Resume Screening & ATS

An AI resume screening system reads every application against the real requirements of the role, ranks candidates, summarises each one and drafts interview questions specific to their background. For a role attracting six hundred applications, it makes a genuine review of all of them possible for the first time.

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Who it's for

Companies receiving hundreds of applications per role.

What changes

Shortlisting in an hour instead of a week.

Starting at: ₹6,00,000
Timeline: 10–20 weeks
Category: AI Web Apps & SaaS
Built from: Vashi, Navi Mumbai

Key takeaways

Keyword-matching ATS filters reject good candidates who used different words — that is the problem being fixed.
The system ranks and summarises; a human decides. Never automate rejection entirely.
Bias must be actively controlled, tested and monitored, not assumed absent.
A 600-application role becomes a reviewable shortlist in under an hour.
Builds in 8–14 weeks from ₹3,00,000.

What is wrong with keyword ATS

Traditional applicant tracking systems filter on keyword presence. A candidate who wrote "managed a team of eight" is rejected by a filter looking for "team leadership", and a candidate who pasted the job description into white text at the bottom of their CV passes.

The result is a system that systematically favours people who have learned to game it over people who are good at the job. Recruiters know this, which is why so many end up reading everything anyway and the ATS becomes a filing cabinet.

Semantic evaluation reads what the candidate actually did and compares it against what the role actually needs. That is a different question and it produces meaningfully different shortlists.

What the system produces per candidate

Ranking alone is not enough for a recruiter to act on. Each candidate needs a reviewable case.

Output	What it contains	Why it matters
Match score	Against defined requirements, weighted	Ordering the queue
Requirement breakdown	Met, partly met, not evidenced	Shows the reasoning
Summary	Four lines on who this person is	Faster than reading the CV
Gaps and flags	Missing must-haves, unexplained breaks	Prompts for the call
Interview questions	Specific to this candidate's history	Better first conversations
Source evidence	The CV lines each judgement came from	Auditability

Writing a requirement the system can evaluate

Most job descriptions are unusable as evaluation criteria. "Excellent communication skills" and "dynamic self-starter" cannot be assessed from a CV by anyone, human or otherwise.

The setup work is converting each role into evidenced requirements: what must be demonstrable, what is preferred, and what evidence in a CV would count. "Has shipped a production mobile application" is assessable. "Passionate about technology" is not.

This exercise is frequently the most useful part of the project for the hiring team, independently of the software. Several clients have found that two people on the same panel had materially different ideas of what the role required.

Indian hiring realities the system has to handle

CVs here have specific characteristics that a system trained on international data handles badly.

Format variety. Everything from a one-page PDF to a six-page Word document with a photograph and a declaration.
Notice periods. A 90-day notice is normal and a filter tuned to immediate availability rejects most of the market.
Company recognition. Strong candidates from firms the model has never heard of should not be penalised for it.
Institution bias. Tier-2 and tier-3 college graduates are systematically undervalued by naive scoring, and correcting for this is deliberate work.
Personal details. Age, marital status, photographs and religion still appear on Indian CVs and must be stripped before evaluation.

How it should sit in the hiring process

The system ranks and summarises. It does not reject. A recruiter reviews the ranked list, and low-ranked candidates remain visible and reviewable rather than being filtered out of existence.

In practice a recruiter reviews the top forty of six hundred carefully, scans the rest, and pulls up two or three the system underrated. That last part matters — it is how you find out whether the ranking is working.

We also build a feedback loop. Which candidates were interviewed, which were hired, and how they performed, fed back to check whether the ranking correlates with actual outcomes. Without that you are trusting a score that has never been validated against anything.

Build scope and handover

Eight to fourteen weeks from ₹3,00,000: requirement definition framework, CV parsing across formats, the evaluation engine with bias controls, recruiter interface, integration with your existing ATS or job boards, and the outcome feedback loop.

You receive the system on your infrastructure, the bias testing framework and its reports, the evaluation configuration, and full source. Candidate data retention is configured explicitly, since applicant information is personal data with a purpose that expires when the role is filled.

FAQ

Resume Screening & ATS — your questions

Is it legal to use AI in hiring in India?

Yes, with obligations. There is no India-specific AI hiring law at present, but the DPDP Act applies to candidate data — lawful basis, stated purpose, retention limits and deletion rights — and general anti-discrimination principles apply to the outcomes regardless of how they were produced. The practical position is: disclose that automated screening is used, keep a human in the decision, retain the reasoning for each assessment, and monitor outcomes across groups. We build for that standard.

Can candidates game it by stuffing keywords?

Much less than with a keyword ATS, which is one of the main reasons to move. Semantic evaluation looks for evidence of what someone did, so a list of technologies with no context scores poorly while a plainly written description of relevant work scores well. Deliberate manipulation — invisible text, fabricated experience — is detectable and flagged. It is not immune to a well-written dishonest CV, but neither is a human reader, and that is what interviews exist for.

How does it handle career gaps?

As a fact to note, not a penalty. Gaps are flagged for the recruiter to ask about rather than reducing the score, because the reasons — caregiving, illness, study, the pandemic — are not visible in a CV and penalising them systematically disadvantages particular groups, women especially. This is one of the specific bias controls we test for, and it is a common default behaviour in off-the-shelf screening tools that clients are surprised to discover.

Can it work alongside our existing ATS?

Usually yes. Most systems — Zoho Recruit, Keka, Darwinbox, Greenhouse, Lever — expose APIs for pulling applications and writing back scores and notes. The screening runs as a layer over your existing process rather than replacing the system your team already knows. Where an ATS has no usable API, the alternative is a bulk import and export, which works but loses the live integration.

What about roles where the CV tells you very little?

For high-volume operational and entry-level hiring, CVs genuinely carry little signal and screening on them ranks noise. Those roles are better served by a short structured application form with role-relevant questions, which the system can then evaluate meaningfully. We would recommend that rather than applying CV screening where the input does not support it, and it usually improves the process more than the ranking would have.

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