AI Product Showcase - Seekout

A live product walkthrough shows how an AI-assisted sourcing platform turns a messy, real-world job description into a working candidate search, without ever comparing that description directly against candidate resumes, a method considered prone to bias.

Starting from a job ad pasted in as-is, the system parses out job title, required and preferred skills, experience level, and education requirements, then builds a fully editable search from those elements. Every decision is shown transparently, so a recruiter can see that "Excel" landed in preferred skills and manually promote it to required if a hiring manager insists it belongs there. Matching candidates are shown with green markers for exact matches, plus additional context, like relevant experience mentioned in a profile that wasn't part of the original search terms, surfacing keywords a recruiter might not have thought to search for.

A demonstration of upcoming conversational AI functionality shows a recruiter typing an ambiguous request, such as narrowing results to people who work at a "Big Four" firm, and watching the system correctly interpret that to mean specific major accounting and consulting firms, then rebuild the search accordingly. Results can be visualized by employer, title, and demographic representation, supporting proactive outreach aimed at underrepresented groups, with the underlying matching model described as highly accurate and complete.

Once a shortlist is built, AI-assisted messaging generates a draft outreach email based on both the candidate's profile and the original job description, intended as a starting point for a recruiter to review and personalize before sending, rather than a finished message for automated bulk campaigns.

Additional capabilities discussed include:

• Cloning a single ideal-candidate profile, or a batch of several, to find similar people rather than relying on a job description alone
• Adjusting search results conversationally, for example by requesting less senior candidates and having the system refine results accordingly
• Unpacking industry shorthand and acronyms, such as company groupings, into their real-world meaning within a search
• Handling requests around experience level and time zone as part of a natural back-and-forth refinement process

The system is described as staying within the bounds of building and refining a structured search rather than acting as an open-ended conversational assistant.