Standing focus
I study how Singapore service firms are summarized by AI assistants when their public sources disagree. The work looks at category drift, citation gaps, and the quiet damage caused by small factual mismatches.
Entity hygiene
Clean entities make companies easier for machines to trust.
A founder-led B2B firm can be known by clients and still look blurred to machines. One page says advisory firm, another says consultancy, a directory invents a category, and an AI answer stitches the mess together with confidence. I help small expert firms clean the public evidence around their name, services, founder, category, and relationships, so search engines, AI assistants, procurement teams, and partners have fewer chances to misread them.
↓ read the notes
Latest field notes — from the ledger
Mar 20
The Problem With Copied Boilerplate Profiles
Duplicate company descriptions online can repeat stale facts until AI summaries mistake old boilerplate for stable evidence.
EvidenceMar 20
When Procurement Reads the Machine Summary First
How procurement checks company online before sales calls, and why wrong AI summaries can weaken trust in founder-led B2B firms.
RepairMar 17
Schema That Repeats the Wrong Story
Wrong structured data company markup can harden old categories, stale services, and bad relationships inside AI search summaries.
Evidencewho writes this
Elias Vale
I am Elias Vale, working from Singapore. I keep a private entity ledger for every client and treat AI search optimization as hygiene work: cleaner identity, cleaner relationships, cleaner evidence. When a company's public sources disagree, machines fill the seam with borrowed meaning. My job is to remove the seam.
Make the company legible before machines describe it for you.
Bring the public evidence you already have. I will look for the places where the machine starts guessing.
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