Architecture & product

AI Systems

The Hard Part of AI Engineering: APIs, Retries and Idempotency

Engineering field notes on REST contracts, webhook delivery, job idempotency and ambiguous AI-provider outcomes in MailTrace and Instant Deck.

Architecture & product

Engineering note · Part 1

Building MailTrace: Turning Email Correspondence into a Searchable Evidence Workspace

How MailTrace connects correspondence, evidence, issues and saved answers without losing their source history.

Architecture & product

Engineering note · Part 2

Where Deterministic Code Ends and AI Begins in MailTrace

A practical boundary between model interpretation and the backend rules that protect source evidence.

Architecture & product

Engineering note · Part 18

Making AI Processing Understandable in the MailTrace Interface

Turn backend states into clear actions, saved progress and honest explanations for people working with evidence.

Architecture & product

Architecture proposal · Part 31

Using Pretrained AI Versus Training Models with TensorFlow or PyTorch

Explain where deep-learning frameworks could fit without implying that every AI product needs its own neural-network training stack.

Architecture & product

Architecture proposal · Part 32

Do MailTrace’s Workflows Need LangGraph, CrewAI, AutoGPT or Bee?

Evaluate orchestration frameworks against durable jobs, explicit tool permissions and the existing application workflow.

Architecture & product

Architecture proposal · Part 34

Designing One Evidence Backend for Web and Android

Share evidence rules and saved jobs across clients while giving Android an interface suited to mobile use.

Architecture & product

Architecture proposal · Part 35

What a Year of MailTrace Iterations Taught Me About AI Product Architecture

Why consolidation needs maintained capability owners, explicit contracts and workflow tests rather than another collection of copied modules.

Agentic AI

How to Design Reliable Agentic AI Workflows

Practical lessons from building Open Tools: bounded agents, explicit permissions, durable task memory, validation gates and deployment checks.

AI Product Design

Human-in-the-Loop AI for Sensitive Workflows

Why AI outputs should often remain drafts until reviewed, and how structured schemas, confidence and confirmation improve trust.

AI Product Engineering

From AI Prototype to Production Product: What Changes After the Demo Works

A practical production checklist from DDDecks.ai: observability, error contracts, security, failure handling, data boundaries and deployment.

Generative AI

AI Document Generation Beyond Prompt Engineering

Lessons from Instant Deck on evidence extraction, research, memo synthesis, narrative planning, visual planning and controlled rendering.

AI Strategy

Building AI Products Around Real Business Workflows

A cross-project framework for deciding where AI belongs: workflow mapping, structured context, model boundaries, human review and measurable outcomes.

Human-Centred AI

Designing Risk Dashboards for Human Review

Human-centred principles for alerting, triage and machine-learning outputs in sensitive workflows.

Agentic AI

Observability for AI Agents: What to Log Without Creating a Privacy Problem

A practical observability model for agent runs, tool calls, retries, failures and sensitive data boundaries.

AI Product Architecture

Designing PostgreSQL Data Models for AI Products

Why durable AI products need explicit entities, source records, artifacts and model-run metadata—not just chat history.

Technical Product

Entity Modelling for AI Product Managers

How entities and relationships turn vague AI features into buildable product specifications.

Technical Product Management

Roadmapping AI Products Under Uncertainty

How to prioritise platform capability, configuration and experiments when model capability and customer needs move quickly.

Enterprise AI

AI POC to Enterprise: A Practical Product Checklist

What has to change when an AI proof of concept becomes a real multi-user product.