Resources
AI business resources drawn from our reporting, including frameworks, checklists and definitions you can use. Everything here comes from a published story, nothing is gated, and nothing is sponsored. This page collects the reusable parts of our reporting so you do not have to dig through an article to find the one paragraph you needed.
Each framework below links back to the full story it came from, with the sourcing and evidence behind it. The glossary further down defines the terms we use most often, written the way an operator would use them rather than the way a provider’s marketing page would.
Resource Library
eCommerce ROI Dashboard
Calculate the conversion lift required for an interactive-video deployment to recover its costs within your modeled period.
Open the DashboardResource Library
The Calibrated Autonomy Checklist
The four-question test, sector rules, and a ten-point checklist for AI agent deployment.
Download the PDFFrameworks you can run today
The four questions to ask an AI contract review provider
Ask for clause level performance, ask which categories perform worst, route those to a human on purpose, and test on your contracts rather than the demo set.
Read the framework »Four checks before you trust an AI investing number
Is it backtested or realised, how many variants were tested to produce it, is the firm registered, and does the tool disclose what it cannot see.
Read the framework »How to audit a brand safety blocklist for overblocking
A practical audit for working out how much legitimate inventory your keyword list is quietly removing, and what it is costing in reach.
Read the audit »Five steps a small seller can take against copycats
What you can do yourself with free tools before paying for a brand protection platform, and the documentation that makes escalation work.
Read the steps »How to forecast an AI bill before it arrives
Why output tokens cost several times more than input, how context windows inflate spend, and the habits that keep a budget honest at volume.
Read the guide »Plain English glossary
The terms that come up most often in our reporting, defined the way an operator would use them rather than the way a provider would.
- Token
- The unit an AI model reads and writes in, usually a fragment of a word. It is how providers measure work and how they bill you.
- Context window
- The model’s working memory for a single conversation. Once it is full, the oldest material is dropped or the request fails.
- Input vs output pricing
- Generating text costs far more compute than reading it, so output is typically billed at three to five times the input rate.
- Backtest
- A strategy tested against historical data. It shows what would have happened, not what did happen, and it can be tuned until it looks good.
- Realised return
- What an approach produced with real money at risk. Very different evidence from a backtest.
- Clause level accuracy
- How well a contract tool performs on each type of clause, rather than one blended score across all of them.
- Blocklist
- Keywords advertisers use to avoid unwanted placements. Set too broadly, it removes legitimate inventory along with the harmful kind.
- Hallucination
- Fluent output that is not supported by any source. The risk is that it looks right, not that it looks wrong.
Why these AI business resources exist
Most publications treat a framework or a glossary as filler to pad out a resources section. Ours works the other way. Every entry here started as a tool we needed to write an investigation, and we only publish it separately once we know it holds up outside the original article. That is the same discipline behind reference resources like the FTC’s guidance on AI and algorithms, which exists to give a testable standard rather than a marketing pitch.
If you use one of these frameworks and find a gap, or a term in the glossary that needs sharpening, tell us. These resources get revised as our reporting turns up better evidence, not on a fixed schedule.
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