Hiring in India rarely means assessing one kind of candidate in one kind of setting. A campus drive may bring in hundreds of graduates at once. A customer-support team may need to hear how applicants communicate in English or a regional language. A recruiter hiring across cities may also need to reach people who have a phone but do not want to install an app or sit in front of a laptop.
AI interviews can help teams run a consistent first round without scheduling a separate recruiter call with every applicant. But the label alone tells you very little. Before choosing a platform, check what it actually assesses, how candidates can take the interview, what the results mean and where the data goes.
Start with the job, not the tool
Write down what a good first-round assessment needs to establish. For a support role, that might mean listening to a customer scenario, explaining a solution and communicating clearly. For a technical role, it may mean discussing relevant experience or completing a separate technical exercise. For a graduate drive, the immediate challenge may be giving every applicant a fair chance to complete the same first round before recruiters review the results.
Then decide what should be assessed in the interview and what needs another method. A conversational interview can help you hear how someone explains an idea or responds to a scenario; it should not be presented as a substitute for hands-on evidence when the role requires it. Set the criteria before reviewing candidates, and make sure the questions relate to the work.
Check language support carefully
In multilingual hiring, “supports Indian languages” is not specific enough. Ask which languages candidates can actually use in the interview, whether the experience works by phone as well as in a browser, and how the platform evaluates answers in each language.
Spoken-English evaluation needs particular care. A useful score should focus on whether the candidate can be understood for the job, not whether they speak with a preferred accent. Ask the vendor to explain the scoring dimensions, show examples of evidence behind a score and clarify whether mother tongue influence is scored or simply reported separately. Do not assume that a framework used for English applies unchanged to every other language.
For example, Fabric runs interviews in Hindi, Tamil, Telugu, Kannada, Malayalam, Marathi and Bengali, as well as Indian English. Its spoken-English score uses a 0–10 scale that measures intelligibility rather than accent, and mother tongue influence is described separately in an MTI read that is not scored. Fabric’s communication analysis guide explains the scale with real voice samples for each band.
Make sure candidates can access the interview
Candidate access can make or break a high-volume process. If your applicants are more comfortable taking a call than using a browser, check whether the platform can conduct the interview over an ordinary phone line. Ask whether an app, camera or laptop is required, and test the candidate journey on the devices your applicants actually use.
Fabric, for instance, can run the first round over an ordinary phone call: candidates can call in or be called, with no app, camera or laptop needed, and applicants who clear that screen move on to the full AI interview. A browser interview is the better fit when the assessment needs on-screen work, such as coding. The right format depends on the role and the candidates you need to reach.
Ask what happens when hiring volume spikes
For campus placements or large hiring campaigns, ask how many interviews can run at the same time, not just how many candidates the system can store. Also check how invitations are sent, how recruiters review completed interviews and what happens when candidates need help. Confirm the workflow for your own deployment instead of assuming every channel is available by default.
Published case studies show what this looks like in practice. At an IIM campus drive, around 250 students applied for two roles; resume screening narrowed them to 83, AI interviews shortlisted 20 for the employer’s final round, and the firm reported saving more than 100 hours. Furlenco screened more than 10,000 candidates and hired 30 engineers across five tech stacks in two months, saving over 1,000 hours of senior engineering time. One customer’s outcome is not a guarantee for every hiring team, so read each case study for its context.
Treat integrity flags as signals, not verdicts
Remote interviews raise a fair question: how can a hiring team tell whether an answer reflects the candidate’s own knowledge? Ask which behaviours the platform checks for, how it explains a flag and who reviews the result. Possible signals include answers read from another device, AI assistance or second screens. No single signal should be treated as conclusive proof of cheating.
Fabric checks more than 20 signals during an interview, including signs of AI assistance and second screens. Its study of 19,368 interviews flagged 38.5% of candidates, and 61% of the flagged candidates scored above the pass threshold, so they would have advanced on interview score alone. The lesson cuts both ways: a high score does not rule out assistance, and a flag tells a reviewer where to look rather than what to decide. Read the study’s method before drawing broader conclusions from the numbers.
Check integrations, data hosting and pricing
A tool should fit the hiring workflow you already use. Ask which HR systems it integrates with and confirm exactly what moves between them: assessment reports, recordings, transcripts, candidate status or something else. A logo in an integration directory does not necessarily mean every field or workflow is supported for your configuration.
Also ask where candidate data is hosted, what security documentation is available, how long recordings and transcripts are retained and how deletion requests are handled. Security certifications are useful evidence, but they are not a substitute for your organisation’s legal and privacy review. In particular, have the right team approve any statement about obligations under India’s Digital Personal Data Protection Act before you rely on it in a vendor assessment.
Fabric’s integrations page lists Darwinbox, Keka and Zoho Recruit among the ATS and HR systems it connects to. Fabric is SOC 2 Type 2, ISO 27001:2022 and DPDP compliant, and its trust center publishes the supporting security documentation. As with any vendor, confirm the integration fields, data location, retention terms and controls for your own deployment.
Finally, compare pricing against your actual hiring plan. Ask whether the quote depends on interview volume, job types or other requirements, and calculate the cost for a realistic hiring period, not just a small trial. Fabric’s pricing is dynamic and scales with your needs, so ask for a quote at your expected volume.
A practical shortlist before you decide
Before choosing any AI interview platform, run a small evaluation using real roles and representative candidate scenarios. Check language and phone access, review sample assessments and score explanations, test the recruiter workflow, verify integration details, and get legal and privacy questions answered. Include recruiters and hiring managers in the review, not only the software buyer.
If Fabric is on your shortlist, start with the roles you need to fill and the first-round evidence you want to collect. Then review the AI interviewer, or take a sample interview yourself, and test the fit against your own requirements rather than any vendor’s feature list.
Last updated: 19 October 2026.