Insights / AI & Automation
Articles, case study summaries, and strategy guides focusing on AI & automation in the Nepal and global markets.

Master ai citation share to client leads by tracking attribution blind spots, structuring llms.txt files, and measuring pipeline ROI.

One checkable fact, a named human, real questions, and the right schema. What actually makes a release extractable in 2026 — including the FAQ markup change most advice has not caught up with.

Assistant referrals look like a rounding error while page-one buying questions earn no clicks. Four measurement methods, what each one misses, and the three report splits that change what you work on next.

We re-analysed 18,151 cited pages across ChatGPT, Google and Perplexity. Relevance, structure and evidence were associated with deeper use, but they are not magic ranking factors.

The llms.txt spec was revised in August 2026. We measured 25 major sites on 13 August: 18 publish a file, but only 2 have implemented the discoverability relations v2 added, and none ship them in HTML. Here is how to write one properly.

We measured thirteen competing websites for AI search readiness on 1 August 2026 and re-checked them on 12 August. One published an llms.txt, eight had no structured data, and the only site restricting an AI crawler was one we built.
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