Insights
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 8, 2026
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.
Read the postSeptember 22, 2026
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.
Read the postSeptember 15, 2026
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.
Read the postSeptember 6, 2026
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.
Read the postSeptember 5, 2026
AI can ship branches faster than a junior. Your git hygiene has to keep up — or Monday becomes archaeology.
Read the postAugust 24, 2026
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.
Read the postAugust 18, 2026
Why growing companies should ground generative AI in their own documents and systems — and how implementation keeps assistants useful after the demo.
Read the postAugust 11, 2026
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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