I’m testing an AI tool that does not write content, code, or summaries
Most AI tools I see here help with one of four things:
write faster
code faster
research faster
automate repetitive work
We’re testing something different with Hirey AI Agent:
Can an AI agent help you find the right person?
Not “give me a list of 100 profiles.”
More like:
“I’m building X. I need to reach people who could actually help with Y. Who looks relevant, why, and what would be a good way to start the conversation?”
For founders, this usually shows up in very real moments:
You need 5 early customers.
You need someone technical to collaborate with.
You need hiring leads.
You need a partner channel.
You need an advisor who actually understands the market.
This early access round is intentionally narrow:
You must already have an OpenClaw agent.
We’re not looking for generic signups. We’re looking for people who can test whether “finding the right people” makes sense as an agent workflow.
The test is simple:
Register on Hirey
Download the plugin
Try it with your OpenClaw agent
Tell us where it feels useful, weird, or broken
If you have an OpenClaw agent and want to test a people-finding workflow, comment openclaw and I’ll send access.
Curious from other builders too:
Do you think “finding the right person” belongs inside an AI agent, or should it stay as a separate search/networking product?
The most valuable AI applications for solopreneurs tend to be exactly this category - not generation but synthesis and decision support.
The highest-leverage AI use cases we keep seeing: surfacing patterns in your own data (which clients churn at the same point, which channel comments get the most traction, what your actual revenue trajectory is vs what you feel it is), answering questions from your own notes and decisions history, and flagging anomalies in operational data that you'd never notice manually.
Generation is easy to see and demo. Synthesis of your operational reality is harder to show but it's where the actual time and money is hidden. What specifically is your tool doing - pattern recognition, anomaly detection, or something else?
The honest test of these is whether the AI part can be removed without anyone noticing. Most relationship-discovery tools I've tried failed it — what they actually do is fancier filtering on a contact list, and the AI is decoration. The ones that survived the 30-day test were the ones whose feature could not exist without LLMs (extracting unstated relationship signals from email or calendar metadata, for instance). Worth asking yourself early: if you stripped the model and left only structured rules, would the product still be useful enough that someone would pay for it? If yes, you may not be selling AI — you may be selling the structured rules with AI as the convincing wrapper.
This is a really fair test, and I agree with it.
If the AI can be removed and the product still works the same, then it’s probably just rules with an AI layer on top. For Hi Agent, the part we’re trying to validate is exactly where rules start to break: vague intent, unstated context, trust signals, and deciding who is actually worth routing to.
We’re still early, so this is a useful lens. I’m going to keep this question in mind as we test: what can Hi Agent do that a structured filter alone could not?
Right, vague intent + unstated context is the right frontier. Those four signals (intent / context / trust / route) are exactly where rules collapse because the answer depends on history the rule can't see. If you can show the first time a routing decision wouldn't have happened under structured rules even a single dialog log - that's the moment 'AI layer' becomes 'AI core'. Worth instrumenting that boundary explicitly.
Whether people-finding belongs inside an agent or as a standalone product depends on one thing: does the agent have context the standalone tool doesn't?
A standalone search tool has no idea who you already talked to, what you're currently working on, or why the last 5 people you contacted weren't a fit. An agent that's been working with you for weeks has all of that. That context is what turns a list of relevant profiles into an actual recommendation. Without it, you're just getting a filtered LinkedIn export with extra steps.
The interesting product question is whether Hirey stores enough session context to make the agent recommendation meaningfully different from what a well-prompted search would return. If yes, that's a real wedge. If no, the UX layer might not justify the integration overhead for most users.
This is a very fair point, and I agree with the test.
If Hi Agent only returns a cleaner list of profiles, then it is not meaningfully better than search. The real reason it belongs inside an agent is the working context: what the user is trying to do, who they already reached out to, what did not work, and what kind of help they actually need.
That is the part we want to validate with OpenClaw users, since OpenClaw already has the concept of agents, skills, plugins, and workflow context.
So yes — the wedge is not “AI search.” It is whether Hi Agent can use context to make people-finding feel like a real recommendation, not a filtered export.
Really appreciate this framing. It gives us a clear bar to test against.
honestly the hardest step isn't searching - it's articulating 'who could actually help with Y' clearly enough for any tool. curious how Hirey handles underspecified intent
Totally agree — that’s a really good point.
A lot of the time, the hard part is not search. It’s turning a vague need into a clear intent: who could actually help, why they’re relevant, and what outcome you’re trying to reach.
That’s exactly the kind of problem we’re thinking about with Hi Agent. Before matching people or agents, the intent itself has to become clear enough to route to the right place.
yeah - and most tools just skip past it and match on keywords. curious what the experience looks like in Hi Agent when someone doesn't know yet how to articulate what they need.
Exactly — that’s the part we’re trying to learn from real users.
If the intent is vague, Hi Agent should help clarify it before jumping into matching. Not just “search profiles,” but understand what kind of help is needed and who might actually be useful.
Do you already have an OpenClaw agent set up? If yes, I’d be happy to share the early access link and would really value your feedback on this flow.
That's an interesting approach... but it's increasingly difficult to find people who are genuinely interested in a product without some ulterior motive, and when the product is in its early stages and almost no one has tested or used it yet, it becomes even more difficult. Good luck on the long road ahead...
You’re right — it’s hard to tell who is genuinely interested early on and who is just being polite, curious, or has another motive.
That’s why we’re trying to focus on people who actually feel the problem in their own workflow, not just people who like the idea. At this stage, honest feedback matters more than positive feedback.
Appreciate the good wishes — definitely a long road, but we’re learning step by step.
I think this is actually a strong AI use case. Most networking tools stop at “here are profiles,” but the hard part is context: who’s actually relevant, why they’d care, and how to approach them without sounding cold or spammy. If the agent can reliably bridge that gap, it feels more useful than another generic outreach/search product.
Totally agree — profiles alone are not enough. The hard part is knowing who is actually relevant, why they might care, and how to reach out without it feeling random.
That’s exactly what we’re trying to test with Hi Agent. It's live now, and we’re learning from early users before expanding the workflow further.
If you have OpenClaw set up and you’re open to giving feedback, I’d be happy to share access and hear what feels useful or missing.
The interesting part is that this does not sound like “hiring” only.
It sounds more like relationship discovery for founders.
Early customers, advisors, collaborators, partner channels, hiring leads — those are all different surfaces of the same problem:
who is the right person to reach, and why now?
That is bigger than a recruiting workflow.
So I’d be careful with “Hirey” as the main frame. It may make people mentally file this as hiring software when the stronger category is closer to founder-network intelligence or relationship routing.
If the product works, the name should not narrow it before users even test the agent.
This is really helpful feedback — and I think you’re right.
“Relationship discovery for founders” is probably a much better frame than making people think only about hiring.
The use cases we’re seeing are broader: early customers, advisors, collaborators, partner channels, hiring leads, and people who can open useful doors.
Your point about the name is also fair. If people mentally file it as hiring software before trying the agent, we may be narrowing the category too early.
We’re testing the workflow with OpenClaw users first because we want feedback from people who can actually try it inside an agent setup.
Do you already have an OpenClaw agent set up?
If yes, I’d be happy to send early access. I’d especially value your feedback on whether this feels more like founder-network intelligence / relationship routing than recruiting.
Bahar, one follow-up since this thread is a couple of weeks old.
This is exactly the kind of product where I would not wait too long to audit the category frame, because early user feedback can get distorted if people test it through the wrong mental box.
If OpenClaw users read Hirey as hiring software, they will judge it against recruiting workflows.
But if the real product is founder relationship intelligence, the better questions are different: who should I reach, why now, what kind of relationship is this, and how does the agent route useful people across customers, advisors, partners, collaborators, and hires.
I’m doing focused naming/positioning audits for early products now. For Hirey, I’d look specifically at current name risk, category framing, how users may misread the product, stronger positioning language, and whether “Hirey” can hold the broader relationship-intelligence direction before more users test it.
It is not a long consulting thing. Just a sharp written breakdown you can use before the recruiting frame gets too baked in.
I’m doing a few at $99 while refining the format.
Best place to discuss privately:
https://www.linkedin.com/in/aryan-y-0163b0278/
I don’t have an OpenClaw agent set up right now.
But the positioning issue is already clear from the outside.
If the product is finding useful people across customers, advisors, collaborators, partners, and hires, then “Hirey” is too narrow for the actual job.
It makes the product sound like recruiting before the user understands the broader value.
That is the risk.
The stronger category is not hiring.
It is founder relationship intelligence.
Happy to take a look once there’s an easier way to test it, but I’d pressure-test the name early before “hiring tool” becomes the default mental box.
Are you on LinkedIn? Easier to share sharper naming directions there.
Quick note: this is intentionally not open to everyone yet.
For this round, we only want people who already have an OpenClaw agent.
The feedback we care about most:
Did the plugin setup work?
Did the workflow make sense inside OpenClaw?
Was the “people discovery” use case clear?
Which use case felt strongest: customers, collaborators, hiring, partners, advisors, or investors?
Happy to send access to OpenClaw users who want to test properly.