Frequently Asked Questions

First of all, this is a comment, not a question. Secondly — you are right, you can set up ChatGPT, Claude, Gemini, whatever, to do most of the work for you. In fact, this is how this project started. I had my own setup for the outreach. However, even I, who work with AI tools for a living, find it much easier to use a dedicated tool that does exactly the job I need. You have your own job: doing awesome, world-class research. Let Loud Camel do the outreach job for you.

Yes. I did, for a year and a half, before building this. The prompt engineering, the context management, the constant re-prompting when the model loses track of your research profile, the reformatting for each new outreach target — it adds up to hours a week. If you enjoy that kind of work, or if you're genuinely interested in building the prompt infrastructure yourself, you should do it. If you'd rather spend those hours on research, that's what Loud Camel is for.

They're good tools. They're built for B2B sales. That's a different job.

Lusha finds direct-dial phone numbers and verified emails for VPs and CTOs at target companies. Apollo helps you run a pipeline funnel from prospecting to close. Hunter finds the email format for a domain so you can reach a decision-maker. ReddGrow monitors Reddit for buying conversations and drafts replies to nudge people toward a product purchase. These are serious, well-built tools — for selling software to companies.

Academic outreach is not B2B sales. You're not selling anything. You're reaching a peer about shared intellectual work. The relevance signal is not job title or company revenue — it's publication overlap, citation networks, and shared research questions. The accuracy requirement is completely different: if a sales tool gets someone's company name slightly wrong, it's annoying. If an outreach tool misrepresents your research or the other person's work, it's embarrassing in a field where your reputation is your career.

None of the tools above model any of that. They're not trying to. Loud Camel is.

You probably haven't heard of it because the people doing it don't talk about it. The evidence is clear: prior informal exposure to a reviewer or evaluator increases the probability of a favorable outcome. That's not manipulation — it's how human cognition works. The researcher whose name you vaguely recognise gets a slightly more attentive read. The name you've never seen gets a standard one. Loud Camel makes sure your name is the one people have heard of.

Most researchers using Loud Camel are not aggressive self-promoters — and they'd probably be offended by the description. They're people who are tired of watching their work get less attention than it deserves. There's a difference between writing cold unsolicited marketing emails to strangers and reaching out to a researcher who published something that directly connects to your work, with a genuine observation about the connection. Loud Camel is built for the second thing.

Loud Camel is not a spamming tool. It will never post or send an email on your behalf. Loud Camel is a tool. You can use it for good purposes or you can use it for spamming. Don't spam.

Frankly, it depends on your moral compass.

The fact that I put my own name and photo — Boris Gorelik, data scientist from Israel — on this product means I firmly believe it is. I use AI to generate email drafts and Reddit outreach. But I have never posted or sent a single message, or a single sentence, without reading it, editing it if needed, and being able to sign under it with my name.

If you hold to the same principle — nothing goes out without your full review and your genuine endorsement of the content — I don't see why using Loud Camel is not ethical. If you use it to send things you haven't read, or to contact people with content you can't stand behind, that's not an ethics problem with the tool.

No. Nothing is ever sent on your behalf. Ever. Loud Camel generates drafts. You send them, or you don't. This is a design principle, not a setting you can accidentally turn off.

No. Loud Camel sends a brief to your email address. It does not connect to your inbox, read your email, or have any access to your email account. The outreach drafts it provides are text you copy and send yourself.

Then you're in a tough spot, and you should follow your institution's rules — full stop.

That said, keep in mind what Loud Camel actually does: it generates drafts and delivers them to you. You press send. Loud Camel never sends anything automatically, never posts anything on your behalf, and has no access to your email account or any institutional system. If your institution prohibits automated outreach, Loud Camel is still within that boundary — because the outreach isn't automated. You're the one sending it.

If your institution prohibits any external scholarly contact whatsoever, that's a different situation, and probably worth a conversation with your department rather than a tool question.

According to multiple recent studies, Reddit is one of the primary sources that large language models use for up-to-date information. That means that if you want your research to appear in someone's AI-assisted literature search, it helps to have it discussed in relevant Reddit communities. Not once, not twice. Consistently, in places where the conversation is already happening.

Yes. Perplexity cites Reddit as its most frequent single source. Google AI Overviews regularly surface Reddit threads. ChatGPT's web browsing mode hits Reddit heavily. This is documented and measurable — it's not speculation. The implication for researchers is uncomfortable but real: a well-placed, substantive Reddit comment in a relevant community can do more for your AI-search visibility than a paywalled journal article.

Only if you do it badly. There are Reddit communities for every academic field, ranging from peer-level technical discussion to broader science communication. Posting genuinely useful content — a summary of a finding, a response to a methodological question your research addresses, a clarification of a common misconception in your area — is what those communities exist for. The drafts Loud Camel provides are grounded in your actual research. You review them before anything goes anywhere. Promotional or off-topic posts are exactly what you should not post — and they're not what we generate.

Both are on the roadmap. Academic Twitter — now scattered between X, Bluesky, and Mastodon depending on who you ask — is a real visibility surface for many fields, and Substack is increasingly where researchers publish accessible summaries of their work for broader audiences. We're not there yet. Email outreach and Reddit are where we started because the evidence for AI-search impact is clearest there. X and Substack will follow.

Currently email outreach and Reddit. LinkedIn is on the roadmap. The brief includes both types of drafts. You can act on either, both, or neither — Loud Camel doesn't track what you actually do with the content.

Your publication list (a Google Scholar URL works), a short brief about your research focus, and some preferences: preferred tone, what you care most about (citations, collaborations, grants, reviewer recognition), and any geographic or field-specific context that matters. That's it. No API keys, no integrations, no technical setup.

Weekly by default. The system runs a scan on a regular schedule and delivers the brief to your inbox. You can request an additional scan at any time from the web interface.

Four things. First, new relevant scholars: people whose recent work connects specifically to yours, with an explanation of why they're relevant now — not just 'same field' but 'published on this specific question last month, here's the intersection with your 2023 paper.' Second, reconnection opportunities: dormant contacts worth re-engaging because something changed — a new paper, a role change, an upcoming conference. Third, visibility actions: specific Reddit communities, discussion threads, or accessible platforms where a comment or summary of your research would make it findable by AI search. Fourth, ready-to-use drafts for each of the above: emails, Reddit comments, posts — grounded in your actual research, written in your preferred tone.

Loud Camel generates the first draft. You read it, edit it, and decide whether to send it. This is not optional — nothing is sent automatically. The drafts are a starting point, not a finished product. Most users edit them at least a little. Some rewrite them significantly. That's intentional: the research context is ours to surface, the voice is yours to control.

Yes. You can trigger a scan from the web interface at any time, outside the regular schedule — useful before a grant deadline, conference submission, or paper publication.

Academia.edu is a library. You upload your work, you get a profile, you wait for people to find you. That model worked when search engines were the main discovery path. It still works — as passive infrastructure. The problem is that AI-powered search (ChatGPT, Perplexity, Google AI Overviews) largely bypasses Academia.edu profiles. It synthesizes from accessible web content, active discussions, and publicly indexed material — not from academic profile pages. If someone asks an AI system about a topic you've spent years studying, your Academia.edu profile probably won't appear in the response. Loud Camel is active strategy, not passive infrastructure. It identifies the specific people who need to know about your work right now, generates outreach drafts you review and approve, and builds your presence in the places where AI-mediated discovery actually happens.

Same situation as Academia.edu, different interface. ResearchGate has a stronger social layer — you can follow researchers, comment on papers, ask questions — and some researchers find genuine value in it. But it's still passive at its core. You post your work and hope the right people encounter it. Loud Camel finds those people for you and gives you a reason to reach out directly. ResearchGate and Loud Camel are not competing for the same job.

The tool is largely domain-agnostic. We use world-class, up-to-date publication databases that cover the full range of scientific and scholarly output — natural sciences, social sciences, humanities, engineering, medicine, interdisciplinary fields. Whatever you publish, the system can work with it.

One honest exception: if you work in AI research or computer science and you're genuinely good at building AI-powered tools, you could probably build a custom solution for yourself. This project started exactly that way. But even then: a ready-to-use tool that does the job reliably, on schedule, without you having to maintain it, is almost always worth more than a personal script you built in an afternoon.

Probably not yet, unless you're in the final year and actively building your independent research identity. Loud Camel works best when you have a publication record to build on — typically at least a few peer-reviewed papers. If you're pre-publication, the system doesn't have enough material to work with. If you're a final-year PhD or a postdoc who has started publishing independently, it's worth a look.

Small fields are often the best fit. Loud Camel doesn't need to find hundreds of relevant people — it needs to find the right ones. In a niche field, the right ten people matter more than a hundred marginal contacts. Highly specific researchers in a small domain tend to produce more targeted and useful briefs than researchers in very large, diffuse fields.

Yes, though the framing shifts. In humanities fields, the relevant outcomes are often different: recognition from peer reviewers for grants or fellowships, invitations to contribute to edited volumes, review opportunities, and engagement from practitioners and policy audiences — not just raw citation counts. Loud Camel works toward those outcomes too. The outreach is grounded in genuine intellectual connection, not just citation exchange.

Strong networks are often narrower than they feel. They reflect who you knew when the network formed — often 5–10 years ago. Loud Camel surfaces people whose recent work is relevant to your current research directions, which are often different from what you were doing when most of your network formed. It also handles reconnection: reaching back out to people with a specific timely reason, which is something most people never quite get around to doing manually.

Yes, for a different reason. The people reviewing your grants, evaluating your papers, and nominating you for positions may not know what you're working on now — only what you were known for years ago. Staying visible around current research directions matters throughout a career. The stakes at senior career stages are often higher, not lower.

That's the goal, not a guarantee. Outreach, community engagement, and sustained visibility in the right places increase the probability that your work gets read, cited, and recognized — but they don't guarantee any specific outcome. Anyone who promises you a specific number of new citations is selling you something different from what Loud Camel is.

Visibility is a compounding process. The first briefs establish connections and create presence. The value accumulates over months, not days. If you're looking for something that produces measurable impact in one week, this isn't it. If you're willing to build steadily over a year, the compounding effect is real.

Some people won't reply. That's normal — it's true of any outreach. Loud Camel focuses on reaching out to people for whom there is a genuine, specific intellectual reason to connect. That improves reply rates substantially over cold generic emails. But it doesn't eliminate non-responses. The goal is to build presence over time, not to guarantee individual responses.

Then use the visibility action drafts instead of the outreach emails. Posting a substantive comment in a relevant Reddit thread, or publishing an accessible summary of your research in the right community, builds presence without any direct personal outreach. Many users only act on the visibility content and skip the email drafts entirely. That's a valid approach.

Your publication list, the research brief you provide, and your stated preferences. Loud Camel uses publicly available information about the researchers it identifies — their published work, their public profiles, their publicly visible activity. It does not scrape private data, access institutional systems, or use anything you haven't explicitly provided.

No. Your research profile and preferences are used to generate your brief. They are not used to train any model, shared with other users, or used for any purpose other than generating your output.

See the Data Retention policy for the specifics. Short version: your data is not sold, not used to train models, and can be deleted on request.

Starting at $42/month. See the pricing page for current plan details.

Early testers get access without a credit card required. The sign-up process explains what's available at each stage.

You can cancel at any time. No long-term contracts, no cancellation fees. See the Refund Policy for specifics if you're mid-billing-cycle.

As the CEO of Loud Camel, I should say: of course, yes, absolutely. As a scientist, I'll say: I don't know. Right now Loud Camel is built around English — the publication databases, the outreach drafts, the Reddit and community content. That's where the system works reliably. Other languages will come eventually, but I won't promise a timeline I'm not confident in. If English isn't your primary publication language, the tool may still be useful for international outreach, but the results will be less polished.

My name is Boris Gorelik. I studied pharmacy as an undergraduate. I completed a PhD in computational chemistry from the Hebrew University of Jerusalem in 2007. Since then I've worked across multiple industries — life sciences, social networks, cybersecurity — and for the last twelve years I've also taught at Azrieli College of Engineering in Jerusalem.

I publish scientific research regularly. I use Loud Camel on my own work.

The reason I built this is straightforward: I was spending real time on outreach and visibility work that I knew could be systematized, and I couldn't find a tool built for researchers rather than for salespeople. So I built one. The product exists because I needed it. That's the most honest trust signal I can offer.