Launching today
Nova Recruiter is the world's most advanced agentic platform to find, contact and recruit top talent. Built for recruiters, by recruiters, it allows you to search and contact candidates from +800 million public profiles worldwide. Compared to traditional platforms, it offers better filters (based on merit, not just keywords), higher reply rates (2-3x more thanks to multi-channel campaigns), AI agents to automate the sourcing workflow E2E (+95% of time saved), and usage-based pricing.
What was your worst hire?
The one that made you think: how did this even happen?
👇 Tell me your worst hire story. We read every one.
Andsend
@celia_rico Early in my career I was building a sales team and had to get people who loved dialing. Brought onboard a person who was great at it but turned out to be a needy mytomaniac, occassionally on drugs. Had to let him go. Was hard, because he pulled all these emotional tricks on me. Playing on my kind spirit.
@celia_ricoWhen I hired someone into the team at a level above the value the rest of the team was bringing, because of a specific project need. It broke the team’s structure and coherence. A year later, that person had to leave, because the model has to prevail if you want a scalable structure.
@celia_rico Worst story or common story?
Headhunter and the company are in a hurry to meet you because the vacancy is urgent. So you have to be always available to meet with them, every hour and everywhere. Then suddenly disappear without giving any information :-D
@celia_rico I once hired a young person who was completely unmotivated; she literally told me she was only there because her parents were making her
@celia_rico I once hired a 19-year-old that stole money from the cashier and left dozens of negative reviews after I fired him, stating that "I am being too dramatic on 100€, it's just pocket-money, I wouldn't even feel it" - It's not even the quantity, but the trust.
Nova Recruiter
Hi there! Excited to launch Nova Recruiter to the world through Product Hunt.
Since 2020, we have been building Nova, the network that connects the most talented people in the world.
Think of LinkedIn, but with a merit-based access.
We have scaled the community to +25,000 members after assessing +150,000 candidates, so we know quite well what top talent looks like and how to measure real merit, not just keywords.
To finance this network, we have delivered headhunting services for 6 years to some of the world's most demanding organizations. And, in this process, we realized that recruiters and founders lacked the right tools.
Traditional tools don't work to source top talent for 3 main reasons:
- Filters are based on keywords, not real merit
- Messaging is super limited to InMails, with very low reply rates
- The whole process of searching is super manual and hasn't almost change in the last 10 years.
So we are excited to bring an alternative built FOR RECTRUITERS, BY RECRUITERS. With everything we wished LinkedIn Recruiter had. An agentic platform to source top talent from over 800 million public profiles worldwide and has all the knowledged and experience from assessing +150,000 candidates for true merit these past 6 years.
It has 4 key benefits vs. traditional platforms:
Better search results (thanks to a proprietary search engine, improved filters, talent scoring, and requirement matching)
Higher response rates (2–3x higher thanks to multichannel campaigns across LinkedIn, email, and Nova)
AI agents to automate the process (clients report +20h saved per process, +95% of the whole recruitment time).
More flexible, usaged-based pricing, adapted to the AI world
So if you want a better way to source top talent, try it free now from the UI or from Claude or any other AI agent using our MCP.
Looking forward to getting your feedback.
🚀🚀🚀
@ramon_rodriganez For a tech scaleup hiring fractional sales VPs remotely, how does the talent scoring prioritize "merit" like cultural fit or sales playbook experience over pure skills?
Nova Recruiter
@swati_paliwal great question. To be transparent: our Talent Score doesn't try to judge skills directly (hard to do reliably from a profile) and it doesn't measure cultural fit either, that's something only you can assess in conversation.
What it actually does is filter out the "SEO noise", profiles optimized with keywords and buzzwords, and rank candidates based on the objective signals that correlate with quality: calibre of the companies they've worked at, career progression, educational background, languages, and role seniority trajectory.
For a fractional Sales VP search specifically, you'd layer that with filters on industry, stage (scaleup vs. enterprise), geography, and the specific playbook experience you need (PLG, outbound, enterprise ABM, etc.).
The Talent Score then surfaces the people with the strongest track record within that pool, so you don't waste time on profiles that look great on paper but lack real substance.
Nova Recruiter
@ramon_rodriganez @swati_paliwal Great question! Just to add a concrete example, we analyze the growth of the companies they’ve led. A VP who grew a team from 2 to 20 during a specific funding bridge (e.g., Seed to Series A) is scored higher for your scaleup than someone who simply managed a steady-state department.
This is a great idea. Did you consider making a filter or a separate section for students and young graduates? @ramon_rodriganez @andrea_marino1
I think it would be very helpful for students seeking for internship or a first job.
@ramon_rodriganez @andrea_marino1 @mirkoa I was about to ask just the same. I believe that would be an amazing feature. Apart from that, does the plataform have any way of filtering the job search by industry/sector?
Nova Recruiter
@ramon_rodriganez @mirkoa @mamen_cortes Hi Mamen! To answer your question: Yes, we do have industry and sector filters! You can narrow down your search to ensure the candidates (or roles) align perfectly with specific markets.
Keep in mind that the Nova Recruiter is an outbound sourcing tool for recruiters, not a job-seeking platform for candidates.
If you're a candidate looking for a job, you would rather sign up to Nova and be part of the network instead.
Nova Recruiter
@ramon_rodriganez @mirkoa Hi Mirko, right now, we’re laser-focused on helping hiring managers cut through the noise using our Talent Score. However, we love the idea of a dedicated section for students and grads, meritocracy should start at Day 1 of a career. I've shared this with the product team.
If you are asking whether recruiters can find students on the platform, the answer is yes. It's as simple as setting the years of work experience to a maximum of one or zero.
800 million profiles is massive scale. the multi-channel campaigns catching my attention too - are you integrating email, LinkedIn, and other channels in a single workflow? traditional recruiting tools make you jump between platforms constantly.
Nova Recruiter
@piotreksedzik indeed, you can use email, LinkedIn and Nova in the same messaging workflow!
Nova Recruiter
@piotreksedzik Including replies from all channels get centralized in one place, very convenient and a time saver.
Viseal
multichannel campaign outreach looks very handy. Question: natural language to search seems to be less accurate and often the requirement specification is already listed for the job by domain leaders. Does it prioritize analyzing the job description to search for matches? How do you make sure at there is no age, gender, race biases in the AI recommendation? Thanks, and congrats for the great product.
Nova Recruiter
@hwellmake
Hi Ji-Ling! Thanks so much for the kind words and for diving into the details. Those are two of the most important questions we tackle at Nova. Here’s how we handle them:
1. Accuracy and search priority
You’re spot on, natural language is great for flexibility or when you don't know where to start, but professional requirements need precision.
JD-First Approach: Nova actually prioritizes the Job Description (JD) or specific hiring manager intake criteria. Instead of just relying on a "chat" interface, you can upload your full requirement spec.
Beyond Keywords: Our AI doesn't just look for "Python" or "Project Manager." It maps the JD into structured criteria (must-haves vs. nice-to-haves) and calculates a Talent Score®. This looks at career trajectory, company selectiveness, and actual achievements rather than just matching words, which significantly cuts down on those "less accurate" results you see in standard natural language tools.
2. Solving for bias
We believe AI should be a tool for fairness, not just speed. We’ve built several "guardrails" into the engine:
Merit-Based Intelligence: The algorithm is trained to ignore "noise" like names, gender, or age markers. It focuses strictly on demonstrated capability and career progression.
Regular Bias Audits: We perform continuous testing (using "synthetic" resumes) to ensure that candidates with identical qualifications but different demographic markers receive the same scores.
Transparency & Explainability: Nova isn't a "black box." For every candidate recommended, we provide cited evidence from their profile explaining why they scored that way. This allows you to verify the recommendation and ensures the decision-making remains human-led and defensible.
Compliance: We are fully aligned with the EU AI Act and GDPR standards, which mandate high levels of transparency and fairness in automated hiring.
We’re all about "merit-based" hiring, getting the right person for the job based on what they can actually do. Would love to hear if you have any other thoughts on this!
Viseal
@andrea_marino1 thanks for the clear explanation. it's impressive to know that so many layers are implemented to provide a accurate and fair match.
Nova Recruiter
@andrea_marino1 @hwellmake Thanks Ji-Ling! Fairness and accuracy are non-negotiables for us, the whole point of building Nova is to make the world more based on merit. If you ever want to stress-test it with a real search, just let us know, we'd love your feedback. For instance, we are now considering removing images from profiles. Although recruiters are used to them from LinkedIn, we believe they do not add value and are prone to bias
@ramon_rodriganez Hi! Congratulations on the launch! Sounds like a very handy service, especially having multichannel outreach. How do you provide Linkedin messaging service? Does it have any limitations on number of messages or threads? Usually it's quite challenging to automate anything Linkedin-related as they have no official API for this and any automation is against their policies. How do you mitigate it? Thank you!
Nova Recruiter
@alex_vavilov good question.
- For free users, the tool helps you first connect with potential candidates and then send them messages as they accept you. Limits are 100 connection requests / week
- For LinkedIn paid users (Sales Nav, Recruiter Lite, Recruiter RPS / Corporate) users have higher limits on the connection side and will enjoy InMails very soon as well!
We offer limit control fro users to avoid having their accounts banned
Nova Recruiter
@alex_vavilov @ramon_rodriganez something cool is that if LinkedIn limits are reached, Nova’s "agentic brain" automatically pivots the outreach to Email or Nova Direct to keep the campaign moving without over-extending your LinkedIn profile.
Essentially, we act as a "digital co-pilot" that does the manual work for you, while keeping the activity 100% organic and compliant with LinkedIn.
interesting approach on the merit-based filters vs keyword matching. we've seen how keyword searches miss great candidates who describe their skills differently. what kind of signals are you using to determine merit beyond the obvious resume markers?
Nova Recruiter
@piotr_pasierbek super relevant question, that’s exactly where the "magic" happens. Most AI recruiting tools use a generic language model to guess what a "good" school is. Over the last 6 years, Nova has built its own proprietary global taxonomies, a structured "brain" that knows the difference between a high-achiever and a high-volume resume.
By analyzing millions of data points from the top 3% of global talent in our network, we’ve built some core ranking engines:
1. The Selectivity engine (Employers)
We don't just look at company names; we look at Company Pedigree. Tiering: We’ve mapped over 100,000 global employers into tiers based on their hiring bar. A "Product Manager" at a Tier 1 (e.g., Stripe, McKinsey, or a top-tier YC startup) is weighted differently than a PM at a legacy firm with lower selectivity.
Talent Flow: Our taxonomy tracks where the "best of the best" go. If we see a high concentration of the top 3% moving to a specific mid-sized startup, that company’s "Pedigree Score" rises in our system.
2. The Academic prestige index (Schools & Degrees)
While we advocate for merit, academic background remains a strong early-career signal, if interpreted correctly. We focus a lot on degree rigor: our taxonomy understands the "difficulty curve" of specific degrees at specific institutions. It knows that a 3.8 GPA in Physics at ETH Zurich represents a different level of technical rigor than the same GPA in a less quantitative field at a lower-ranked school.
On top of this, we value things like language skills and international experience as they strongly correlate to professional performance. I hope this gives a bit more context.
Nova Recruiter
@piotr_pasierbek @andrea_marino1 One more angle to add: we also weight career velocity (how fast someone progresses vs. peers in the same role/tenure) and real output signals when available (open-source commits, patents, publications). Titles are cheap, trajectory and shipped work are not.