Hitting $3.3k MRR in two months while working a full-time job

Erik Chavez is a Senior Solutions Architect at Microsoft who moonlights as the founder of Jobric, an AI job-matching platform that focuses on candidates instead of employers. He launched it less than two months ago and is already at $3.3k MRR.

Here's Erik on how he's doing it. 👇

Working 5am to 10pm

I've spent more than fifteen years in cloud and platform engineering, working up from sysadmin to VP-level cloud strategy. Along the way, I've built and led Cloud Centers of Excellence, run datacenter-to-cloud migrations that saved seven figures a year, and advised everyone from seed-stage startups to Fortune 100s. I'm currently a Senior Solutions Architect at Microsoft, and I build Jobric in my own time and with my own money. Most days run from 5am to 10pm.

Jobric is an AI job-matching platform built entirely for candidates, not employers. Every other tool in this space works for whoever's paying — the recruiter. Jobric flips it: You upload your resume, set your preferences, and the platform filters the whole market down to the few roles worth your time, scoring each on how well you fit.

People hear "AI matching" and assume it's a wrapper around an LLM. It isn't. A large part of Jobric is proprietary engineering that never touches a model: the matching logic, the scoring, and the data work enabling the rest. On top of that, I run a set of small language models in-house. The AI handles the narrow things it's good at, and the engineering underneath keeps the matches accurate and the cost profile low enough that I can fund this indefinitely if I have to.

We launched on May 1, and we're already at $3,300 MRR, entirely from candidates who converted after the public beta and paid out of their own pockets. The signal I care about is the paid conversion underneath it. The whole industry assumes candidates won't pay because job boards and platforms like LinkedIn and Indeed have trained everyone to expect free. Turns out they will, when the product works for them.

Right now, I'm focused on how far it scales organically before funding becomes the right move.

Building for the people

I built Jobric for someone I love who was stuck in a job that was draining them. After a stressful day, the thought of going home and searching for another job was unbearable. They knew I worked in tech and was deep in AI lately, so they asked if I could build them an "agent" that would watch job postings in their field and email them regularly with roles that matched their resume.

I figured it would be a fun side project. But as I built it and started getting their feedback, something clicked. They were genuinely excited, and the thing surfaced roles that were both strong matches and recently posted. That's when I realized this wasn't just useful for one person. It could matter to almost anyone.

The real tipping point came when I tested it on my own profile. A job hit my inbox, and my honest reaction was, "Oh wow, I almost want to apply for this." I never would have looked at the role on my own because its title wasn't typical for my field, so I would have scrolled right past it. The product saw a fit I wouldn't have. That was the moment I decided I had to get this out into the world.

From there, it became a mission to build a "for the people, by the people" product. Talk to almost anyone, and you'll hear the same thing: applying for jobs is miserable. Ten rounds of interviews, endless applications, no signal back. Everyone knows the pain, and the current "solutions" don't help. The "use our tool to blast 1,000 applications and we'll auto-rewrite your resume for each one" approach isn't realistic, and everyone applying for jobs knows it.

Realizing the true complexity

Building the first version for one person made providing the AI enough context trivial. Testing it on myself yielded similar results. It appeared to work. Then I asked a friend for their resume, fed it into the early Jobric system, and it completely failed. This person works in cybersecurity, but the system interpreted their background as physical security, like a security guard. The reason: their most recent title was "Security Officer," which, on its face, says nothing technical. The AI took the words at face value, misinterpreting the entire person.

This led me down a path that revealed a non-technical problem. The real problem was human: How do you accurately identify what a person does and, just as importantly, what a job description asks for, when titles lie and language is inconsistent?

Consequently, most of my initial time and money went into research, not code. I focused on figuring out which data points matter, what's publicly available, and how to use engineering first and AI second to achieve accurate matching. My biggest early expense involved running endless tests against every resume I could find. I used personal savings to fund it, knowing it was a worthwhile bet.

The build has remained lean because I know cloud architecture well enough to keep infrastructure costs low, and I can perform much of the hard engineering work myself, avoiding expensive model calls.

I haven't handled the parts outside my expertise alone. As Jobric developed, I brought on fractional advisors to cover areas a solo technical founder cannot: a CISO for security, a general counsel for legal, a CFO for finance, and an exited founder for the operational and go-to-market side. This ensures I don't figure out the rest in a vacuum.

A containerized stack

Our stack divides simply:

• Python runs the "brain."

• TypeScript/Node runs everything the user touches.

• The AI and data work — parsing a resume, classifying a candidate into the right career family, scoring how well they fit a job — use Python because it's the native language of modern AI tooling and hosts the best libraries and model SDKs.

• Next.js (React/TypeScript) powers the candidate app and our partner portal, providing a fast, modern web experience and allowing a small team to move quickly.

I containerized from day one. Many services share a foundation at the dependency level, so a handful of base images underpin everything, making on-demand scaling straightforward from the start. Architecturally, I'm most proud that almost nothing runs all the time. Expensive work — fit analysis, company briefings, job matching — is on-demand. Each piece is a self-contained containerized service that wakes up, performs its job, and then goes quiet. We coordinate it all through a message queue, which reliably hands off work and allows each piece to scale independently with demand. The payoff is cost and reliability: We don't pay to keep a giant always-on machine idling, and a spike in one area doesn't take the rest down.

For the AI itself, I run the core matching and classification on small, in-house language models. Postgres with vector search handles the semantic matching.

Expansion possibilities

Our model is deliberately narrow: candidate-side subscriptions. We offer a free Seeker tier, plus Candidate at $29/month and Contender at $49/month, with quarterly through annual billing options. This constitutes our revenue today. We have no ads and, critically, never sell candidate data. Jobric works for candidates because their interests come first. We prioritize this alignment in everything we build.

We started charging on May 1st, after running a free beta through March and April to stress-test match quality before asking anyone for a card. We currently have $3,300 MRR. Two months in, I won't pretend a dramatic growth curve exists yet, but I see early, durable paid conversion in a category everyone insists candidates won't pay for. Margins help: As a cloud architect by trade, I run a set of small language models in-house for high-volume work at a fixed cost of around $20/month, while only heavy reasoning hits a frontier model. My cost per match drops as I grow. Anyone running purely on third-party LLMs has the opposite problem.

Expansion moves in a few directions. The simplest direction involves upgrades: free to paid, and Candidate to Contender. Geography offers a larger expansion path: we launched in the US, UK, and Canada from day one instead of US-only. Europe is next. Each new market widens our audience at little added cost.

Segments represent the biggest opportunity. Jobric is built for everyone, not just tech. I've spent real time with universities, career coaches, and people in completely different fields to understand each group's struggles. This drives features for users nobody else serves: ghost and scam job detection, visa sponsorship filtering, and more are coming.

Churn presents an honest challenge, and it's baked in. If I do my job, you get a job, and then you don't need me. Retention and expansion become the same question: how do you stay valuable between searches? This thinking drives the biggest thing I'm building right now, which I'm not ready to detail yet. Without giving it away, it aims at people actively searching and those who already have a job but want to stay ahead in their career. If it lands, it transforms Jobric from a job-hunting tool into something you keep around all the time.

One piece of advice from all of this: charge before you feel comfortable. Free users tell you almost nothing. The day a stranger pays out of their own pocket for something they could get a worse version of for free, that's the only validation that counts. Everything before that is a hobby with traffic.

Educating the market on LinkedIn

LinkedIn has been the engine from day one. That's where Jobric's future users live, so when I needed beta testers, I went straight to the public and asked who wanted to try something new. That's how I attracted the first cohort, and it's still the main channel. I run it as a campaign, not random posts, and constantly tune it based on what resonates. One number I'm weirdly proud of: the business page crossed 100 followers in under a month, which is harder for a brand page to achieve than a personal profile.

Our own data powers much of the content. The same market intelligence that drives the matching informs what we publish, including a newsletter and blog we launched in June. Instead of guessing what to say, I use what Jobric sees about the job market, which makes the content both differentiated and quietly demonstrates the underlying product.

Research has shaped the channel strategy as much as the content. I've spent time talking to different segments, universities, and career coaches to understand not just how Jobric helps, but why it matters specifically to each group. I'm a "start with why" person, and the more I go back to why Jobric exists, the sharper the messaging becomes. This is driving a second-half push I expect to achieve far more than the first half did.

If I offer one piece of growth advice, it's that the hardest part isn't reach; it's reprogramming a reflex. Job boards, LinkedIn, and one-click-apply tools have trained everyone to believe that more equals better. More listings, more applications, more activity. Jobric is the opposite bet: fewer and better listings. So a key part of growth is convincing people that quality over quantity isn't just a nicer way to job hunt; it's the only one that respects their time.

Shift that belief, and the product sells itself. That's the marketing problem I'm solving.

Feedback and books

End-user feedback has been the single most valuable thing to me — and not just the positive kind. During the beta, we constantly asked for it. Most feedback was encouraging, with suggestions we baked straight into the product. But the angriest feedback stuck with me most.

One beta user told me, "You should pay me for my time because this is such a crap product." You can't please everyone, and we almost certainly lost that person for good. But I learned something important from it. A beta user only had to upload a resume and wait for jobs to come to them. No cost, no real effort on their end. So why were they so furious? Because Jobric's underlying promise actually matters to people. People don't get that angry about something they don't care about. People are exhausted by how job searching works today, and that reaction showed me I'm building something worth being angry about. It motivated me more than any compliment.

Another thing that helped was Simon Sinek's "Start With Why." That book has influenced me greatly lately. It keeps pulling me back to Jobric's true purpose — not the feature list, but why it exists, who it's for, and how to clearly explain that to someone who's never heard of it. When the day-to-day gets noisy, returning to the 'why' keeps decisions clean.

Focus, think, and really understand your product

My advice? Focus. Then focus more. I advise a handful of startups, and I see butterfly brain wrecking most of them. You know the kid or the puppy who spots a butterfly and chases it, then a second one drifts by, and they chase that one instead, and so on. That's how a startling number of founders operate now, and AI has made it worse, because you can chase every shiny idea instantly. The discipline to pick one problem and stay on it until you solve it has become rare, and it's the single biggest predictor of who makes progress.

Related to that: Think before you build. I'll ask a founder something basic like, "Walk me through your user's workflow: Where do you start and what are they trying to solve?" Instead of an answer, I get "Look at this cool mockup I made with Claude." People sprint straight to the solution, the demo, the visual, and skip sitting with the problem and asking hard questions first. You don't have to take every piece of advice, but you must think and do the research. The mockup is the easy part. Knowing why it should exist is the work.

Finally, and I mean this: Understand your own product. I talk to founders who built something with AI tools and can't answer basic questions about how their product works. It doesn't matter whether you're technical or not. If AI built it, use AI to interrogate it. Ask it what it made, why it chose those options, and where it's fragile. Being non-technical is not an excuse to be a stranger to your own product. Founders who can't explain what they built fall apart the first time something breaks.

What's next?

My biggest goal is for Jobric to become the first place anyone reaches for when they look for work. Not a job board they check, but the trust layer the whole search runs through. People are jaded, and AI has flooded everything with noise; job seekers lack something they can truly trust to be on their side. That's the position I want Jobric to hold.

Part of that involves refusing to accept things everyone else treats as normal. Consider salary numbers: our data shows only about 57% of postings carry real detail. Why is that acceptable? I can't force an employer to be honest, but I can make dishonesty stop working. If a posting hides the basics a person needs to make a real decision, Jobric can flag it, filter it, or surface what the employer left out, so these games quietly lose their power. The more the platform does that, the more it protects people instead of wasting their time.

Ultimately, the goal is simple and human. I want people to find work without getting lost in how fast everything changes, and to emerge with a job that truly fits and a bit of hope they didn't have going in. If Jobric reliably does that, at scale, across every kind of worker, then it will have achieved what I built it to do.

To see the product, start here. For market commentary and data behind much of what I discussed here, read our newsletter: The Update from Ric.

To follow along or reach out directly, I write regularly on LinkedIn: Jobric and personal. I'm genuinely easy to reach, so if anything here resonated, feel free to send a DM.