Furlenco, one of India's well-known furniture rental and subscription companies, was scaling its engineering team. They had open positions across frontend, backend, Android, iOS, and SDET. Multiple vendor partners and job boards were sending candidates in. The pipeline was large.
The problem was bandwidth. Furlenco's senior engineers were deep in product releases and sprint cycles. Pulling them into first-round interviews for hundreds of candidates would slow down both hiring and shipping.
With over 10,000 candidates to screen, Furlenco's engineering leadership needed a way to run rigorous first-round interviews without loading their senior team. They used Fabric to take over the entire R1 process. This post covers how they set it up, what they customized, and how it helped them hire 30 engineers in two months.
Why does scaling an engineering team become a bottleneck?
Hiring one or two engineers is manageable. Hiring 30 across multiple roles in a compressed timeline creates a different kind of problem.
The people who can best evaluate engineering candidates are the same people building the product. Every hour a senior engineer spends interviewing is an hour they are not shipping features or reviewing code. Multiply that across hundreds of first-round interviews and the cost becomes enormous.
Furlenco's brand recognition made the volume problem worse. Strong employer brands attract more applicants, which is great until you realize someone has to screen all of them. Ten thousand candidates across five roles means thousands of resumes and hundreds of first-round interviews.
And then there is cheating. Fabric's analysis of 19,368 interviews found that 48% of technical candidates show cheating behavior. When your senior engineers are already time-constrained, asking them to also watch for AI-assisted responses is an unreasonable ask.
What makes hiring across multiple engineering roles at once so complex?
Frontend, backend, Android, iOS, and SDET engineers need different technical evaluations. A React developer and an iOS developer should not be assessed with the same coding questions.
Nataraj Subramanian, Associate Director of Engineering at Furlenco, needed each role's interview to reflect what that specific team actually cared about. Generic technical screens would have let too many wrong-fit candidates through to later rounds, wasting even more senior time.
The customization requirement adds overhead. Designing five different interview formats, calibrating them, and ensuring consistency across hundreds of candidates is a project in itself. For a team already stretched, this kind of setup work gets deprioritized, leading to either delayed hiring or lower evaluation standards.
How did Furlenco structure the AI interview process?
Furlenco's process had two stages, both handled by Fabric before any human interviewer was involved.
1. Resume screening
Candidates came in from job boards and vendor partners. Fabric screened every application against role-specific criteria, filtering for relevant experience, tech stack match, and seniority level. This alone saved the team from manually reviewing thousands of resumes.
2. Customized R1 interview
Candidates who cleared resume screening were invited to a Fabric AI interview. This is where Furlenco's customization mattered. They configured each role's interview to include their own first-round questions covering coding ability and technical experience. The interview format was tailored per role, so a backend candidate faced different questions than an Android candidate.
Fabric's AI conducted the interview, asked follow-up questions based on candidate responses, and evaluated coding quality and experience depth. Throughout the process, cheating detection ran in the background, flagging candidates who showed signs of using AI assistance tools.
This setup effectively replaced one full round of human interviews. Instead of senior engineers spending 30-45 minutes per candidate on a first-round screen, they received a scored and ranked shortlist with detailed evaluation notes.
What were the results?
10,000+ candidates screened. Fabric processed every incoming application across all five roles without any manual resume review from the engineering team.
Hundreds of AI interviews conducted. Qualified candidates went through role-specific technical interviews with coding questions and experienced deep-dives, all without a single senior engineer being pulled from their work.
30 engineers hired in two months. Furlenco filled all open positions across frontend, backend, Android, iOS, and SDET within their timeline.
1,000+ hours saved. By replacing the first round entirely, Furlenco's senior engineers stayed focused on product work. The team estimated this saved over a thousand hours that would have come directly out of engineering capacity.
Every candidate went through the same structured evaluation for their role, eliminating the calibration problem that comes with multiple interviewers running first rounds independently.
How can you set up something similar for high-volume engineering hiring?
If you are scaling an engineering team across multiple roles, here is what the Furlenco process shows.
Customize interviews per role. A generic technical screen wastes time at scale. Configuring role-specific questions in Fabric meant each candidate was evaluated on what actually mattered for their position.
Replace the first round entirely. Furlenco did not use Fabric as an add-on to their existing process. They replaced R1 completely. This only works if the AI interview is rigorous enough that your senior team trusts the shortlist, which requires thoughtful question design upfront.
Make cheating detection automatic. At 10,000+ candidates, manual cheating detection is impossible. Fabric's automated signals flagged suspicious behavior without adding any work to the engineering team.
Protect your senior engineers' time. The real cost of engineering hiring is not recruiter hours. It is the senior engineers who stop shipping to conduct interviews. Offloading R1 keeps your best people focused on the product.
Conclusion
Furlenco hired 30 engineers across five roles in two months without pulling their senior engineering team away from product work. Fabric handled resume screening and the entire first interview round with customized, role-specific evaluations.
The 1,000+ hours saved went back into building product. Nataraj's team got scored shortlists with cheating flags and detailed evaluation notes for every candidate, and only stepped in for final-round conversations with vetted engineers.
FAQ
Can AI interviews handle different types of engineering roles in the same hiring cycle?
Yes. Fabric allows you to configure different interview formats per role. Furlenco ran separate evaluations for frontend, backend, Android, iOS, and SDET candidates, each with role-specific coding questions and experience criteria.
How does Fabric detect cheating during technical interviews?
Fabric analyzes 20+ signals during the interview including response timing, gaze patterns, and language consistency. Candidates using AI assistance tools are flagged with a probability score and timestamped evidence.
What is Fabric?
Fabric is an AI interview platform that screens resumes, conducts customizable technical interviews, and ranks candidates with built-in cheating detection. It connects to job boards and handles the full top-of-funnel hiring process.
Can Fabric replace the first interview round entirely?
That is exactly how Furlenco used it. Fabric conducted the full R1 interview with coding and experience evaluation, and the engineering team only interviewed candidates from the final shortlist.
How much interview time can AI interviews save for large hiring drives?
Furlenco estimated 1,000+ hours saved across their 30-person hiring drive. The actual savings depend on your volume, but replacing first-round interviews removes the biggest time sink in engineering hiring.