AI People Search
AI People Search for Talent and Business Discovery
Turn a natural-language brief into reviewable criteria and inspect the public professional evidence behind each match. RightWho is built for talent and business discovery, not consumer identity lookup or facial recognition.
Example query
“Find ML engineers who have shipped recommendation systems”
Search criteria
What the search evaluated
Search summary
Shipped Recommendation Systems Search
Results
Reviewed result rows
Fully matched
Recommendation Engineer 01
Machine Learning Engineer · Large consumer video platform
Shipped recommendation and search ranking changes at consumer scale, including reviewed gains in engagement, click-through rate, and revenue.
Public source: LinkedIn
Review match evidence
This row was grouped from the public professional evidence available when the search snapshot was created. Verify the original source, scope, and recency before making a hiring or outreach decision.
Personalization Engineer 02
Staff Machine Learning Engineer · Global marketplace platform
Built multi-task ranking and personalized search systems across large marketplace and professional-network products.
Public source: Public Talent Pool
Review match evidence
This row was grouped from the public professional evidence available when the search snapshot was created. Verify the original source, scope, and recency before making a hiring or outreach decision.
Ranking Platform Engineer 03
Senior Machine Learning Engineer · Global retail platform
Owned recommendation pipelines from model features through online serving and revenue experiments across retail and knowledge products.
Public source: LinkedIn
Review match evidence
This row was grouped from the public professional evidence available when the search snapshot was created. Verify the original source, scope, and recency before making a hiring or outreach decision.
Recommendation Infrastructure Engineer 04
Machine Learning Platform Engineer · Streaming and delivery platform
Delivered high-throughput, low-latency recommendation infrastructure for multiple consumer products.
Public source: LinkedIn
Review match evidence
This row was grouped from the public professional evidence available when the search snapshot was created. Verify the original source, scope, and recency before making a hiring or outreach decision.
Commerce Ranking Engineer 05
Machine Learning Technical Lead · Short-video commerce platform
Shipped production ranking and recommendation systems spanning content, advertising, and shopping experiences.
Public source: Public Talent Pool
Review match evidence
This row was grouped from the public professional evidence available when the search snapshot was created. Verify the original source, scope, and recency before making a hiring or outreach decision.
Retail Personalization Lead 06
Machine Learning Lead · Global apparel retailer
Led a multi-person team that deployed a retail recommender and validated commercial uplift through controlled experiments.
Public source: LinkedIn
Review match evidence
This row was grouped from the public professional evidence available when the search snapshot was created. Verify the original source, scope, and recency before making a hiring or outreach decision.
Large-Scale Ranking Engineer 07
Recommendation Systems Engineer · Global consumer internet company
Owned retrieval, ranking, and online experimentation for recommendation products serving hundreds of millions of users.
Public source: LinkedIn
Review match evidence
This row was grouped from the public professional evidence available when the search snapshot was created. Verify the original source, scope, and recency before making a hiring or outreach decision.
Marketplace Recommendation Engineer 08
Senior Data Scientist · Fashion marketplace
Shipped ranking and recommendation improvements with an approximately eight-percent gain in an offline relevance metric.
Public source: LinkedIn
Review match evidence
This row was grouped from the public professional evidence available when the search snapshot was created. Verify the original source, scope, and recency before making a hiring or outreach decision.
Notification Personalization Engineer 09
Machine Learning Engineer · Professional network platform
Built notification-personalization systems and productionized dozens of models through a recurring deployment pipeline.
Public source: Public Talent Pool
Review match evidence
This row was grouped from the public professional evidence available when the search snapshot was created. Verify the original source, scope, and recency before making a hiring or outreach decision.
Commerce Recommendation Engineer 10
Senior Machine Learning Engineer · Online commerce company
Delivered an online recommendation service at production throughput with reviewed conversion and revenue-impact claims.
Public source: LinkedIn
Review match evidence
This row was grouped from the public professional evidence available when the search snapshot was created. Verify the original source, scope, and recency before making a hiring or outreach decision.
An anonymized editorial subset of a completed 37-result search: fourteen fully matched, eight partially matched, eight weak-match, and seven unscored profiles. Ten evidence-rich, identity-unique fully matched rows were selected after human review. Find More was not used because the initial run already exceeded the ten-row publication target. Names, exact employers, profile links, contact details, and unreviewed fields are intentionally omitted. AI-generated avatars are illustrative and do not depict the people represented in these rows.
Market Landscape
Context for how teams search and evaluate talent today.
Query format
Natural language
Reasoning step
Evaluation Blueprint
Validation unit
Evidence per match
Result format
Ranked profiles
Key Trends
Start with a hiring outcome and turn it into reviewable qualification criteria
Use semantic retrieval to broaden discovery, then require evidence before ranking a match
Show why each person matched so recruiters can challenge or refine the criteria
Keep human review in the loop before outreach or hiring decisions
Common Misconceptions
AI people search always means finding a private individual or matching a face
RealityThe term covers different products. RightWho focuses on professional discovery for talent and business workflows using available public professional evidence; it is not a consumer identity lookup or facial-recognition service.
More contacts in a database = better people search
RealityDatabase size does not guarantee relevance. Useful people search must interpret the requirement, show why each result matched, and make missing evidence visible to the reviewer.
AI people search is just keyword search with a chatbot wrapper
RealityTrue AI people search involves semantic understanding (interpreting intent), evidence gathering (validating capabilities), and intelligent scoring (ranking by actual fit). RightWho builds an evaluation Blueprint before searching — a fundamentally different approach from keyword matching.
LinkedIn is enough for finding anyone
RealityProfessional profiles are useful context, but technical work often appears elsewhere. RightWho supplements profile information with public code, technical writing, talks, and project evidence.
How RightWho Works
Not keyword matching. Not database filtering. Evidence-driven intelligence.
Blueprint Generation
When you describe "ML engineers who have shipped recommendation systems", RightWho does not simply search for profiles containing those keywords. It builds a Blueprint covering recommendation delivery, retrieval and ranking depth, production ownership, and measurable outcomes, then searches for evidence matching each dimension.
Evidence We Collect
- Code contributions and open-source activity (GitHub, GitLab)
- Published work and technical writing (papers, blog posts, conference talks)
- Professional trajectory and role progression (LinkedIn, company announcements)
- Social proof and peer recognition (recommendations, citations, community standing)
- Content creation and thought leadership (videos, podcasts, newsletters)
- Business outcomes and impact signals (company growth, product launches, press mentions)
3D Profile Dimensions
- Verified professional experience and role history
- Demonstrated technical or domain expertise (evidence-backed)
- Public reputation and peer recognition
- Content and thought leadership presence
- Network and influence signals
- Activity recency and career momentum
Illustrative Evaluation Framework
Adapt these dimensions and weights to the role, then review the evidence behind each match.
Semantic Match to Intent
35%Signals
Sources
Evidence Strength
30%Signals
Sources
Source Coverage
15%Signals
Sources
Evidence Context
10%Signals
Sources
Uncertainty Review
10%Signals
Sources
Evidence Gaps to Verify
- A specific profile claim conflicts with the linked source or project record
- Available examples do not support the stated scope, ownership, or outcome
- Keyword-stuffed profiles with no corresponding real work
- Available sources are stale or omit the context required for the claim
- Contradictory information across different platforms
Supporting Evidence
- Multiple relevant sources support the same role, contribution, or outcome
- Recent public work is directly related to the search criteria
- Relevant collaborators corroborate the person’s contribution
- Outcomes are described with enough context for a reviewer to assess them
- Source dates and links make the evidence easy to inspect
Example Workflow
An illustrative workflow showing how evidence-first sourcing supports a shortlist.
Scenario
Illustrative search for an engineering leader with distributed systems experience
The Challenge
A title search returns engineering leaders with very different technical backgrounds, while the phrase "distributed systems" alone does not prove production experience.
RightWho's Approach
RightWho builds a Blueprint covering system scale, leadership scope, and technical specificity such as consensus protocols or data replication, then looks for supporting work in public code, talks, and technical writing.
Outcome
The shortlist separates title fit from evidence fit and gives recruiters direct sources to review before outreach. Criteria can be adjusted when the evidence is too broad or too narrow.
Frequently Asked Questions
What is AI people search?+
AI people search uses natural-language understanding and structured evaluation to find people relevant to a goal. In RightWho, that goal can include recruiting, investor discovery, buyer research, expert search, or partner discovery, and results are supported by available public professional evidence.
Is RightWho a face-search or consumer people-finder service?+
No. RightWho is designed for professional talent and business discovery. It evaluates available public professional evidence against reviewable criteria; it does not identify people from faces or provide private-address lookup.
What makes RightWho different from Apollo, ZoomInfo, or LinkedIn Recruiter?+
These products support different workflows, including contact databases or searches within a professional network. RightWho starts from a natural-language brief, builds reviewable criteria, and organizes available public evidence behind each match. Coverage and capabilities should be compared against the current product documentation for each tool.
How does the Blueprint system work?+
When you describe who you need, RightWho turns the brief into a structured set of requirements and evidence criteria. Reviewing those criteria before relying on the results helps expose ambiguous requirements and makes the ranking easier to challenge.
What data sources does RightWho search?+
RightWho uses publicly available professional sources relevant to technical work, including code repositories, personal sites, company pages, technical writing, conference material, and publications. Source coverage varies by query and by what a person has made public, so every result exposes the evidence available for review.
Is RightWho only for recruiting?+
No. Recruiting is the initial growth focus, but the same People Model and Evidence Criteria workflow can support searches for investors, early users, partners, experts, and key buyers. The useful evidence changes by use case, and every result still requires human review.
How accurate are the results?+
Result quality depends on the clarity of the brief and the public evidence available for that person and use case. RightWho shows the evidence used for the match so reviewers can validate it, report incorrect information, and refine the criteria.
Can I use RightWho for outreach after finding people?+
Yes. Smart Pitch can draft outreach using the reviewed evidence associated with a result. The sender remains responsible for checking accuracy, tone, consent, and applicable outreach requirements before sending.
This page presents an editorially anonymized subset of a real RightWho search using public professional information and reviewable qualification criteria.
Limitations: Only reviewed rows are shown. Public evidence can be incomplete or outdated, unscored rows are excluded, and search results must not be treated as an automated hiring or outreach decision.
Explore AI People Search by Goal
Search Smarter. Review the Evidence Behind Each Match.
Describe who you need, then review the available public evidence, source limitations, and gaps behind each suggested match.
Try Free Search