Hiring keeps failing for the same reasons. We built the fix.

We are a team of AI engineers, enterprise systems architects, and talent scientists who got tired of watching great candidates get lost in broken processes, and great teams burn out trying to find them.

What we believe

Three principles that shape everything we build

Capability first

We evaluate what candidates can actually do, not what they have managed to get onto a résumé.

Context matters

Role-specific, organization-aware frameworks. The same candidate is a different hire in a different context.

Clarity over guesswork

Your panel walks in with scores, evidence, and probe areas. The decision is yours. The groundwork is ours.

The team

Built by specialists. Not generalists.

Enterprise hiring touches AI, systems, psychometrics, and organisational behavior. We built a team that covers all four.

AI & Machine Learning

Intelligence

Multi-agent systems and LLM orchestration: each agent specialized for one stage of the funnel, not general-purpose
Natural language understanding at enterprise scale, with structured output guarantees
Domain-calibrated evaluation grounded in role-specific knowledge bases, refined with every assessment
Reasoning transparency: every AI decision traceable to evidence

Enterprise Systems

Infrastructure

Deployment architecture supporting Cloud, VPC, and On-Premise models
Designed for SOC 2 readiness from day one, with data governance built in, not retrofitted
High-availability infrastructure for organizations with zero tolerance for downtime
ATS integrations: Darwinbox, Zoho, Keka, Naukri RMS, and more

Talent Science

Evaluation

Competency modelling and assessment design grounded in how capability actually works
Industrial and organizational psychology informing every rubric and scoring model
Bias mitigation designed into the evaluation architecture, not applied as a post-hoc filter
Structured interview science as the foundation: the strongest validated predictor of job performance in a century of selection research

The problem we set out to solve

Hiring was failing for reasons nobody wanted to say out loud.

Panels were spending 80% of their time on candidates who were never going to pass. Decisions were inconsistent. Evidence was thin. And the best candidates were disappearing before the process caught up with them.

01

The bottleneck was structural

Every TA team was running the same funnel: manual screening, panel time on early rounds, unstructured evaluation. The problem was not effort. It was architecture.

02

Tools weren't solving it

ATS platforms organized the problem. Video interview tools moved it online. Assessment platforms added a step. None of them removed the bottleneck. They rearranged it.

03

So we built infrastructure

Not a tool that sits inside your current process. A system that replaces the early funnel entirely: autonomous from role configuration to ranked shortlist, with no human intervention between steps.

Your panel's time is worth too much to spend on candidates who aren't going to make the cut. We built NH so they never have to again.

What we built

An end-to-end hiring system that removes the bottleneck entirely.

NH is not a tool you add to your process. It is the process: from role configuration through ranked shortlist, with no human intervention between steps. Built for GCCs, deep-engineering enterprises, and large-scale hiring operations recruiting 50 or more people a year across tech, ops, and specialist functions.

Needles & Haystacks

Autonomous hiring infrastructure for enterprise.

NH runs the enterprise hiring funnel end to end: role configuration, resume matching, autonomous R1 and R2 assessments with cross-round context retention, and ranked shortlist delivery. Your panel receives a top-5 DeepLens shortlist, fully scored and evidence-backed, ready to decide.

needlesmith

Campus placement readiness, autonomous.

needlesmith is a student-facing app that prepares engineering graduates for the job market: an ATS-optimised resume builder, a personalized industry news feed, and adaptive AI mock interviews of 30 to 45 minutes, built on the same assessment engine that powers NH enterprise. India produces roughly 1.5 million engineering graduates a year, and study after study shows most leave college without the preparation industry expects. needlesmith closes that gap.

NH runs the hiring team's side of the table. needlesmith runs the candidate's. Together they form a closed loop, from campaign launch to panel-ready shortlist.

Data responsibility

Built to protect your organisation's data and every candidate's privacy.

When you deploy NH at scale, you are handling two sets of sensitive data: your organization’s hiring intelligence and your candidates' personal information. We built the security and governance layer to protect both, with the auditability enterprise organizations require.

Security practices

Encryption in transit and at rest
Zero-trust architecture
Continuous monitoring
Regular audits
Hardened RBAC controls

Compliance readiness

SOC 2
GDPR
ISO 27001
HIPAA
DPDP compliant
Responsible AI standards

AI ethics & transparency

No black-box scoring
Explainable outcomes
Bias mitigation at prompt & model levels
Complete audit trails

Assessment integrity

Standardized criteria
Consistent conversational pathways
Bias-controlled design
Complete evidence logs

Data responsibility

Purpose-limited data usage

Candidate data is used only for the assessment it was collected for. No secondary use.

Least-privilege access

Only the roles and systems that need access to data have it. Logged, auditable, revocable.

Transparent governance

Full visibility into how data moves, where it's stored, and how long it's retained.

Global regulation alignment

Aligned with HIPAA, DPDP, GDPR, and global data residency requirements.

Multi-tenant isolation

Each client operates in an independently secured environment. No data overlap. No shared access between organisations.

Start with one role

You've seen how we think.
Now see what we find.

Pick any live requisition. NH runs autonomously. You have a ranked DeepLens shortlist in 48 to 72 hours. Compare it to what your traditional process would have produced.