When the AI is asked, be the answer.

When your customers ask an AI assistant who to hire, the answer is drawn from structured records. Most businesses don’t have one. This sprint builds it.

₹15,000 – ₹35,000 · fixed · 1 – 2 weeks · verification artifacts included

What’s included — the definitive list.

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Entity architecture: a coherent machine-readable record of who you are, what you do, and where — the thing AI search engines cite when they recommend a business.

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Schema architecture: JSON-LD structured data across your site — organisation, services, offers with prices, FAQs — validated against Google’s Rich Results requirements.

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llms.txt and a full machine-context file, so AI engines that fetch one document get your whole operating record in one request.

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AI-search and directory presence: the listings and profiles that feed assistant answers, established or corrected under your control.

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Verification artifacts: validator outputs, fetch logs, and the exact queries to run — so you can check the work yourself rather than take my word for it.

What’s explicitly not included — also in writing.

No content farms, no link schemes, no invented reviews — visibility built on records that aren’t true doesn’t survive contact with a fact-checking model, and I won’t write them. Paid advertising and ongoing content production are separate disciplines. A rebuild of your website itself is the Working Build, not this sprint.

Proof — STUDIRT.

The entity work shipped for STUDIRT is checkable today: structured data on every page of the storefront, a published llms.txt, and a Wikidata entity tying the records together. Ask an AI assistant about STUDIRT and the answer comes from records this practice built. Read the case file →