Civico runs programmes that put senior leaders on a real AI problem. To do that well we need to know the market they are working in. So we are building an open research library, and this is how it is put together.
Six views, one set of rows
- Funding Map. Which AI startups are being funded: company, round, amount, date, investors, workflow, industry and the page that says so.
- AI Radar. Technologies and application layers across models, agents, data, applications and infrastructure, scored for maturity and momentum.
- Workflow Map. Thirty-six business workflows and what is being disrupted in each, and by whom.
- Use cases and Industry heatmap. Named deployments, surveys, filings and case studies, which show which industries AI and agents disrupt most.
- Agentic tracker. Dated benchmark results, with the primary source for each point, showing what agents can reliably do.
Three rules
- Every row has a source we opened. No invented companies, amounts, dates or investors. If a source does not say, the field stays blank.
- Scores are computed, not typed. Momentum, attractiveness and the disruption signal are calculated at build time from the rows, so anyone can reproduce them from the data files.
- Judgements are written down. Maturity is a person’s call, with a reason and a review date. Until then an item says “Not yet judged”.
Where we are
The structure is live and the first sourced research pass is still to come, so the views show the framework rather than findings. Each weekly Insight from here will say what changed and link to the rows behind it.