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How we think about AI and ML development and implementation, data, and the operating cadence around them. Written so operators and owners can use it — and so search engines can find it.

October 23, 2026
Dirty data breaks automation, migrations, and reports. Cleanse before you cut over.
Automations, ETL jobs, and cutovers fail the same way when source data is messy — duplicate customers, blank keys, mismatched units. Here is what goes wrong, the practical DBA steps we take to cleanse first, and why accurate reporting depends on it.
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October 8, 2026
Your database is slowing the business down. Here is why performance needs an owner.
Slow queries cost staff time, trust in reports, cloud spend, and sales. Here is what active database performance management looks like (monitoring, tuning, maintenance, capacity planning) and when to bring in a DBA.
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September 22, 2026
How AI helps with small-business back-office work — and how to implement it without the mess
Back-office work is where small businesses bleed time — bookkeeping, invoices, scheduling, inventory, follow-ups, documents, and reports. Practical AI on your own data can cut that grind. Here is how PelicanSoft helps implement tools that stick.
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September 15, 2026
A data warehouse plus custom ML is how smaller companies grow without guessing
Spreadsheets do not scale past the person who built them. A durable warehouse with custom ML on your own data lets mid-size teams forecast, catch drift, and move faster — without a data-science department.
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September 6, 2026
When AI ships the app, IT’s job is to shepherd
Users can stand up tools overnight with AI. IT’s job is to shepherd what gets built so the company still has one stack, one set of numbers, and a reverse path.
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September 5, 2026
Managing code repositories when AI writes the code
AI can ship branches faster than a junior. Your git hygiene has to keep up — or Monday becomes archaeology.
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August 24, 2026
AI and ML implementation for growing companies
How small and mid-size companies get value from AI and machine learning without a data-science department — by implementing models on the data they already have.
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August 18, 2026
Generative AI that answers from your data, not the open internet
Why growing companies should ground generative AI in their own documents and systems — and how implementation keeps assistants useful after the demo.
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August 11, 2026
Why machine learning projects die in notebooks
Most ML work never reaches production. Here is how implementation, MLOps, and a smaller first job keep models alive after the pilot.
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