For companies
Hire AI engineers on evidence, not assertions
A résumé is a claim. A repository is a record. SensAI reads the public work an engineer has already published — repositories on GitHub, models on HuggingFace — and hands your team a dossier of what they have built, where the gaps are, and whether the profile is genuine.
Ajai GeorgeLast updated
What happens before a name reaches you
Agents source candidates from public work, vet the profile, interview against the role, and validate that the engineer behind it is real. Your team sees the result, not the queue.
The Problem
Why AI hiring is broken
Three active failures — most companies are experiencing all three simultaneously.
SCARCITY
The talent pool is smaller than you think
Engineers who genuinely build and ship production-grade AI systems are rare. Every serious AI team is competing for the same few hundred people. The bottleneck isn't budget — it's signal.
FRAUD
Most "AI engineers" aren't
The market is flooded with candidates wrapping LLM APIs and calling it AI engineering. AI-generated portfolios. Padded GitHub histories. Traditional interviews cannot tell the difference. Résumés lie. Public repos don't.
WRONG PLAYBOOK
AI is still hired like it's 2015
Most companies run the same job-board, keyword-filter, panel-interview process they used for Java developers. AI engineering demands a different evaluation entirely — depth, shipped models, trajectory of growth.
SensAI was built for this exact problem.
Evidence-based evaluation. A legitimacy check before anything is scored. An assessment shaped around how AI engineers actually work.
The two questions buyers ask first
Is this GitHub profile real?
The signals we read, the judgment we make, and the limits of what public data can prove.
How we check legitimacy →What does the score actually measure?
Five dimensions, what each one reads from, and how a legitimacy verdict constrains the result.
Inside the Talent Intelligence Score →