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Company Research Should Keep Missing Fields Missing

#ai

Company research automation has a subtle failure mode: when a source does not return a field, an AI system fills the gap with something that merely sounds plausible. The table looks complete, but the result becomes harder to audit.

Separate three states

A reviewable data model should distinguish:

  • verified — backed by a traceable source;
  • inferred — derived from evidence, with the reasoning chain retained;
  • missing — no reliable source found, so the field stays empty.

That distinction matters in sales preparation, supplier screening, and competitive research because downstream users need to know when manual verification is still required.

What a sanitized Stripe sample returned

Using Stripe and stripe.com as inputs, a sanitized Company Intelligence sample returned the official website source, a Fintech classification, a business description, a Next.js technology signal, and official LinkedIn, X, and GitHub entries.

Headquarters, founding year, founders, and Y Combinator batch were not reliably returned, so they remained explicitly missing. A dataset quota limitation was also disclosed instead of being hidden.

Five checks for a trustworthy pipeline

  1. Can every important field be traced back to a source?
  2. Are facts, inferences, and unknowns represented separately?
  3. Are missing values preserved instead of auto-filled?
  4. Are timestamps, quotas, and coverage limits visible?
  5. Can another researcher reproduce the check from the same inputs?

A useful field schema is closer to { value, status, source, observed_at } than a bare value. This makes upstream changes and disagreements diagnosable.

A complete table is not necessarily reliable. In automated company research, an honest blank is often more professional than a confident guess.

Reproduce the sample input here: https://mce.best/explore/688637636463284224

Disclosure: HSH provided and authorized this sanitized sample output. It is not a completed MERVYX customer order, paid case study, or accepted delivery, and it does not promise completeness or business results.

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