Reactivating a Dormant Candidate Database with AI Recruiting Workflows

These Echo customer case studies show how Taller's AI platform applies relationship intelligence, sourcing, and sales workflows across staffing and professional-services organizations.

The client had more than 30,000 candidate records inside their ATS platform, but only around 1,000 were actively worked by its five recruiters. The rest of the database was effectively dormant: stale, incomplete, unenriched, and difficult to search with confidence.

The ATS contained a large latent asset, but there was no intelligence layer on top of it. Recruiters could not reliably surface old candidates for new roles, candidate data was not current enough for matching, and sourcing remained manual. Job descriptions, resumes, and proposals were also produced by hand in the recruiting firm's own formats, adding more operational drag to the recruiting process.

The problem was not only that the data existed in an ATS. The problem was that the ATS could not turn that data into active recruiting intelligence.

Echo was deployed across the recruiting firm's recruiting workflow while their ATS platform remained the system of record. Taller integrated Echo bidirectionally with their ATS platform so candidate, job, and application data could move between both platforms without double entry.

The first major workstream was database reactivation and enrichment. Echo ingested approximately 30,762 records from their ATS platform, then enriched the candidate base through LinkedIn. Around 24,860 candidates were scraped and enriched, while approximately 5,900 records with no usable data were unmasked and improved with education and work-history information. This converted a mostly dormant database into an AI-matchable talent base.

The second workstream was recruiting workflow intelligence. Echo enabled AI matching, candidate assessments, ATS publishing, and custom role-status workflows mapped to the recruiting firm's own pipeline stages. Recruiters could work candidates, jobs, and applications inside Echo while their ATS platform stayed synchronized. The third workstream was branded content generation. Echo generated custom job descriptions, tailored resumes in the recruiting firm's multi-section CV format, and proposals exported as branded PDF and DOCX files.

Taller also customized Echo for the recruiting firm's operating model, including a global recruiter-owner filter, AI matches restricted by assigned recruiter, and an increased matched-candidate limit from 50 to 100.

Echo ingested approximately 30,762 candidate records from their ATS platform and made them matchable. The original candidate base was roughly 97 percent dormant. Around 24,860 candidates were enriched through LinkedIn, and approximately 5,900 candidates were unmasked from records with no usable data.

Ten users now work from a single synced recruiting pipeline instead of a static ATS, with no double entry across their ATS platform and LinkedIn.

The recruiting firm demonstrates Echo's ability to turn a recruiting database from a passive repository into an active sourcing asset. Many staffing and recruiting firms sit on large ATS databases that are technically valuable but operationally underused. Echo unlocks that value by enriching stale records, normalizing candidate data, and making the full base searchable and matchable.

Strategically, this is one of Echo's strongest recruiting use cases: the fastest source of new candidate supply is often not outside the firm, but already inside the ATS. Echo gives that buried asset a second life.

The next phase for the recruiting firm is extending the same foundation into sales, including proposal generation and the AI deal detection feature.