Recruiting tools won't get you the hire. The right team will. Contrario combines a network of expert recruiters with AI agents built for hiring. Our recruiters and agents handle 90% of the work — sourcing, screening, coordination, and closing — all from Slack via natural language. With every decision, the system learns your bar and surfaces better candidates over time.
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the curated jd feature sounds lowkey useful. writing job descriptions is the one task i always procrastinate on. does the agent suggest which companies to poach from or do we provide the list?
Love that! Do you plug directly into slack?
@alexandre_berkovic indeed we do :) and we have MCP agents that can take actions on behalf of customers like scheduling interviews and moving candidates forward
How does this process feel different/same for the recruited?
@mariegael Hey Mariegael great question! On the candidate side, it feels the same in the sense that you're still working with the human recruiter throughout the entire process, who is your champion and advocate. It feels different because we've added features like curated lists where you can indicate interest in certain jobs that let you become more engaged as a candidate. What features would be most valuable to you as a candidate being recruited?
Do hiring managers get to see recruiter ratings or track records before they're assigned?
@deven_lathiya Yes HMs can see recruiter track records and relevant experience upon request. Today, because our network of expert recruiters is intentionally curated + relatively small, we’re able to match companies with recruiters using performance data and internal ML models rather than public ratings. We may expand the visibility of recruiter performance metrics as the network scales, but the idea is to work with a highly-vetted group of recruiters who each have demonstrated strengths is the core thesis right now.
As a recruiter, I tend to be skeptical. However, I can tell you that working with the Contrario team the past year has been great. Hard working, innovative, passionate about clients and candidates.
Other companies promise that they will address your hiring with volume and pure breadth of recruitment coverage. Contrario focuses on applying the right matched recruiters with needs resulting is a fast, yet personal experience.
Proud of what the team has accomplished.
@tteshara it's been a pleasure having you on the platform, Tony, and seeing the success you've had with the combination of software/agents and incredible clients who are responsive and engaged. Curious, what's been the most valuable part of the platform for you? What's the biggest feedback you have for us?
congrats on the launch. the network-of-recruiters-plus-AI-agents framing actually makes a lot of sense given how broken the candidate side feels right now.
we see this from the other angle, helping candidates parse and respond to recruiter outreach. so much of what comes through still reads like a template that did not bother checking the resume. the human-in-loop piece you describe sounds like exactly the part that makes calibration possible.
two questions if you have a sec:
what does the feedback loop look like between an agent and the recruiter when a candidate does not fit but is close on one dimension. does the agent learn from the override or wait for an explicit retrain?
and how do you handle the cold-start problem for a startup hiring its first five engineers, where there is no past hire data to calibrate against?
@whateverneveranywhere Hi Ava thanks for the support, and glad you agree with the human-in-the-loop component!
On your first question: within a role, the agent updates in real time off overrides. this means if you relax a dimension on one candidate, the rest of the slate gets rescored within minutes. cross-role generalization is gated though, since one recruiter's "close enough" is another's "not even close," so global updates only happen during periodic calibration passes over many overrides. the thing that made it actually work was asking recruiters to tag why on each override (calibration vs one-off vs exploration) and that's what separates retrain signal from noise.
On your second question: we've placed enough founding engineers across pre-seed and seed startups that we calibrate from comparable roles rather than the company's own history. the cold start is really only a problem when both the role and the archetype are novel, which is rare.
Curious, how do you think we approach completely novel roles with no past hiring data, and do you think the feedback loop between our agent and recruiter right now is the most effective it can be?
the honest framing about tools not being able to make great talent reply to u is the part most ai recruiting pitches skip. pairing expert recruiters with agents instead of trying to fully automate the human bit is a way more believable angle. hell ya !!
@saad_el_gueddari Thanks for the support Saad! Curious - what parts feel automatable by agents now vs. required by humans? Do you think that will change at all in the next 5 years?