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Insights

Short reads for decision makers bringing AI into the business. Each starts from a question teams actually run into and shows, in three minutes, what gets solved and what is different.

Part 1

Why AI built for work

How AI built for work differs from general-purpose AI, and the questions that frame the whole series.

Part 2

Asking your company database

Connecting company databases safely, asking in plain language, and getting accurate answers: internal and Amazon RDS or Supabase databases, accurate SQL, department-specific definitions, and what security teams ask before a production database is connected.

Part 3

Better with OwlDB

OwlAI alone can connect to company databases and answer questions. Adding OwlDB, a database management solution, brings table-level permissions, protection from heavy queries, standby node use, and connections without credentials. This part covers what improves and how.

Part 4

AI that improves with use

How individual and team knowledge accumulates so answers get better, and how the company keeps control of that memory.

Part 5

Research and delegating work

From researching documents and the web with sources attached, to handing off real work such as recurring reports and documents in company templates.

Part 6

Everyday conversations and meetings

Trust problems that come up in daily use, such as long conversations, instructions added mid-conversation, and wrong answers that are easy to miss, plus the path from meeting recordings to minutes.

Part 7

Security and audit

What is blocked by rules, what is left to AI judgment, and how records confirm it: data egress, hidden instructions, personal data, passwords and tokens, sandboxed code execution, approvals, external connections, and audit records.

Part 8

Adoption and operations

From choosing a deployment model to installation, rollout, updates, and a reference case.