Resources · 15 pages

Resources, for people deciding.

Guides, explainers, comparisons and a glossary, written for the person who has to choose — and who will be asked to defend the choice to someone who has not read the vendor pitch.

Shipped & in build · 13

Our own · coming soon

yourOS

An AI operating system, with Roxy at its heart.

Live · iOS · Android

Sonora

AI sound therapy for Cognitive AI.

Five platforms

Podswap

A growth engine with no human in the loop.

Prototype · closed beta

unOpen.AI

A private assistant with permanent memory.

Live

Coherence

Identity and motion, generated with agents.

Live

Domain Booth

A domain brokerage, rebuilt from the mark up.

Domain landing page · Built

Eclipse.com

One day, one lunation

Domain landing page · Live

ZeroOne.com

The whole page is a switch

Domain landing page · Live

Altitude.com

A climb through the atmosphere

Domain landing page · Built

Coral.com

Field notes on a living word

Domain landing page · Live

Ascend.com

Registered in 1990, once Nokia’s

Domain landing page · Built

Protocol.com

The route is the P

Domain landing page · Built

Engineer.ai

The one-word .ai for coding

01 / 13

All our work →

At a glance

Read it, then decide.

01 What is here: Practical guides, technical explainers, comparisons, frameworks and a glossary, grouped by what you are trying to decide.
02 Who it is for: The person who has to choose, and who will be asked to defend the choice to someone who has not read the vendor pitch.
03 How it stays current: Every resource is listed from the site’s registry, so a new explainer appears in the index without a hand-edit.
04 The next step: If a page describes your problem, the next step is a conversation about your numbers, not a deck.

START FROM YOUR ROLE

Start from your role.

Pick the one that describes you. Each path is a reading order over the pages on this site.

Pick one role

Each path is in reading order. Every page in it is also listed in the index below.

Vendor comparison

Compare vendors on what they have shipped.

Start with the questions that separate a vendor who has shipped from one who has only demoed, then check the build and agency calls before you shortlist.

1Read how to write an AI RFP: the questions that separate shipped from demoed, and what to ask for instead of a fixed price.
2Decide whether an off-the-shelf product or a custom build fits: Build vs Buy AI.
3Weigh an AI agency against an in-house team on cost and time.
4Check the model choice against your constraints, not against benchmarks: How to Choose a Model.

Design

Build it on a design you can defend.

Learn the mechanism well enough to reason about why a system fails, then the orchestration and retrieval choices that follow from it.

1Learn enough of the mechanism to reason about failure: How LLMs Actually Work.
2Learn what a context window really costs: Tokens & Context Windows.
3See how routing, tool use and multi-step reasoning fit together: LLM Orchestration Explained.
4Decide when to retrieve and when to fine-tune: RAG vs Fine-Tuning.
5Understand why prompt injection is not solved, and how to design an agent that fails safely: Prompt Injection Explained.

Procurement

Put the case in writing before the review.

A procurement review asks what it costs, where the models run and what data leaves the building. These pages answer those in the order a review asks them.

1Check whether the data can support the system before you commission it: Is Your Data Ready for AI?
2Model the full cost, including the lines usually left out: Modelling the Cost of an AI System.
3Decide where the models run, on premise or in the cloud: On-Premise vs Cloud AI.
4Threat-model agentic systems for prompt injection, tool abuse and excessive agency: AI Agent Security for Enterprise.

Business case

Build a case that survives the costs usually omitted.

Start with the framework for a defensible case, then test the cost model and the build-or-buy and agency-or-in-house calls it depends on.

1Build the case with the framework that includes the costs usually omitted: The ROI of AI Automation.
2Build a cost model that survives production, including the lines usually left out: Modelling the Cost of an AI System.
3Test the build-or-buy call against the case: Build vs Buy AI.
4Compare an AI agency with an in-house team on real cost and time: AI Agency vs In-House Team.

KNOWLEDGE INDEX

Start from the question you are asking.

Filter

01 Practical guide

What are AI agents?
What separates an agent from a chatbot or a workflow, in practical terms.

Read the guide
02 Technical explainer

LLM orchestration explained
How routing, fallback and abstraction reduce lock-in and token cost — and what they cost in return.

Read the explainer
03 Comparison

RAG vs fine-tuning
Which approach fits which problem: the cost, latency and accuracy trade-offs, side by side.

Read the comparison
04 Framework

The ROI of AI automation
A framework for building a defensible business case, including the costs usually omitted.

Read the framework
05 Trust & compliance

AI agent security for enterprise
Prompt injection, data leaving the perimeter and excessive agency — the controls, and designing agent workflows your own security audit can evidence.

Read the guide
06 Reference

AI glossary
Plain definitions of the terms that appear in AI vendor conversations.

Open the glossary

EVERY RESOURCE

Every resource.

RES.01

What’s New at QQuantum.ai

A dated record of new QQuantum.ai pages and meaningful content updates, with direct links to the technology guides, services and resources that changed.
Open page
RES.02

September 2026 AI Technology Briefing

AI developments reviewed through 19 September 2026: DeepSeek V4.1, Claude Fable 5.1, managed agents, voice and agent interoperability, with practical customer priorities.
Open page
RES.03

What Are AI Agents?

What separates an agent from a chatbot or a workflow, in practical terms.
Open page
RES.04

LLM Orchestration Explained

How routing, tool use and multi-step reasoning fit together in a production system.
Open page
RES.05

ROI of AI Automation

How to build a defensible business case for an AI project, including the costs usually omitted.
Open page
RES.06

AI Agent Security for Enterprise

Threat model for agentic systems: prompt injection, tool abuse and excessive agency.
Open page
RES.07

How LLMs Actually Work

Tokens, attention, training and sampling — enough mechanism to reason about why a model failed.
Open page
RES.08

Tokens & Context Windows

What a token is, what a context window really costs, and why "it fits" is not the same as "it works".
Open page
RES.09

Modelling the Cost of an AI System

Building a cost model that survives contact with production, including the lines usually left out.
Open page
RES.10

How to Choose a Model

A decision procedure for model selection that starts with constraints rather than with benchmarks.
Open page
RES.11

AI Glossary

Plain definitions of the terms that appear in AI vendor conversations.
Open page
RES.12

Prompt Injection Explained

Why prompt injection is not solved, what actually reduces the risk, and how to design an agent that fails safely.
Open page
RES.13

Is Your Data Ready for AI?

How to tell whether your data can support the system you want, before you commission it.
Open page
RES.14

How to Write an AI RFP

The questions that separate vendors who have shipped from vendors who have demoed, and what to ask for instead of a fixed price.
Open page

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