TalentAI - Reengaging Dormant Candidates with AgenticAI
A leading financial services firm’s Human Capital Management group faced a problem familiar to any enterprise: An unexpected reorganization led to a shift in hiring needs. Filling open positions was put on hold, leaving 70–80 highly qualified candidates sitting in their applicant tracking system (ATS).
When the team received the go-ahead to fill those positions, they wanted to re-engage with the previously identified top candidates. The challenge was quickly figuring out who was still available without wasting weeks on manual research.
The limits of manual verification
The recruiters’ only means of checking candidates’ employment status was to search LinkedIn, one individual at a time. For a handful of profiles, that was manageable. At scale, it was not, especially as recruiters were simultaneously sourcing new applicants.
Among the potential challenges:
Candidate numbers could grow from dozens to thousands, but the ATS offered no scalable way to verify who was still active, making manual checks impossible to sustain.
Because of the time spent researching candidates’ availability, recruiters risked losing them to other employers before recruiters could reach them.
Recruiters’ inability to reconnect with candidates could erode talent’s trust and confidence in the company.
A request for speed and simplicity
The firm asked Tenjumps if AI could help solve their candidate reengagement challenge—a need that, once addressed, opened the door to broader improvements in their recruiting process. During the discovery and ideation phase, Tenjumps’ strategic principals worked closely with the firm’s talent acquisition team to surface pain points in their process and outline what success would look like. Within days, Tenjumps proposed a roadmap and implementation plan for a custom cloud-based solution using AgenticAI, which they dubbed TalentAI.
The clients supplied just three data points per candidate: name, email, and previous employer. From there, Tenjumps took ownership of design, build, deployment, and delivery. The project moved quickly. In less than 10 days, the first version was implemented.
Tenjumps assembled a focused team, comprising a principal strategist, a VP of engineering, two AI developers, and a UI designer. The firm’s team also included an HR VP who served as the decision-maker (VP), the head of engineering, and a data administrator.
Building a platform in record time
The working enterprise prototype of the AgenticAI solution matched candidate records to live LinkedIn profiles. Using only the basic data provided, the model delivered more than 90% accuracy in its matches.
This minimum viable product (MVP) was built as the first phase of a more robust AgenticAI solution designed to track applicant behavior. It ingested the candidate data in multiple formats, connected directly to the firm’s ATS, and presented recruiters with a clear dashboard for prioritizing outreach. In less than four weeks, the MVP was expanded and fully deployed.
How the AgenticAI model worked
At the core was a matching and verification engine built on custom AI. The model could integrate seamlessly with the firm’s ATS to ingest data in multiple formats, including PDF, CSV, Excel, and JSON.
The AgenticAI model matched candidate records to live LinkedIn profiles using name, email, and employer history. It delivered 90% accuracy in matches, helping recruiters trust the results. A scoring system then rated each candidate on a sliding scale, each measuring different evaluation criteria defined by the firm, factoring in availability, job stability, skill match, open-to-work signals, and LinkedIn engagement.
Recruiters accessed everything through a user-friendly dashboard. Candidates were ranked high, medium, or low priority, complete with current employment verification and activity metrics. Another benefit was the ability to adjust scoring criteria. If a hiring manager wanted to emphasize a particular skill set or factor, recruiters could shift the weighting and see updated rankings instantly. That flexibility created a smoother experience and a faster path to identifying the right candidate. Instead of scrolling LinkedIn for hours, recruiters could see at a glance who was ready for outreach.
End-to-end delivery with minimal client lift
The success of the project came from Tenjumps’ ability to combine services across its core practice areas.
Consulting and Strategy: During the discovery phase, Tenjumps conducted a rapid assessment of the client’s ATS and recruitment processes. From that assessment, the team created a roadmap for embedding AI into talent management, which guided the build and deployment of the TalentAI solution.
AI and Machine Learning: The solution was built on OpenAI’s v4 model, which the client selected to reduce overhead costs. This version provided strong language and reasoning capabilities but was limited to smaller batch runs and less consistency across extended tasks. For future phases, Tenjumps outlined an upgrade path to v5, which delivers sharper reasoning and more natural responses. Alongside this, Tenjumps developed custom algorithms to match job descriptions to resumes, score candidates, and map qualified profiles to LinkedIn data for verification.
User Interface and Integration: Tenjumps designed and delivered a web-based dashboard that allowed recruiters to see candidate rankings at a glance. The interface integrated directly with the firm’s ATS and included APIs for LinkedIn connectivity, creating a seamless user experience without requiring changes to existing systems.
Data Engineering and Analytics: The team built real-time pipelines to process resumes, automated scraping to verify LinkedIn data, and business intelligence dashboards to present results. This allowed recruiters to move from raw candidate information to actionable insights in minutes.
All of this was handled by Tenjumps. The client’s involvement was limited to sharing data and validating results.
The results recruiters could see immediately
The outcomes were both clear and measurable. The platform re-engaged more than 70 qualified candidates sitting unused in the ATS. Recruiters gained visibility into which candidates were still in their previous roles, which had moved on, and which had signaled they were open to new opportunities.
Manual LinkedIn checks, which once stretched into weeks of work, were reduced to minutes. Recruiters were able to spend time on what mattered most: conversations with qualified talent. The savings were dramatic as well. What previously required 100 hours of recruiter time (valued at $1,800) was cut to virtually no labor cost—an efficiency gain of more than 99%.
Deployment speed was equally important. What traditionally would have taken up to four months went live in less than four weeks.
This rollout was framed as phase one, designed to validate both the capabilities of the TalentAI solution and the business case for broader adoption. Phase two will involve deeper collaboration with the talent acquisition team to expand features and embed the system more fully into day-to-day recruiting.
The scalability of the solution points to even greater potential. Candidate volumes could increase fourfold without slowing performance, and the same system could be applied to talent pools and continuous hiring pipelines rather than one-off reengagement efforts. Recruiters could even share verified candidate profiles directly with business leaders for interviews, creating a faster, more connected hiring cycle. The opportunities for expansion are virtually unlimited.
“Our goal was to give recruiters speed and clarity. Within days, we had a model that matched candidates with over 90% accuracy. Instead of scrolling through LinkedIn, they now had a dashboard that showed exactly who was available.”
Senior Engineer, Tenjumps
Why this case matters for enterprises
This project underscores the importance of execution. The client didn’t need to commit a large internal team or navigate months of back-and-forth. They provided basic candidate data, and Tenjumps delivered a working enterprise prototype that solved a critical business problem.
It also highlights the speed to value of Tenjumps’ delivery model—measured not just in how quickly the system was built, but in how quickly the client saw results. From request to working prototype in 10 days, and from prototype to production in less than four weeks, the firm moved from stalled hiring to actionable candidate insights in record time. For enterprise recruiting teams used to slower cycles, that pace demonstrates what’s possible when AI and data engineering work in sync.
A repeatable solution for enterprise recruiting
Every large organization has dormant candidates sitting in their ATS, but manually verifying their status one by one doesn’t scale. By automating this step, Tenjumps turned unused candidate records into a live, actionable pipeline.
The system built for this client can be adapted across industries. With minimal client requirements and full-stack delivery from Tenjumps, enterprises can turn unused data into actionable pipelines. The approach is repeatable, customizable, and ready for broader deployment.
From complexity to clarity
For this leading financial services firm, Tenjumps turned a slow, manual process into an automated, scalable system. Recruiters reconnected with top candidates in minutes, and hiring cycles shortened as a result.
Enterprises don’t need to accept long delays or wasted effort as a necessary evil of recruitment . With the right partner, even complex processes can be streamlined and automated. Tenjumps builds the systems that make it possible.