Built AiLancerX š ā an AI tool that helps freelancers analyze jobs & write better proposals.
I realized I was spending more time understanding job posts and deciding whether to apply than actually doing the work.
So I built this:
- Analyze job descriptions instantly
- Highlight key requirements
- Generate a proposal draft
Goal: help freelancers save time and win more projects.
š Try it: https://www.ailancerx.com
š Chrome extension (works directly on Upwork): https://chromewebstore.google.com/detail/plbchpmilcdpfkpabkdkphjfcklecgie?utm_source=item-share-cb
Would love feedback š
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spending more time deciding than doing - that's a clean automation signal. I hit the same thing building PM tools. are you planning a fit-score component, or is proposal-first the right order?
Yeah, exactly ā that ādeciding > doingā gap was the main trigger for building this. Right now Iām actually shifting away from proposal-first and focusing more on a fit-score / decision layer first. The idea is to help users quickly filter out low-quality or low-fit jobs before they even think about writing. Proposal generation still exists, but more as a second step after a job passes that initial filter. Curious how you approached this in your PM tools?
fits the problem better honestly - once you have the decision layer you know what to sell and the actual proposal gets faster. what signals are going into the fit score?
This solves a very real pain. I have wasted way too much time just deciding whether a job is even worth applying to.
One thing you might find interesting ā getting early visibility for tools like this is often harder than building them.
I have been working on a simple launch/feed where you can post your project and get it seen without needing an audience first: https://buildfeed.co
Might be worth dropping AiLancerX there and seeing what kind of feedback or traction it picks up.
Curious how your first users are finding you so far?
Appreciate this, youāre absolutely right, getting visibility has been harder than building the product š Most of our early users are coming from Indie Hackers, some direct outreach, and organic posts where the problem resonates, but itās still very early and Iām experimenting to find what works best. Buildfeed sounds like a solid idea for early traction, Iāll give it a try and see how it performs. Thanks for sharing š
My early users mostly came from Peerlist and HackerNews. Let me know how Buildfeed goes, no worries! Good luck with your projects
This is actually a solid use case , Iāve seen freelancers spend more time deciding which jobs to apply for than actually writing proposals.
One thing Iām curious about , does it help filter which jobs to avoid as well?
Seems like identifying low-quality or low-conversion jobs could be just as valuable as generating proposals.
Glad you pointed that out ā and yes, thatās a big part of it.
It helps flag low-quality or low-fit jobs (vague scope, low budget, etc.) so you can skip faster.
Avoiding bad jobs is honestly half the win here. Would love your feedback if you try it š
That makes sense. Iāve seen similar patterns with AI tools , sometimes the real value is filtering out noise, not just generating output.
Curious, are you seeing better conversion rates after filtering those jobs?
Yeah, thatās exactly what Iāve been noticing. Early on, even just filtering out low-quality jobs made a noticeable difference ā less wasted time and more focus on relevant opportunities. Itās still early, but the initial pattern looks promising in terms of better response rates. Iām trying to validate this more with real users over time. Curious if youāve seen similar results with other tools?
Yeah, thatās a strong signal already. ..
Iāve noticed something similar with AI tools , the ones that help filter first usually outperform the ones that just generate output.
In a lot of cases, removing low-quality options actually improves overall conversion more than improving the top picks.
Iām seeing this especially in AI tool discovery , people get overwhelmed with options, so curation becomes the real value.
Curious, are you planning to turn this into a standalone product or keep it as part of your workflow?
Yeah, thatās exactly the direction Iām leaning towards. The more I explore this, the more it feels like the filtering/decision layer is the actual product, and everything else comes after that.
Right now Iām keeping it as part of a broader workflow, but I can see it evolving into a more standalone decision engine if that part proves strong enough. Still validating where the real value compounds.
This is a solid guide. Iād add that testing different variations can reveal some unexpected results. What works for one setup doesnāt always work for another. Learn How to do computer repairs.
Thatās a great point. Iāve already seen some unexpected behavior depending on job types and user profiles, so ongoing testing has been key. Curious what kind of variations youāve seen work best?
Youāve nailed the pain ā deciding what to apply to is the real bottleneck, not writing.
Only thing Iād push:
Right now this still feels like a helpful tool, not a āmust-useā layer.
If you tighten it to:
ā āonly shows jobs you should apply to + whyā
instead of helping with everything, it becomes way harder to ignore.
That shift alone could change adoption a lot.
Also ā small note, if you lean into that sharper positioning, your name/brand will matter more than it does right now.
Thatās a really good point.
Iāve been focusing on helping with the whole flow, but youāre right, the real pain is deciding what not to apply to.
Iām already seeing people use the analysis part for that, so narrowing the positioning around āonly apply to the right jobsā makes a lot of sense.
Appreciate this, super helpful.
Yeah ā once you narrow it like that, the framing does most of the work.
Right now AiLancerX still sounds like a generic AI tool ā doesnāt carry that āfiltering decisions / only apply to the right jobsā angle.
If the name doesnāt reflect that shift, youāll keep attracting people looking for proposal writers instead of decision filters.
Something closer to:
ā ājob filter / apply signal / bid selectā direction
will instantly pre-qualify the right users.
Curious ā are you planning to keep the name or change it as you narrow?
Thanks for your suggestion, but Iām not planning to change the name right now. The product is designed for freelancers across multiple marketplaces, not just one. While it currently supports Upwork and LinkedIn, weāre building it to scale across many platforms in the future.
Fair ā scaling across platforms makes sense.
But thatās exactly where this can break.
āAI tool for freelancers everywhereā = broad ā gets compared ā ignored
āOnly shows you which jobs are worth applying toā = sharp ā people feel it instantly
The risk isnāt the product ā itās attracting the wrong users early and getting stuck there.
You can still build for multiple platforms under the hood, but the entry point has to be painfully specific.
Name plays into that more than people expect ā itās what sets expectation before they even try it.
If you ever feel like youāre attracting the wrong type of users (proposal writers vs decision-focused), thatās usually where the issue starts.
The insight that you were spending more time analyzing job posts than doing the work is the exact kind of friction that rarely gets named but kills freelance momentum. The proposal-writing part is the one where most tools help, but the analysis step before you even decide to apply is genuinely underserved. Curious whether you found any patterns in what signals in a job post most accurately predict whether it's worth bidding on, because that filtering logic tends to be where experienced freelancers build intuition that's hard to capture.
Thatās a great point ā the filtering part is actually where most of the time goes.
From my experience, a few signals tend to matter a lot:
Over time you kind of build an intuition around these, but itās not always consistent.
Thatās actually what Iām trying to figure out with this ā how much of that āgut feelingā can be turned into something more structured.
Still early, but interesting to see patterns forming.