 Apr 2026 00:00:00 GMT</pubDate><category>agents</category><category>regulatory</category><category>document processing</category><category>CPSC</category><category>Python</category></item><item><title>Evals for analytics agents: grading answers against the ledger</title><link>https://www.hzy230051.com/tech/evals-for-analytics-agents/</link><guid isPermaLink="true">https://www.hzy230051.com/tech/evals-for-analytics-agents/</guid><description>If an AI answers finance questions, its accuracy should be a number you can report. How to build a golden question set, compute ground truth from certified models, score numeric and tabular answers, and catch regressions in CI.</description><pubDate>Sun, 22 Feb 2026 00:00:00 GMT</pubDate><category>evals</category><category>agents</category><category>analytics</category><category>CI</category><category>finance data</category></item><item><title>Five long bets on AI, data and financial infrastructure</title><link>https://www.hzy230051.com/tech/five-long-bets/</link><guid isPermaLink="true">https://www.hzy230051.com/tech/five-long-bets/</guid><description>Where I&apos;m putting time and capital over the next decade, the signal I watch for each bet, and what would prove me wrong.</description><pubDate>Sun, 28 Dec 2025 00:00:00 GMT</pubDate><category>AI</category><category>investing</category><category>semantic layer</category><category>infrastructure</category><category>long-term thinking</category></item><item><title>The semantic layer is the API for AI</title><link>https://www.hzy230051.com/tech/semantic-layer-is-the-api-for-ai/</link><guid isPermaLink="true">https://www.hzy230051.com/tech/semantic-layer-is-the-api-for-ai/</guid><description>Text-to-SQL demos well and fails quietly on finance data. Governed metrics, declared grain and semi-additive measures are what make an analytics agent trustworthy, and the semantic layer is how you hand them over.</description><pubDate>Sun, 12 Oct 2025 00:00:00 GMT</pubDate><category>semantic layer</category><category>text-to-SQL</category><category>MCP</category><category>agents</category><category>finance data</category></item><item><title>Basis points on a moving balance: modeling asset-based fees</title><link>https://www.hzy230051.com/tech/modeling-basis-point-fees/</link><guid isPermaLink="true">https://www.hzy230051.com/tech/modeling-basis-point-fees/</guid><description>Custody and asset-management fees are charged in basis points on balances that change every block. Average daily balance, tiered vs. banded pricing, minimums, valuation cutoffs, and reconciling the model to the invoice.</description><pubDate>Sun, 03 Aug 2025 00:00:00 GMT</pubDate><category>fees</category><category>pricing</category><category>finance data</category><category>dbt</category><category>crypto custody</category></item><item><title>Novelty is the highest-yield asset</title><link>https://www.hzy230051.com/life/novelty-is-the-highest-yield-asset/</link><guid isPermaLink="true">https://www.hzy230051.com/life/novelty-is-the-highest-yield-asset/</guid><description>The best returns I get are on time spent learning something new or going somewhere unfamiliar. Why routine makes the years disappear, and why reading and travel belong in the budget, not the leftovers.</description><pubDate>Sun, 29 Jun 2025 00:00:00 GMT</pubDate><category>time</category><category>memory</category><category>reading</category><category>travel</category><category>living deliberately</category></item><item><title>One customer, five systems: entity resolution and survivorship for finance data</title><link>https://www.hzy230051.com/tech/one-customer-five-systems/</link><guid isPermaLink="true">https://www.hzy230051.com/tech/one-customer-five-systems/</guid><description>CRM, contracts, ERP, billing and KYC each have their own idea of who the customer is. How I model a canonical party, match records without guessing, pick survivorship rules per attribute, and prove the result is right.</description><pubDate>Sun, 18 May 2025 00:00:00 GMT</pubDate><category>entity resolution</category><category>customer data</category><category>master data</category><category>dbt</category><category>finance data</category></item><item><title>As-of vs. as-was: bitemporal modeling for financial reporting</title><link>https://www.hzy230051.com/tech/as-of-vs-as-was-bitemporal-finance/</link><guid isPermaLink="true">https://www.hzy230051.com/tech/as-of-vs-as-was-bitemporal-finance/</guid><description>Late prices, backdated deposits and restatements mean &quot;what was the balance on December 31?&quot; has two right answers. How to model both, and why auditors care which one you show.</description><pubDate>Sun, 09 Mar 2025 00:00:00 GMT</pubDate><category>bitemporal</category><category>finance data</category><category>dbt</category><category>data modeling</category><category>audit</category></item><item><title>Ledger-first: a data architecture for a crypto custodian</title><link>https://www.hzy230051.com/tech/ledger-first-data-architecture/</link><guid isPermaLink="true">https://www.hzy230051.com/tech/ledger-first-data-architecture/</guid><description>Every finance number should be derivable from one immutable, double-entry ledger. How to model custody, balances and valuation so that dashboards are just views and reconciliation is a test.</description><pubDate>Sun, 26 Jan 2025 00:00:00 GMT</pubDate><category>data architecture</category><category>ledger</category><category>crypto custody</category><category>dbt</category><category>reconciliation</category></item></channel></rss>