VeritasLinks public report

Your buyer perception splits into two modes with no single clear signal

VeritasScore 629 on a 300 to 870 scale.

Mention rate: 68.8% of answers name Context.dev.

You reach many AI answers with 57 percent visibility and 69 percent share of voice yet buyers rarely choose you.

The 629 VeritasScore shows strong discovery but weak conversion because trust signals stay thin and key enterprise queries go unanswered.

You're in the answer 68.8% of the time.

Across 234 answers from 6 models, Context.dev came up 161 times. When the models talk about you, 95.0% of what they say is favorable.

AI tells one favorable story about you — with uneven conviction.

We placed 310 AI answers by how favorable they are and how much conviction they carry. Most answers sit in Favorable / muted (polarization 0.62) — conviction varies more than tone.

You are named in 69% of answers. Apify appears in 35% of rival-comparison answers.

Your mention rate is 69%, ahead of Apify, ScrapingBee, ScraperAPI on your mention rate. These rival figures are mention rates in competitive prompts, not shares of one pie — they do not need to sum to 100%.

AI sees you. It doesn't fully trust you yet.

Your authority score is 67 out of 100, and citation quality is 58 (fair).

In the full report

  • missed_demand (10) — Buyer questions where models name competitors instead of you, with modeled monthly value at risk and fix steps.
  • buyer_panel (1) — Simulated buyer-panel rounds, objections, and choice shares across personas.
  • investor_deck — How models score your deck and the red flags they raise.
  • action_plan (5) — Prioritized fixes, impact notes, and a 4-week schedule to move the score.
  • more_answers (57) — Additional model answers beyond the free sample shown on the public page.
  • more_models (5) — Full multi-model breakdown beyond the free preview models.
  • split_perception — Model-by-model score grid and where assistants disagree about you.
  • over_time — Score and visibility trends across previous runs.

Unlock the full report · Run your own analysis · JSON · Markdown

VeritasLinks public report

Your buyer perception splits into two modes with no single clear signal

VeritasScore 629 on a 300 to 870 scale.

Mention rate: 68.8% of answers name Context.dev.

You reach many AI answers with 57 percent visibility and 69 percent share of voice yet buyers rarely choose you.

The 629 VeritasScore shows strong discovery but weak conversion because trust signals stay thin and key enterprise queries go unanswered.

You're in the answer 68.8% of the time.

Across 234 answers from 6 models, Context.dev came up 161 times. When the models talk about you, 95.0% of what they say is favorable.

AI tells one favorable story about you — with uneven conviction.

We placed 310 AI answers by how favorable they are and how much conviction they carry. Most answers sit in Favorable / muted (polarization 0.62) — conviction varies more than tone.

You are named in 69% of answers. Apify appears in 35% of rival-comparison answers.

Your mention rate is 69%, ahead of Apify, ScrapingBee, ScraperAPI on your mention rate. These rival figures are mention rates in competitive prompts, not shares of one pie — they do not need to sum to 100%.

AI sees you. It doesn't fully trust you yet.

Your authority score is 67 out of 100, and citation quality is 58 (fair).

In the full report

  • missed_demand (10) — Buyer questions where models name competitors instead of you, with modeled monthly value at risk and fix steps.
  • buyer_panel (1) — Simulated buyer-panel rounds, objections, and choice shares across personas.
  • investor_deck — How models score your deck and the red flags they raise.
  • action_plan (5) — Prioritized fixes, impact notes, and a 4-week schedule to move the score.
  • more_answers (57) — Additional model answers beyond the free sample shown on the public page.
  • more_models (5) — Full multi-model breakdown beyond the free preview models.
  • split_perception — Model-by-model score grid and where assistants disagree about you.
  • over_time — Score and visibility trends across previous runs.

Unlock the full report · Run your own analysis · JSON · Markdown