Company profile · 4 min read
Elip AI: a WhatsApp job agent that filters before notifying
Elip AI matches job seekers with filtered roles in WhatsApp, sends tailored résumés and charges employers when a hire is made.
Cofounder of Lazyweb (a16z Speedrun). Previously grew subscriptions and built native ads at Duolingo. Writes First1000 (90,000+ subscribers).
Published · Updated
Elip is a job agent that lives in WhatsApp and filters the job market before it reaches the job seeker. The buyer side is employers paying when Elip helps make a hire; the user-facing promise is simpler: describe the role you want once, then receive only the matches that fit, with a tailored résumé and an honest reason.
What it does
The current Elip homepage says the agent continuously reads job postings, skips ghost listings and near-misses, and sends a short ranked set of matches. The about page explains that it normalizes jobs from boards, applicant-tracking systems and company career pages against the user’s experience, location, notice period and chat history. It says the agent can produce an ATS-ready résumé for each role and that one message can wipe the user’s data.
The current model is free for job seekers; employers pay when they hire through Elip. That is the company’s published model, not a guarantee that every match or hire is free of bias. The jobs page currently frames the service around a large India-focused job inventory. The YC launch linked from the company profile is for an earlier product, Tejas AI, an AI risk-decisioning platform for banks. I am treating that as company history, not current Elip product evidence.
Why I’d look closer
The advantage is attention filtering. Job boards make the candidate do the scanning; Elip says the agent does the repetitive reading and sends nothing when it cannot find a good fit. The founder’s public context is relevant: Gaurav Luhariwala previously built data-driven products for Indian banks and financial institutions and founded Tejas AI.
The tradeoff is matching quality and data sensitivity. A job agent can miss a good role, over-rank a weak one or infer the wrong constraint from a chat. CVs and chat history are sensitive, and WhatsApp is a convenient surface that still needs clear consent, deletion and employer-side data boundaries.
What I’d ask
Which job sources are covered and how quickly are expired listings removed? Can a user see why a match passed or failed and correct the profile? How are employer fees, candidate consent and referral attribution handled? What exactly is deleted after the one-message wipe, including backups and processors?
My editorial take
Shortlist Elip if job search volume is the bottleneck and WhatsApp is where the candidate will actually respond. Start with a narrow role and location profile, review the first matches and résumé edits, and keep the user in control of every application. The product’s useful insight is filtering before notification; it still has to earn trust on the boundary between relevance and exclusion.
Quick facts
| Field | Sourced detail |
|---|---|
| Product | WhatsApp job agent, matching, job filtering and tailored résumés |
| Buyer | Job seekers; employers pay when a hire is made through Elip |
| Published price | $0 for job seekers in the founding cohort; no card required |
| Current focus | India-focused job inventory on the checked public jobs page |
| Main question | Does the agent reduce job-search volume without hiding why it filtered a role? |
Sources checked
| Source | Checked |
|---|---|
| YC company profile | 2026-09-19 |
| Elip homepage | 2026-09-19 |
| Elip about page | 2026-09-19 |
| Elip jobs page | 2026-09-19 |
Cohort context
Elip AI is listed in Winter 2025. In our 2026-09-18 directory snapshot, 104 of 165 listed companies in that cohort have YC’s primary industry label B2B (63.0%). This is a current-directory comparison, not an original intake count or a performance ranking. Nine-cohort dataset.
Public website snapshot
Observed 2026-09-19T16:19:32.873Z in raw homepage HTML. This records visible metadata and advertised links, not agent execution or product quality.
| Signal | Homepage observation |
|---|---|
| Product description metadata | Observed |
| Canonical link | Observed |
| H1 or H2 heading | Observed |
| Typed structured data | Observed |
| Docs/developer link | Not observed in this response |
| Pricing link | Observed |
| llms.txt link | Not observed in this response |
| Markdown alternate | Not observed in this response |
Public observations · Collection method. Missing links here do not establish that a capability or file is absent elsewhere.
About the author
Cofounder of Lazyweb (a16z Speedrun). Previously grew subscriptions and built native ads at Duolingo. Writes First1000 (90,000+ subscribers).
I cofound Lazyweb and publish Mudpie. This is an owner-written publication, not an independent testing organization. Research notes distinguish observations, sourced reporting and editorial judgment.
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