Proctored coding sandboxes, AI resume ranking, and behavioral analytics, so you interview the right candidates, not the best test-takers.
Analytics
Overview of assessment performance and user activity
127
Total Assessments
14 completed this week
61
Total Users
22 of 61 users
36.1%
Completion Rate
Across active sessions
5h 11m
Avg Duration
Across completed sessions
Status Breakdown
User session statuses
Language Distribution
Users by programming language
Top Performing Assessments
Assessments ranked by completion rate
0×
Faster screening vs. resume-only review
0%+
Anti-cheat accuracy with on-device proctoring
0
Behavioral dimensions in our Developer DNA score
0-tier
RBAC with hard org isolation across tenants
Each candidate gets an isolated, ephemeral VS Code environment provisioned on demand, no installs, no setup, no environment drift.
Multi-cloud isn't a roadmap item. Pick the provider per assessment template, with first-class IAM and console access on each.
Branded forms, AI resume ranking, adaptive coding tests, voice interviews, side-by-side comparisons. Every signal flows into the same decision view.
Single token-based URL routes through every stage
JD or resume-pool match dictates which template loads
Every keystroke, edit, paste, run captured for review
Ranked, rubric-scored, ready to push to your ATS
An AI interviewer that talks like a senior engineer pair-programming with the candidate — and actually sees their screen. Not a tour of pre-recorded questions; a real conversation about the code in front of them.
1 function twoSum(nums: number[], target: number) {
2 const seen = new Map();
3 for (let i = 0; i < nums.length; i++) {
4 const diff = target - nums[i];
5 if (seen.has(diff)) return [seen.get(diff)!, i];
6 seen.set(nums[i], i);I see you went with a hash map on line 4, what was the trade-off you weighed there over a sorted array?
Constant-time lookup. Sorting would be n log n up front, and we hit the lookup more than once.
Got it. And what happens if the input array has duplicates?
Two-way sync with Greenhouse and Lever. Scorecards, status callbacks, partner errors, all in the system your TA team already lives in.
Face presence, multi-person detection, phone detection, off-screen gaze, all running on the candidate's machine with severity-graded events.
Bulk-assign 14 candidates · Senior BE Round
Senior Backend Engineer · v3
candidate@example.com
Delete resume pool · Q3 Frontend
Frontend Intern · Spring '26
An audit log that's good enough for compliance and useful enough for daily ops. Admins see who did what, when, and to which record — without paging an engineer or scraping logs out of the database.
From the moment a user sees your job post to the moment you make the offer — every step lives in one platform, with the data flowing through.
Public, slug-routed application forms on your subdomain. Custom fields, resume uploads, slot booking — collect everything you need before they touch a coding test.
Bulk-upload resumes, parse with Gemini, embed and search semantically. Find the users who match the JD, not just the keywords.
Coding, MCQ screening, voice interview — mix and match. Templates per role. Auto-route users by skill detected from their JD or pool match.
Side-by-side comparison, ranked user views, replay timelines, hiring outcomes tracking — and a clean handoff to Greenhouse or Lever when you're ready to offer.
Most platforms in technical hiring own a single slice of the funnel and stop there. HireYouGo is built to carry the candidate — and the data — from first application all the way to the offer letter.
One stage of the funnel, usually just the assessment, or just the ATS, or just the interview.
End-to-end: branded application → AI resume pool → assessment → voice interview → offer handoff.
Sandboxed editor with a fixed language list, no terminal, and no real filesystem.
Full cloud workspace, install packages, run servers, work across multiple files.
Tab-switch flagging and a webcam thumbnail.
On-device proctoring with a replayable timeline of every keystroke, paste, and run.
A pass/fail score against a rubric.
Behavioral DNA, five dimensions of how the candidate actually thinks, replay included.
A separate vendor, or no support at all.
Built-in adaptive voice interview that reads the candidate’s own code in real time.
CSV export with manual field mapping at the other end.
Native push to Greenhouse and Lever with structured outcomes, scores, and replay links.
Application-level filters that depend on bug-free app code to keep tenants apart.
Postgres row-level security plus service-role isolation, enforced at the database.
Comparison reflects typical capabilities across the technical-hiring category. Your current vendor may differ on individual rows.
Security and data isolation aren't add-ons — they're wired into the schema.
Postgres RLS enforces every query. A bug in app code can't leak data — the database itself filters by org.
Privileged operations run server-side via service-role tokens. Client-side keys can't escalate.
Every recruiter request is logged with requester, reviewer, decision, and outcome. Replayable. Defensible.