Most job search tools are built around one idea:
Apply to more jobs.
That sounds good at first. More applications should mean more chances, right?
But after talking to job seekers and building TryApplyNow, I started seeing the problem with that logic.
More applications do not always help.
Sometimes they create more noise.
A weak application sent to 100 companies is still weak. A generic resume sent to the wrong role is still generic. A job seeker who applies fast but does not understand fit, keywords, referrals, or follow-up is still stuck.
That is the problem I wanted to solve.
I did not want to build a tool that blindly spams applications.
I wanted to build a tool that helps people apply with more intent.
That became TryApplyNow.
TryApplyNow is an AI job search platform that helps job seekers upload a resume, compare it against jobs, see match scores, tailor their resume, find employee contacts, and apply with a clearer plan.
The goal is simple:
Help someone take one better job action, not 100 random ones.
You can check it out here:
Why I did not want to build a spam auto-apply tool
There is a lot of pressure to make AI tools feel magical.
For job search, the “magic” idea is usually this:
Upload your resume once, then AI applies to hundreds of jobs for you.
That sounds powerful.
But it also creates problems.
First, many job applications ask questions that need real user input. Work authorization, location, salary needs, experience, disability questions, and sponsorship questions should not be guessed by AI.
Second, each job is different. A resume that works for one role may be weak for another.
Third, employers already deal with too much low-quality application volume. If every candidate sends 500 AI-generated applications, the system gets worse for everyone.
Fourth, job seekers can lose track of what they applied to. That makes interviews harder. It also makes follow-ups messy.
So I took a different path.
TryApplyNow is not meant to hide the job search from the user.
It is meant to make the user better at it.
The tool should help answer questions like:
- Is this job actually a good fit?
- What is missing from my resume?
- Which keywords matter for this role?
- Should I tailor before applying?
- Can I find someone at the company?
- Did I apply already?
- What should I do next?
That is more useful than blindly clicking apply.
The core idea: score before you apply
One of the first things I built was resume-to-job matching.
A user uploads a resume. Then TryApplyNow compares that resume against job descriptions.
The output is not just “good” or “bad.”
The user needs a clear reason.
A useful match score should explain:
- Which skills match
- Which skills are missing
- Which experience looks relevant
- Which keywords may matter
- Whether the job is worth more effort
The goal is not to scare users with a score.
The goal is to help them make a decision.
If a job is a poor match, maybe they should skip it.
If a job is a strong match, maybe they should tailor the resume and apply.
If a job is close but missing some keywords, maybe they should update the resume honestly.
This matters because job seekers waste a lot of time on roles where they have very little chance.
A good tool should help them focus.
Tailoring should not mean lying
Resume tailoring is one of the most useful parts of AI job search.
It is also one of the easiest parts to misuse.
Bad tailoring makes up skills.
Good tailoring finds better ways to explain real experience.
That was an important product rule for me.
TryApplyNow should help users improve how they present their background. It should not invent fake experience.
For example, if a job asks for React, TypeScript, and API work, and the user has those skills buried in old bullet points, the tool can help bring them forward.
But if the user has never used React, the tool should not pretend they have.
This is where AI tools need strong boundaries.
A resume is not just content. It is a professional record.
If the tool pushes users to lie, it may get them short-term attention but long-term trouble.
So the better product is not “AI writes anything.”
The better product is:
AI helps you present your real work more clearly.
That is the line I try to follow.
The real bottleneck is after resume upload
When I started looking at user behavior, one thing became clear.
Getting users to upload a resume is not the hardest part.
The harder part is what happens after that.
A lot of products stop at upload.
They say, “Your resume is ready” or “Here are jobs.”
But that is not enough.
After upload, the user should know exactly what to do next.
In TryApplyNow, I want the post-upload flow to push toward one real action:
- View a high-match job
- Tailor the resume for that job
- Find an employee contact
- Save the job
- Apply
- Track the next step
That sounds basic, but it matters.
A job seeker does not need another dashboard full of buttons.
They need a clear next move.
The product should say:
Here is your best match. Here is why it fits. Here is what to fix before applying. Here is the next action.
That is the product experience I care about most.
Why I added employee contact finding
A lot of job seekers think the only path is the apply button.
But many strong applications get ignored because there is no human signal.
That is why I added employee contact finding.
The idea is simple:
If you find a good job, you should also try to find a person at that company.
That person may be:
- A recruiter
- A hiring manager
- Someone on the same team
- A current employee
- A possible referral contact
This does not mean spamming people.
It means sending a short, honest note.
Something like:
Hi, I saw your team is hiring for this role. I think my background may be a fit because of X and Y. Would you be open to pointing me in the right direction?
That is much better than sending 50 random messages.
Again, the theme is intent.
A good job search tool should help users be more thoughtful, not more annoying.
Why the Chrome extension exists
Job applications are repetitive.
Workday, Greenhouse, Lever, and other ATS platforms often ask the same things again and again.
That is painful.
So TryApplyNow has a Chrome extension to help with autofill on job applications.
But this area needs care.
Autofill should save time. It should not take control away from the user.
The user should still review the application. The user should still answer sensitive questions. The user should still decide when to submit.
That is why I think of the extension as an assistant, not a hidden bot.
The best version of this workflow is:
- Fill common fields
- Reuse resume/profile info
- Help with repeated forms
- Let the user review
- Let the user submit
That is safer than trying to fully automate every step.
It also builds trust.
And in job search, trust matters a lot.
The technical challenge: messy job data
One of the hardest parts of this product is job data quality.
Job posts are messy.
Titles are inconsistent. Locations are weird. Remote roles are not always truly remote. Some jobs are expired. Some have missing apply links. Some are duplicated. Some companies use different naming formats.
A job search product is only useful if the job data is clean enough to act on.
That means a lot of work goes into boring things:
- Removing stale jobs
- Checking apply URLs
- Normalizing locations
- Handling remote roles
- Removing thin pages from SEO
- Avoiding duplicate job pages
- Making sure users do not land on dead listings
This is not the shiny AI part.
But it is the part that makes the AI useful.
If the job data is bad, the match score is less useful.
If the job is expired, the tailored resume does not matter.
If the apply link is broken, the whole flow fails.
So the real work is not only AI.
It is data quality, product flow, and trust.
Why I care about tracking every step
A job search funnel has many steps.
A user might:
- Visit the site
- Sign up
- Upload a resume
- View job matches
- Tailor a resume
- Find a contact
- Click apply
- Save a job
- Return later
- Start a trial
- Become paid
If tracking is missing, you are guessing.
And guessing is dangerous.
For example, if users upload resumes but do not tailor, the problem may be the post-upload UX.
If users click pricing but do not start checkout, the pricing page may be unclear.
If users start checkout but do not unlock Pro, the billing sync may be broken.
If users apply but do not return, the tracker may not be useful enough.
This is why I care about events.
Not because analytics dashboards are fun.
Because each event tells you where the product is failing.
For a founder, that truth matters more than vanity traffic.
What I learned while building this
Here are some lessons that have shaped how I think about AI products.
1. AI should help users make better choices
It is easy to build AI that generates more output.
More resumes. More cover letters. More messages. More applications.
But more is not always better.
The better question is:
Did the user make a better choice?
For job search, that means applying to better-fit jobs with better materials.
2. A score needs an explanation
A match score alone is not enough.
Users need to know why the score exists.
A useful system should explain what matched, what is missing, and what to do next.
Without that, the score feels random.
3. Trust is a feature
Job seekers upload sensitive data.
They upload resumes. They may share location, work history, education, and career goals.
That means trust is not a marketing page.
It is part of the product.
Clear privacy pages, honest claims, safe extension behavior, and no fake testimonials all matter.
4. The boring parts decide product quality
Parsing resumes is hard.
Normalizing job data is hard.
Tracking events is hard.
Billing sync is hard.
Chrome extension permissions are hard.
None of that looks exciting in a demo.
But it decides whether users trust and keep using the product.
5. Do not automate what should stay human
Some parts of job search should stay human.
A user should decide if they are eligible.
A user should review their resume.
A user should approve outreach.
A user should choose whether to submit.
AI can help, but it should not remove judgment.
What I would build differently next time
If I started again, I would focus even earlier on activation.
I would not only ask:
Can users upload a resume?
I would ask:
Can users complete one useful job action in the same session?
That is the key.
Upload is not value by itself.
Value happens when the user sees a match, improves a resume, finds a contact, applies, or tracks a job.
I would also build analytics earlier.
Every important button should have an event.
Every success state should have an event.
Every failure should be visible.
When you are early, you do not need a huge data stack.
But you do need enough truth to avoid lying to yourself.
What TryApplyNow does today
TryApplyNow helps job seekers:
- Upload a resume
- See job match scores
- Understand resume gaps
- Tailor resumes for specific jobs
- Find employee contacts
- Track job actions
- Use a Chrome extension for job application autofill
It is built for job seekers in the United States, Canada, and India.
It is free to start.
The main site is here:
The ATS resume checker is here:
https://tryapplynow.com/tools/ats-resume-checker
The Chrome extension page is here:
https://tryapplynow.com/chrome-extension
What I am working on next
The next goal is not just getting more traffic.
The next goal is making the product more useful after resume upload.
That means:
- Better first-session flow
- Clearer match explanations
- Stronger resume tailoring UX
- Better saved job behavior
- Better job tracking
- Cleaner analytics
- More trust pages
- Better Chrome extension permission education
I also want to publish more honest notes about what I am learning while building this.
Job search is stressful.
AI can help, but only if it is built with care.
I do not think the future is “AI applies to everything for you.”
I think the better future is:
AI helps you find better-fit jobs, improve your real resume, reach the right people, and apply with more confidence.
That is what I am trying to build with TryApplyNow.
Thanks for reading.
Top comments (0)