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Engineering Notes

Design decisions and measurements from building an on-premises AI for work — how the principle of enforcing with code, guiding with instructions, and verifying with audits is implemented.

Part 1

Design principles and architecture

The design principles behind the whole series, and the execution structure every AI feature shares.

Part 2

Structured data and NL2SQL

Turning natural-language questions into accurate SQL against business databases.

Part 3

Database connections and OwlDB

How the connection to a customer database is opened and protected: SSH tunnels through a bastion, encryption in transit and certificate checks, importing Amazon RDS and Supabase connections on SaaS, and how execution works for databases connected through OwlDB.

Part 4

Memory and search

Memory design that lets individual and team knowledge improve answers, plus document search, deep research, and source design.

Part 5

Conversation engine

The internals that keep conversations running: multi-turn, caching, compaction, and messages sent mid-turn.

Part 6

Tools and execution

Workflow automation, data movement, tool design, document and diagram generation, and subagents.

Part 7

Voice and meetings

Meeting recording, speech recognition, meeting glossaries, minutes, and audio retention.

Part 8

Security and audit

How the principle of blocking deterministically, guiding with instructions, and confirming through audit is implemented: egress control, injection defense, personal data masking, a shared output boundary, credentials, sandboxing, approval gates, external tool policy, and permissions and audit.

Part 9

Installation, operations, and quality

Technical reference for on-premises adoption, from installation and air-gapped transfer to updates and operational checks, plus the principles that keep an installable AI product's quality.