Company profile · 3 min read
Tsenta: an AI agent for job applications
Tsenta finds matching roles, tailors application materials, submits across ATSs and tracks a candidate’s job-search pipeline.
Cofounder of Lazyweb (a16z Speedrun). Previously grew subscriptions and built native ads at Duolingo. Writes First1000 (90,000+ subscribers).
Published · Updated
What it does
Tsenta is an AI career agent that finds matching jobs, rewrites application materials and submits applications on a candidate’s behalf. Its current homepage says it watches 50,000+ career pages across Workday, Greenhouse, Lever, Ashby and other ATSs, then finds roles, tailors a résumé and tracks the application pipeline. The YC profile describes the wider product across web, iOS, Android, iMessage, WhatsApp and a browser extension.
The fit is a job seeker with a clear target profile who wants to remove repetitive search and application work. Tsenta is less useful if the candidate needs highly bespoke networking or wants to personally review every application before submission. The product’s real value depends on relevance, truthful personalization and not flooding employers with low-fit applications.
Why I’d look closer
The platform exposes a concrete pipeline: find, match, tailor, submit and track. The homepage shows application states such as queued, submitted, needs-you and failed, which is the right shape for an agent that acts on someone’s behalf. The YC launch reports 45,000+ users at an earlier point, more than 70% of paid users landing interviews and 25 free applications in its launch offer. The current directory reports more than 90,000 job seekers and about $3M annualized revenue after three months. These are dated company claims, not an independent measure of applicant quality or interview causation.
The founders have direct experience with the pain. The YC biographies describe Agnay Srivastava with systems, MLOps and software-engineering experience, and Pulkit Gupta with prior full-stack work and an education app reported at 1M+ downloads. The official launch says the founders manually applied to more than 3,000 jobs before building Tsenta.
What I’d ask
How does Tsenta decide when to apply, and can a candidate approve or edit every tailored answer? How are résumé facts kept truthful, how are ATS account credentials and OTPs handled, and how does the system respect employer application terms and duplicate submissions? I’d run a small job set with explicit approval and inspect match quality, skipped roles and failure recovery.
My editorial take
Tsenta is a strong fit for candidates who value application throughput and visibility. The pipeline is concrete; the large usage and interview claims should stay company-reported. The product earns trust when it makes the candidate more selective, not merely faster.
Quick facts
| Field | Sourced detail |
|---|---|
| Buyer fit | Job seekers with a defined role and profile target |
| Workflow | Find, match, tailor, submit and track across ATSs |
| Usage signal | Company reports 90,000+ job seekers; earlier launch metrics are time-bound |
| Public pricing | No current price card used in this profile |
Sources checked
Checked 2026-09-19.
| Source | Used for |
|---|---|
| YC company profile | Product, founders and dated company-reported traction |
| Tsenta homepage | Current ATS coverage and application pipeline |
| Tsenta dashboard | Public dashboard/product surface observed without login |
Cohort context
Tsenta is listed in Summer 2026. In our 2026-09-18 directory snapshot, 11 of 232 listed companies in that cohort have YC’s primary industry label Consumer (4.7%). This is a current-directory comparison, not an original intake count or a performance ranking. Nine-cohort dataset.
Public website snapshot
Observed 2026-09-19T16:19:16.727Z 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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