Insights
Winning public transport work, and putting AI to use
Guides, stories and tools on winning public transport work and putting AI to use. Every article is a node in the same brain, and every figure is cited.
Illustration: the Insights library listing published articles, a card showing that every figure on these pages links to its source, and a card showing the pages are built to be read by machines, with structured data, an llms.txt and real headings.
This is the economAIcs library: the practical reading behind the Brain and the Practice, written for the people who bid for, buy and run public transport. Two themes run through it. The first is winning the work: how tenders are scored, where bids are really won and lost, and what a bid team should fix first. The second is putting AI to use in transport without the hype: what is actually real, how to tune it to win bids, and how buyers should prepare for it.
Every figure on these pages is cited, with sources at the foot of each piece, so you can check us. New here? Start with the eight principles for public transport tenders, then read the long-form article behind them. For the product view, bid intelligence is what the Brain does.
Everything, newest first.
How to Win Public Transport Tenders: Lessons From the Bid Table
The win is decided before the writing starts. 10+ years of bid-table lessons on where public transport tenders are really won, and lost.
Read the guide →The Driver and Vehicle Allocation Agent
A free agent build for bus and coach operators: a solver, two graphs and an honest self-improving loop to cut dead mileage and idle driver hours.
CitedStoryCan AI increase your chance of winning a bid?
Yes, but not by using the buyer's AI. Three levers: what their machine can read about you, what yours knows, and whether your bid survives a machine first.
CitedDataThe Invisible Industry
I scored 500 transport companies on whether an AI can find and read them, and checked 30 sites by hand. Most of transport is legible to AI by accident.
CitedGuideHow PPN 017 affects transport bids: AI transparency in central government procurement
PPN 017 replaced PPN 02/24 for central government procurements from February 2025. What it asks of transport bid teams, what it leaves out, what to do now.
CitedStoryHow local transport authorities and operators can use AI in bus franchising: six moves
Franchising hands authorities the revenue, the data and the pen. Six practical AI moves, plus how to make a bus network legible to AI assistants.
CitedGuidePreparing for AI on the buying side
Machine written bids are now mainstream. That changes three things on your desk: volume, similarity and verification. This page is about all three.
CitedGuideTuning AI to Win Transport Bids
Machine writing has been here a while. Machine reading is arriving now. Practical tips to make your business and your bids legible to AI.
CitedGuideAI in UK Public Transport: what's actually real in 2026
Eight AI use cases for UK bus operators and transport authorities, with the delivery reality behind each, written by someone who ships, not sells.
CitedToolThe Public Transport Independent Advisor Agent
A free prompt file that turns any AI tool into an independent advisor for assessing AI claims in public transport.
CitedStoryFranchising. A Modern Day Epic
Every English local transport authority can now franchise its buses. One has completed it, one has rejected it. Published costs and values, with sources.
CitedStoryOut of stealth: YourTransportBrain is live
economAIcs comes out of stealth. YourTransportBrain is AI bid intelligence and compliance built for public transport tendering, live today.
CitedGuideHow to choose AI bid software for transport
How I'd evaluate AI bid tools for transport if I were still an operator, and when not to buy ours. From 10+ years as a commercial lead.
CitedStoryBid software was the wrong idea. Here is what transport bidding actually needs.
Bid software speeds up writing. But writing was never the bottleneck in transport bidding. Intelligence is. Here is why that matters.
CitedEvery claim on these pages is cited, with sources at the foot of each article. The index is evergreen, so it carries no "posted" dates.