Tsenta Wants to Make Job Applications Faster, Smarter, and a Lot Less Manual

For students and young professionals entering the job market, getting a job can feel like a full-time job in itself.

The difficult part is not always finding suitable openings. Once candidates find them, they still have to customize resumes, prepare cover letters, answer the same questions repeatedly, create accounts on different websites, and fill out lengthy application forms.

After dozens of applications, the process can become repetitive and exhausting.

This was the experience that led Agnay Srivastava and Pulkit Gupta to build Tsenta. The two founders were engineering students at Rose-Hulman Institute of Technology when they experienced the problem themselves while applying for internships.

They found that existing job application tools either offered limited automation or were too expensive for students.

“The tools available were either not transparent about how they worked, or not affordable for students like us,” says Srivastava.

In 2025, the founders decided to build their own solution.

Tsenta is an AI-powered job application platform designed to automate repetitive parts of the job search while keeping applications relevant and personalised.

The Indianapolis-based startup is trying to find a middle ground between simple browser extensions that require constant user involvement and expensive cloud-based platforms that automate almost everything.

Tsenta claims it can tailor resumes and cover letters and apply to roughly 70% of roles at Fortune 500 companies in less than two minutes.

The pricing is also designed around high-volume applications. While some competing services charge around $20 for 80 applications, Tsenta offers 600 applications for the same price.

The founders believe that giving candidates access to more applications can increase their chances of getting interviews, particularly in a competitive job market.

The startup has also attracted early backing. Tsenta received $500,000 from Y Combinator, after the company was discovered through a Georgia Tech hackathon.

How Tsenta Started

The idea for Tsenta came from a problem the founders experienced personally.

As engineering students, Srivastava and Gupta were applying for internships and quickly discovered how much time was being consumed by the application process.

A single job application might require a candidate to upload a resume, enter information that already appears on the resume, answer screening questions, write a cover letter, and provide details about education and previous experience.

Doing that once is manageable.

Doing it dozens of times is not.

The founders looked at existing automation tools but found limitations. Some browser extensions could fill out individual forms but still required the candidate to control the process manually.

Other cloud-based platforms offered more automation but came at a price that was difficult for students to justify.

That gap became the opportunity for Tsenta.

The company started with a very small investment. Before receiving outside funding, the founders had reportedly spent only around $100 on building and deploying the application.

In September 2025, the eight-member team participated in Georgia Tech's annual hackathon and became the only team to win across two separate tracks.

One of the prizes offered a choice between interviews with Y Combinator companies and a referral to Y Combinator itself.

The founders chose the referral.

They applied in November and were accepted in December.

That gave Tsenta access to funding and an opportunity to scale a product that had started from a problem the founders had faced themselves.

The Nature of the Job Market—and Where AI Fits In

AI has already changed how candidates search and apply for jobs.

Tools such as automated application platforms can submit large numbers of applications with limited human involvement. At the same time, companies have become more aware of AI-generated resumes and automated applications.

Applicant tracking systems are also becoming more sophisticated.

This creates a difficult situation for job seekers.

Automation can save time, but excessive automation can also make applications look generic. If hundreds of candidates submit resumes with similar wording and keywords, simply generating another AI-written resume may not provide much of an advantage.

Tsenta takes a different approach.

Rather than completely rewriting a candidate's resume for every job, the platform aims to make targeted changes based on what a particular position requires.

The objective is to keep the candidate's original experience, language, and skills while making the resume more relevant to the role.

“Our priority is keeping the candidate's language, experience, and skill set true to who they actually are,” says Srivastava.

That approach is important because personalization is one of the biggest challenges in automated job applications.

A candidate may have strong experience, but a generic application can fail to communicate why that experience is relevant to a particular position.

Tsenta attempts to use AI to bridge that gap without changing the candidate's professional identity.

How the Tsenta Application Process Works

The process begins with the candidate uploading a resume.

Tsenta then asks the user around five questions to understand their preferences and background.

Based on the information provided, the platform identifies relevant job opportunities.

Once the candidate chooses to apply, Tsenta can tailor the candidate's profile and application materials to the specific position before submitting the application.

This removes much of the repetitive work from the process.

Instead of opening one job posting after another and manually copying information into forms, candidates can allow the platform to handle many of those steps.

The company says its technology can apply to roughly 70% of Fortune 500 job roles.

That does not mean every application can be completely automated. Different employers use different application systems, and some positions require additional questions, assessments, or steps that cannot easily be handled through automation.

Still, the ability to automate a large portion of applications can save candidates considerable time.

Automation Without Losing Personalisation

One of the biggest challenges facing AI-powered job application tools is finding the right balance between speed and authenticity.

Applying to hundreds of positions may sound attractive, but sending exactly the same resume everywhere is unlikely to produce the best results.

Recruiters are looking for candidates whose experience matches the requirements of a role.

Tsenta's approach is based on making applications more relevant rather than simply producing more applications.

The platform uses the candidate's existing information and adjusts it according to the requirements of the position.

This means the candidate remains in control of their professional background while AI handles much of the repetitive work.

That distinction could become increasingly important as companies become better at detecting automated or AI-generated applications.

If automation simply creates generic resumes, it may eventually make the hiring process even noisier.

But if AI can help candidates present their genuine experience in a way that is more closely connected to a job description, it can potentially save time without removing the human element.

The Problem Exists on Both Sides

Tsenta's founders believe the inefficiency does not affect only job seekers.

Employers also spend significant amounts of time dealing with large numbers of applications.

A candidate may spend several hours applying to jobs, while recruiters may have to review hundreds or thousands of submissions for a single opening.

That creates inefficiencies on both sides.

Candidates want to apply quickly to more relevant positions.

Companies want to identify qualified applicants without spending excessive time reviewing unsuitable applications.

AI could potentially help improve both sides of this process.

However, Tsenta is currently concentrating primarily on the candidate side.

The founders have discussed the possibility of playing a broader role in hiring in the future, but their immediate priority is building distribution and serving job seekers.

That focus allows the company to concentrate on a specific problem instead of trying to redesign the entire recruitment process at once.

Growing Beyond the US Market

Tsenta currently has a strong focus on the US job market, but the company also supports users in India.

The startup is working to expand its coverage of Indian employers and improve its compatibility with the applicant tracking systems commonly used by companies in India. Platforms such as heloix.com also reflect the growing interest in technology-driven career and employment solutions in the Indian market.

This could become an important area of growth.

India has a huge pool of students, graduates, and experienced professionals competing for jobs across technology, finance, consulting, marketing, and other sectors.

For many of these candidates, applying to jobs can involve the same repetitive steps seen in the US.

Improving automation for Indian employers could therefore open up another significant market for Tsenta.

Funding and the Road Ahead

Tsenta's early growth has been rapid.

The company says it has crossed 9,000 customers, while user growth has been doubling month over month.

Revenue has also increased sharply, with the company reporting fivefold growth in a single month.

The $500,000 funding from Y Combinator gives the startup additional resources to build its product and expand its customer base.

The immediate focus is on increasing adoption, improving the application experience, and making the technology work across more employers and hiring platforms.

For candidates, the appeal is easy to understand.

Instead of spending hours applying to jobs one by one, they can use AI to handle much of the repetitive work while still maintaining control over their application profile.

The bigger question is how the hiring market will respond as these tools become more common.

If candidates can apply to hundreds of jobs with very little effort, companies may receive even more applications. That could create another layer of competition and force employers to improve their own screening systems.

At the same time, better personalisation could help candidates submit stronger applications instead of simply submitting more applications.

Tsenta is betting that the future of job searching will involve a combination of human decision-making and AI automation.

For now, its mission remains focused: make applying for jobs faster, reduce repetitive work, and give candidates more time to focus on finding the right opportunity rather than filling out the same form again and again.