Xcelore is an IT services company specializing in AI-powered chatbot development and enterprise automation solutions. As the company expanded its delivery teams, campus hiring became one of the most important channels for bringing in technical talent. The company hires fresh graduates for roles such as Java developers, QA engineers, DevOps engineers, AI/ML specialists, and MERN stack developers, primarily through campus drives across colleges in the Delhi NCR region. We spoke with Sakshi Srivastava, the recruiter responsible for campus hiring at Xcelore, to understand how Goodfit helped the team introduce structured technical screening and manage high-volume hiring more efficiently.
The challenges
Managing 1000+ campus applications
Campus hiring generates significant application volumes for Xcelore.
For off-campus roles posted on LinkedIn, each technical role typically receives 500–600 applications. Campus drives generate even larger pools, with nearly 1,000 students applying from a single college.
For a small recruitment team, this created immediate screening challenges. The team needed a way to evaluate candidates faster while still ensuring that technical quality remained high.
Manual screening slowed down hiring
Before Goodfit, the process was entirely manual from start to finish. Recruiters reviewed every resume individually, then invited shortlisted candidates to attend in-person interviews at the company office, completing all technical rounds in a single day. There was no early filtering layer, which meant a lot of time spent before anyone knew whether a candidate could actually code.
For campus hiring cycles, this meant roles often took 15–20 days to close, particularly when evaluating large candidate pools.
"Whenever we post technical openings, we receive hundreds of applications for each role, and during campus drives it can reach close to a thousand applicants from a single college. Screening such a large number of candidates manually takes a lot of time for the recruitment team."
Sakshi Srivastava
Campus Recruiter & Campus Relations, Xcelore
Why Xcelore chose Goodfit
As Xcelore continued to grow, the recruitment team started evaluating platforms that could add structure to technical screening without adding operational complexity. The goal was simple: assess candidates more accurately, earlier in the process, and stop burning recruiter time on candidates who could not clear a basic coding bar.
The team took multiple demos across two to three days before making a decision. Several platforms were in the mix. What made Goodfit stand out was that it combined coding assessments and AI interviews in a single platform, without the per-user pricing model or separate-product bundling that other tools used. Most alternatives either charged significantly more per candidate or split these into two separate products.
At Rs. 50 per candidate, Goodfit gave a 100-250 person IT services company access to both capabilities without the overhead of an enterprise contract.
"First we screened resumes manually, and then we invited candidates to the office and completed all interview rounds in a single day."
Sakshi Srivastava
Campus Recruiter & Campus Relations, Xcelore
The solution
AI interviews for high-volume technical screening
Xcelore implemented Goodfit primarily for technical hiring workflows. Candidates applying for Java, QA, DevOps, AI/ML, and MERN stack roles now complete an AI interview as the first screening stage, before any recruiter time is spent. This allows the team to evaluate candidates at scale without scheduling hundreds of live interviews.
Since implementation, over 2,600 AI interviews have been conducted through the platform, the bulk of them during campus drives where large batches of students are being screened simultaneously.
Coding assessments for skill-based evaluation
Goodfit's coding assessments introduced an additional layer of skill validation.
Previously, coding evaluation happened only during live interviews. With Goodfit, candidates now complete coding assessments earlier in the process. This ensures that candidates reaching technical rounds already demonstrate baseline coding competency.
By filtering candidates earlier, hiring managers can focus their time on evaluating stronger applicants.
Faster screening for campus hiring cycles
Automating first-round screening significantly reduced the time recruiters spent evaluating candidates.
For campus hiring drives where hundreds of students apply simultaneously, this reduction in screening time made the recruitment process more manageable.
Recruiters could spend less time manually filtering resumes and more time engaging with qualified candidates.
Improved candidate quality
Structured AI interviews and coding assessments helped the team evaluate candidates more objectively.
This ensured that candidates progressing to later rounds already met baseline technical expectations, improving the overall quality of applicants moving forward in the hiring pipeline.
Responsive customer support
Along with the platform, the Xcelore team appreciated the responsiveness of Goodfit's support team during onboarding and day-to-day usage.
Having reliable support was especially valuable during campus hiring drives, where large numbers of candidates were being screened simultaneously. The Goodfit team remained accessible and helped resolve queries quickly whenever needed.
What's next for Xcelore
As Xcelore continues to expand its campus hiring efforts, the team is exploring additional ways to strengthen early-stage candidate evaluation.
Future plans include refining structured assessments and further enhancing the AI interview process to support larger campus hiring drives. With automation now integrated into the recruitment workflow, the team aims to continue scaling hiring operations while maintaining strong candidate quality.
