Names, locations, and the source trail stay in context while you investigate.
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Find Jordan Avery from Northstar Labs in Austin.
I found three public profiles. One matches the employer, city, and recent work history across independent sources.
Which details appear on more than one page?
The Austin location, Northstar role, and two recent public appearances agree. I kept the other candidates separate.
Show me the source trail.
Here’s the strongest sourced match:
Likely matchJordan Avery
12 public sourcesNow check Maya Chen at Stanford and recent climate conferences.
One profile appears on the lab roster and two 2026 speaker programs. The other candidates do not share those signals.
Any conflicting details?
DeepSearch compares the public pages it finds without merging uncertain matches.
Ask another question whenever you need without losing the evidence trail.
Important details stay linked to the public pages that support them.
Similar names remain distinct until the public evidence supports a match.
Conflicting or missing details remain clear instead of being smoothed over.
Results are built from publicly available pages you can open and review.
Return to the result, review its sources, and continue from the same context.
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People researching with AI
I started with a name and a few details. DeepSearch pulled the profiles, mentions, and source links into one page I could reason through.
The source links made the difference. I could follow the trail behind each detail before I shared the result with my team.
The AI answers were useful because every claim pointed back to a public source. I did not have to trust a black-box summary.
DeepSearch gave me a useful answer quickly, but still left the final call with me. That balance is exactly what I needed.
Similar matches stayed separate, and every important detail kept its source link. That made the final review much easier.