What is TrySignalHire?
TrySignalHire is an early AI hiring product focused on real technical candidate signal. Candidates create one evidence-backed packet from real work, and companies review sourced proof before resumes flatten the signal.
Frequently asked questions
Plain answers for people, search systems, and AI crawlers trying to understand who TrySignalHire is for, what it is not, and why it exists.
What is Signal OS?
Signal OS is the working system behind the TrySignalHire workflow. The first version turns projects, writing, recommendations, role context, job context, and proof notes into reviewable match signals with sources visible.
Who is TrySignalHire for?
TrySignalHire is built first for software engineers and technical job seekers with real work to show. The company-side audience is technical recruiters, founders, HR partners, and hiring managers who want to inspect sourced proof before another keyword screen.
Why are you building this?
Jesse Peplinski is building it from lived job-search pain after roughly 40 applications, many repeated profiles, and the feeling that current AI-heavy hiring funnels are missing trust.
Is TrySignalHire available today?
TrySignalHire is in early access. The public site is live, candidate and company profile foundations are underway, and the first one-profile-to-one-job matching loop is the current MVP focus.
Does TrySignalHire make hiring decisions?
No. TrySignalHire is not meant to make hiring decisions or replace human judgment. The goal is to make candidate evidence easier to review and harder to overlook.
How does TrySignalHire use AI?
AI can help organize, summarize, and connect candidate evidence to job requirements, but the product keeps claims tied to real sources instead of generating unsupported hiring theater. App code calculates the score; a person still makes the decision.
What candidate evidence does it use?
The candidate packet can include projects, writing, resume context, recommendations, role targets, tradeoff notes, and other proof that helps explain what someone can actually do.
How does auto-matching work?
Auto-matching compares a company's open job against candidate packets that opted into discovery. The first version uses AI to organize public evidence into match signals and deterministic app logic to score evidence overlap, preference alignment, proof coverage, and profile completeness.
How does company review work?
The company view is intended to show sourced claims beside the underlying candidate evidence, context, score breakdown, review prompts, and response timer so a hiring team can understand the person behind the profile before relying on another keyword pass.
What is the 24-hour response window?
The MVP shows a 24-hour response tracker once a candidate match enters a company queue. That tracker is meant to respect candidate time first. Email and text follow-ups are planned later.
Is TrySignalHire free for candidates?
Not for the founding pilot I am testing publicly. Candidate access starts with a 7-day card-upfront trial. Founding users can lock in $10/month before the candidate rate moves to $15/month. It does not guarantee interviews, offers, or placement.
How will company pricing work?
Company pilots start with a 7-day card-upfront trial. Founding company pricing is $50/month while active before the company rate moves to $100/month, plus a 15% first-year salary success fee when TrySignalHire helps create a real hire.
What about privacy and early access?
The early-access form collects contact details and can include an optional phone number if you are open to a direct feedback call. There is no newsletter; the information is for TrySignalHire early-access follow-up.
Who is building TrySignalHire?
TrySignalHire is built by Jesse Peplinski in Syracuse, New York. You can join early access on the site or text or call the founder at (315) 565-6061.
Will personality assessment be part of the product?
Personality or working-style assessment is a future area being explored. It is not the current core product, and it would need to support context and trust rather than reduce people to a simplistic score.