The client is a global recruitment process outsourcing firm, hired by businesses to assist them for some or all of their hiring. Their recruiters face the same pressure that every RPO firm across the globe faces: stiff SLAs set by clients, high expectations on quality and speed, and constant pressure to cover more volume at similar costs than the year before.
At a given instance, each recruiter carried more than 40 open requisitions, with sourcing and screening consuming up to 60% of that capacity. The firm already had legacy RPA tools and a CRM-based automation tech stack. Both added friction instead of removing it: they automated emails and tasks, while the conversations and judgment calls that move a candidate toward an offer stayed exactly where they were. Leadership saw the potential in generative AI but did not possess an in-house team with the depth to build a solution around recruiting rather than adapt one to it. What the team needed was the right-sized solution that moved at its speed without losing a recruiter's judgment, working inside the ATS, email, SMS, and scheduling tools it already ran on.
Extending their existing RPA and CRM automation meant more point tools that handled the repetitive parts of the job alone, without any help left for every judgement call and every candidate conversation where it had always been.
thinkbridge had the expertise and resources to build the solution the client needed with a quick turnaround and outcome assurance. We proposed a generative AI recruiter built upon the firm's own recruiting conversations, job requisitions, and language. The recruiter was set alongside the tools recruiters already used rather than bolted on top of them.
thinkbridge partnered with the firm's operations, talent acquisition, and technology teams to build the AI Recruiter that powers their operation today with the thinkOne methodology.
Discovery was rolled out with thinkAI within the methodology, and the thinkbridge team and the firm’s teams worked together to understand workflows, map their process, and build an as-is understanding that would act as the foundation for the build.
Then, with a composed thinkTeam pod, the AI Recruiter was built upon the firm's own recruiting language and operating model. The tough part of the build was earning back the trust the earlier automation had enforced. Recruiters needed to see, from week one, that the agent would return judgment calls to them rather than take them over. It was then embedded into the ATS, email, SMS, WhatsApp, and scheduling tools recruiters already used.
The resulting AI recruiter handles outreach, follow-up, screening, and scheduling, personalized by role, geography, experience, and prior interaction. Recruiters keep the final say: every message it sends can be monitored, overridden, or retrained in real time, and a weekly feedback loop built from recruiter ratings, interview outcomes, and client input keeps tuning its judgement after launch. Every interaction is logged and auditable for GDPR and EEOC requirements.
The firm owns the recruiter fully and saw outcomes from week one. Over 10,000 recruiter hours were freed in 90 days. The time spent on repetitive tasks moved to higher-value work, and candidate experience held steady throughout, by the firm's own account.
3x recruiter productivity. Recruiters who managed about 40 open requisitions now manage 120 or more, with no corresponding rise in headcount.
80% faster candidate response. Average time to first contact fell from 48 hours to under 10 minutes.
70% more qualified submissions. The agent's screening runs at 95% accuracy, so recruiters see more candidates who fit the role, not simply more candidates.
Every new client added another point tool to a stack that could not talk to itself: one system for reconciliation, another for reporting, another for client communication. Account managers absorbed the gap by hand, re-entering data, chasing exceptions, and stitching together updates one client at a time.
The labor hiding between those systems set a hard ceiling on how many accounts one person could run. Every added client added headcount and overhead before it added margin, and the firm needed a way to grow revenue without growing cost at the same rate.
The realistic alternative was a sixth point tool bolted onto a stack that five tools had already failed to fix. The firm required one platform built around how it delivers the service: intake, reconciliation, exception review, and client reporting as a single flow.
That kind of system around a client's own operations is the thinkOne solution.
Production stage, thinkOne primary, thinkAI ancillary. thinkbridge and the firm's own team designed and built one delivery platform in place of the old tool stack. The platform automated the repetitive parts of the job: invoice processing, reconciliations, and routine reporting.
AI-driven exception review then routed staff attention to the anomalies that required a judgment call. Real-time dashboards gave the firm's internal team and its own clients a shared, current view of the numbers, replacing the periodic manual updates the old model ran on.
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