Product manager for HRTech and recruitment tech

Recruitment technology has a credibility problem: everyone claims AI matching, and every recruiter has been burned by a tool whose "match score" was a keyword count in a trench coat. Building HRTech that practitioners actually adopt means solving the real workflow — high-volume, deadline-driven, trust-scarce — not the idealized one in the pitch deck. This is the market I'm operating in right now, daily.

Currently building: WisOwl AI

I co-founded WisOwl AI, an agentic hiring platform for the Indian job market. I designed and implemented its embedding-based semantic matching engine on FAISS and Supabase pgvector, and shipped autonomous recruiter agents that match candidate supply and role demand in real time. Traction so far: 5,000+ organic signups and 15+ recruiter partnerships with zero paid marketing. Not a case study I read — a P&L I own.

What that means for your HRTech product

• Matching quality is an evaluation problem. I'll help you define what a "good match" measurably is for your users, build the golden datasets, and stop shipping on vibes.
• Two-sided liquidity is sequencing. Candidates without recruiters churn; recruiters without candidates never activate. I've navigated the cold-start problem with real constraints and can pressure-test your plan.
• Recruiter workflow is sacred. Features that demo well but add clicks die in week two. I design around the ATS, the phone, and the WhatsApp thread that recruiting actually runs on.
• Agents need guardrails. Autonomous outreach and screening carry real fairness and brand risk. I build the human-in-the-loop checkpoints before they're needed, not after.

Earlier, I spent eight years at CaaStle running growth product across a $30M–$50M ARR B2B SaaS portfolio — useful scar tissue for HRTech companies selling into enterprises. I take fractional roles, advisory retainers, and scoped builds; I'm happy to start with a teardown of your matching or activation funnel.

Frequently asked questions

Isn't it a conflict to advise HRTech companies while building WisOwl?

I don't take engagements that compete directly with WisOwl's market, and I'm explicit about boundaries before we start. Most HRTech is adjacent — assessment, payroll, ATS, sourcing tools — where my current operating knowledge helps rather than conflicts.

Our AI matching gets complaints about relevance. Where do you start?

With an honest eval: sample real matches, have practitioners label them, and measure. In my experience the root cause is usually representation — what the embeddings actually encode about roles and candidates — not the model itself.

Do you understand recruiting agency workflows, not just in-house?

Yes — WisOwl's 15+ recruiter partnerships span agency and in-house teams, and agencies' margin-per-placement economics shape several of our product decisions. Volume tolerance and speed expectations differ enormously between the two.

What engagement shapes do you offer?

A two-week product teardown, fractional product leadership at 1–3 days a week, or a scoped build of a specific capability like semantic matching or an agent-assisted screening flow.

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