Rigel

Skills-first hiring, built to be honest on both sides.

Rigel is a skills-first recruiting platform. Recruiters define what a role actually requires (specific skills, experience, education) and decide how much each one counts toward the final score. Candidates upload their CV once; Rigel parses it into a structured profile they review and confirm. From that point on, matching is automatic. Every relevant job gets scored. Both sides see the same math.

No keyword filters. No black-box “AI match %.” Re-run the same inputs, get the same result.

Hiring is broken on both sides, in ways that compound each other.

The recruiter's side

Hiring managers and recruiters usually know who they want. Putting it in a JD is the hard part. The result is a mess: priorities upside down, “excellent” and “strong”standing in for measurable requirements, nice-to-haves bleeding into must-haves. The JD reads like a clean spec; the actual criteria are still in someone's head. Candidates can't tell what matters because the people writing the JD couldn't either.

The candidate's side

Meanwhile, parser-based filtering rewards keyword matching over real skill. A new wave of tools tells candidates to “optimize” their CVs for these parsers, making CVs less honest and the filters less reliable. Strong candidates get cut for using different words. Recruiters drown in noise they can't defend.

Vague JDs reward keyword-stuffed CVs.
Keyword-stuffed CVs reward parser-based filtering. The cycle accelerates.

Every new “AI hiring tool” pushes both sides further from actually understanding each other.

We're built to break this cycle.

Not optimize it. Not soften it. Break it.

For recruiters

Recruiters use a structured JD builder that forces the decision criteria into the open. If education counts for 10%, it sits where 10% sits. If a skill is required, it can't also be optional. The spec becomes consistent with itself before it ever reaches a candidate.

For candidates

Candidates upload their CV once, review the parsed profile, and confirm it. They're scored on what they confirmed, not on how well their CV gamed a parser. Every score traces back to specific inputs.

There's nothing to game, on either side.

Two founders, one bet.

Francesca Scipioni

Co-Founder & CEO

Francesca founded Rigel and leads its engineering: the FastAPI backend on Google Cloud, the CV parsing pipeline, the matching engine, and the data layer on Cloud SQL and Vertex AI. She holds a Master of Information in Data Science from UC Berkeley (GPA 4.00) and a PhD in Astronomy from the University of Rome Tor Vergata. Before Rigel, she spent five years at the SETI Institute applying machine learning to NASA Cassini and New Horizons mission data (published in 23 peer-reviewed papers) and earlier worked as a data scientist alongside Monia at NOUS in Italy. Based in the Bay Area.

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Monia Patacchiola

Co-Founder & CSO

Monia leads R&D at Rigel, working on the scientific questions behind the matching engine: how to weight skills, experience, and education in a way that is transparent, explainable, and recalculable by hand, with no AI black box. She holds a Master's in Mathematics and Computer Science (summa cum laude) from the University of L'Aquila, with a thesis in combinatorial optimization. Before Rigel, she built data warehouses for a major Italian telco, managed enterprise software projects, and spent seven years teaching math, physics, and computer science in high school. Based in Lisbon, she speaks Italian, Portuguese, English, and French.

LinkedIn →

Live. Feature-complete. Built on Google Cloud.

Rigel launched in May 2026. The MVP is live at rigelsignal.com and feature-complete: candidate CV pipeline, recruiter job builder with weighted requirements, passive auto-matching engine with explainable per-pair scoring, and email-verified authentication.

Built on Google Cloud: Cloud Run, Cloud SQL, Cloud Tasks, Vertex AI. First real signups landed in May.