35% increase in placements from existing ATS database, 50% reduction in external sourcing spend, talent pool utilization from 5% to 40%.
Every recruitment agency sits on a goldmine it barely uses. The average agency ATS contains tens of thousands of candidate records accumulated over years of operation. These are people who have already been sourced, screened, and in many cases interviewed. Yet when a new role lands, the first instinct is to post on job boards and start sourcing from scratch.
The existing database -- which represents millions of dollars in accumulated sourcing investment -- goes largely untapped. The reason is practical, not strategic. Searching an ATS database is painful. Boolean searches return too many irrelevant results or too few. Candidate records are inconsistently tagged, skills are described in different terminology, and there is no reliable way to know which candidates from two years ago are now actively looking. Consultants learn through experience that searching the database often wastes more time than it saves, so they default to fresh sourcing from LinkedIn and Indeed.
This creates a vicious cycle. New candidates are sourced, placed or not, and then added to the database where they are equally likely to be forgotten. The database grows larger and less useful over time. Meanwhile, the agency pays increasing job board fees and LinkedIn Recruiter licenses to find candidates who may already exist in their own system under slightly different keywords. Source mix becomes heavily weighted toward expensive external channels.
Hannah is your AI Job Matching specialist. She continuously indexes and normalizes candidate records, understands semantic skill relationships (a "full-stack developer" matches roles asking for "software engineer" with React and Node experience), identifies candidates whose career trajectory suggests they may be ready for a move, and proactively surfaces matches for every new role before any external sourcing begins. At 8 AM Monday, you see: "New Director of Engineering role from Acme Corp. 6 strong matches already in your database. Top match: James Park, last placed at Senior Engineer 18 months ago, recently updated LinkedIn. [View Matches] [Draft Outreach]."
That is why you need Hannah.
Each step is automated. Hannah only escalates when human judgment is required.
Hannah parses role requirements into structured criteria, expanding keyword-based requirements into semantic skill clusters and experience patterns. She also identifies implicit requirements (a "startup CTO" role implies hands-on coding + management + fundraising awareness).
Hannah searches the entire ATS candidate database using semantic matching, scoring each candidate on skills fit, experience depth, salary expectations alignment, location compatibility, and recency of engagement. LinkedIn activity signals (profile updates, job changes) are factored in to predict availability.
Hannah presents the consultant with the top 15 candidates via Slack, each with a match summary, last contact date, predicted availability status, competing offer risk assessment, and a suggested re-engagement approach tailored to how long since last contact.
Hannah drafts personalized re-engagement messages referencing the candidate’s last interaction with the agency, their career progression since then, and the specific new opportunity. Each message is staged for consultant review before sending.
Hannah generates a talent pool report: database utilization rate, candidates matched vs. placed, source channel ROI comparison (database matches vs. job board vs. LinkedIn sourcing), and recommendations for database enrichment (stale records that need updating).
Hannah flags the record for consultant review rather than including it in automated outreach. Confidential searches are handled entirely by the assigned consultant with no automated candidate contact.
Clear boundaries. Hannah works autonomously within defined limits and escalates everything else.
Hannah connects to the platforms you already use. No new software to learn.
Hannah is deployed gradually with measurable checkpoints at every stage.
Monitoring mode first, then gradual rollout.
Pilot indexes the full database and runs matching against 10 active roles. Week 1-2 Hannah-generated shortlists are compared against consultant-sourced candidates from job boards.
Full validation before production deployment.
These AI employees share data and coordinate with Hannah to cover your full operation.