Product Technical assessment platform

Technical assessments that show how candidates really work, AI included

Online coding tests, real-world projects, SQL, system design and AI challenges with automated scoring, an AI-assisted IDE and cheating detection.

‹
Question 1 — High and low
Section: 00:07:57
Pause (1 of 2)
Next
Description
You receive a sequence of space-separated numbers and must return the highest and lowest number.
Note:
•All numbers are valid Int32, no need to validate them.
•There will always be at least one number in the input string.
•Output is two numbers separated by a single space; the highest comes first.
Your camera · Live
⌃
Camera on
Proctoring active during the assessment
main.kt
Kotlin (1.3.70)
1class Solution {
2
3 /**
4 * Complete the 'highAndLow' function below.
5 * @returns {String} The expected output.
6 */
7 companion object {
8 fun highAndLow(numbers: String): String {
9 val nums = numbers.split(" ")
10 .map { it.toInt() }
11 val high = nums.max()
12 val low = nums.min()
13 return "$high $low"
14 }
15 }
16}
Tests
RUN TESTS
Test 1numbers: 1 2 3 4 5Passed
Test 2numbers: 1 2 -3 4 5Passed
Test 3numbers: 1 9 3 4 -5Passed
Code quality
3/3 tests passed
Correctness100%
Code readability88%
91
Skill matched — 91%
Recommended for next stage

AI does the heavy lifting. Your team makes the call.

While a recruiter can interview 5 people a day, Coodesh's AI tests, interviews, scores and ranks hundreds in the same time, using the same criteria for everyone.

1

Start from your job description

Set up technical assessments based on the role or the skills you want to evaluate.

2

Invite candidates

Send invites by link, email or straight from your ATS. Cheating detection and image proctoring turn on according to your policy.

3

Review and compare with automated scoring and cheating detection

Compare assessed candidates side by side. The best part? Scoring and suspicious-activity detection are handled for you.

1M+
candidates already assessed on the platform
4x
faster to identify qualified talent
99%
assessment accuracy
70%+
less time spent on the hiring process

Traditional hiring vs. Coodesh

What changes when screening no longer depends on résumés and gut feeling.

Traditional process
✕Manual résumé screening that takes days
✕Tests built and graded by hand
✕Low candidate engagement
✕Subjective, inconsistent results
✕No prioritization: every candidate looks the same
With Coodesh
✓Automatic candidate ranking in hours
✓30+ technologies, with AI-powered automated scoring
✓92% candidate engagement rate
✓Accurate, comparable and defensible reports
✓Prioritize the best candidates and interview fewer

Ask AI about Coodesh

ChatGPT logo
OpenAI
Gemini logo
Gemini
Claude logo
Claude
Grok logo
Grok
Perplexity logo
Perplexity
DeepSeek logo
DeepSeek

Key features

Advanced tools to streamline technical hiring and skills assessment.

Start for free

Coding test and interview library

Cheating detection and image proctoring

AI and automation

Certifications, development plans and learning paths

Take the guesswork out of hiring tech talent

Online coding tests with AI, or live coding

Measure what now sets great software engineers apart: direction, verification and judgment.

Coding test — AI-assisted, like real work
Section: 00:24:10
AI use tracked
Submit
rate_limiter.ts
TypeScript (5.4)
1class TokenBucket {
2 constructor(rate: number, capacity: number) {
3 this.tokens = capacity;
4 this.lastRefill = Date.now();
5 }
6
7 // AI suggestion: refill inside the lock
8 tryAcquire(): boolean {
9 const now = this.refillAndGetTime();
10 this.tokens = Math.min(this.capacity, …);
11 return this.tokens >= 1;
12 }
13}
8 / 8 tests passed
RUN TESTS
AI assistant
Claude ▼
Why is my bucket refilling too fast under concurrent calls?
You call refill() before reading lastRefill — two callers can both see a stale timestamp. Move the timestamp update into the same critical section.
Ask the assistant…
Every prompt reviewed
Prompt qualityStrong
Copy without reviewNone
Iterations with AI4
Bring your AI stack
Your choice
Coodeshbuilt in
Claudeconnected
Codexavailable
Coodesh assistant built in · or connect Claude / Codex · full prompt history in the report
89
AI collaboration — 89%
Prompts, judgment and code all scored

Turn technical interviews into hands-on sessions

Live coding interviews

Run more effective live coding and whiteboard technical interviews, and see how candidates think and solve problems.

Live
Live coding — Senior Backend Engineer
SR
DK
AL
00:31:04
End session
Question 2 of 3
Rate limiter middleware
Implement a sliding-window rate limiter for the API gateway.
From template library
Backend · System design
Data structures · Warm-upDone
Debugging · Follow-upUp next
Integrity monitor
Active
Copy/paste trackingClear
Tab focus monitorClear
AI-code detectionClear
rate_limiter.py
2 editing
Python 3.12
1import time
2from collections import deque
3
4class RateLimiter:
5 def __init__(self, limit, window):
6 self.limit = limit
7 self.window = window
8 self.hits = {}
9
10 def allow(self, key, now=None):
11 return len(q) <= self.limitSofia · candidate
12
13# What about burst traffic at the window edge?Daniel · interviewer
All tests passing 4/4 · 0.8s
RUN
Scorecard
Shared live
Problem solving4/5
Code quality5/5
Communication4/5
No cheating detected
Plagiarism · paste events · AI-generated code

System design interviews to assess software architecture

System Design / Whiteboard

Find out whether a candidate can design a system that holds up in production, not just recite design patterns.

Live
System design — Design a URL shortener
SR
DK
00:24:18
End session
Whiteboard
2 drawing
HTTPSroutereadwrite
Client
Load balancer
APIx3
Cache
DB — shardkey: hash(short_id)
TTL on cache? Discuss eviction policy — LRU vs LFU
Sofia · candidate
Daniel · interviewer
Prompt · from template library
Scale to 100M URLs/day. Cover storage, caching and hot-key mitigation.
Session integrity: clear
Scorecard
Shared live
Architecture & trade-offs5/5
Scalability reasoning4/5
Communication4/5
Architecture captured
Board snapshot saved to the report

Advanced proctoring and monitoring

Cheating detection

Catch sophisticated fraud and AI misuse with telemetry, pattern analysis and full session monitoring.

Proctoring & integrity — live monitoring
Recording session
View timeline
Image proctoring
Live · AI analysis
Face verified
CameraOn · blocked if off
Face coveredNot detected
Second personNot detected
AI-use detection 1 flag
Pasted without copy1 event
AI-generated textLow risk
Typing patternHuman-like
Activity timeline Screen recording · playback up to 32×
00:00Tab switch · 12:40Copy/paste · 31:0558:00
Second screenExternal monitor blocked — 1 display detected00:02
Tab switchLeft the assessment window for 4s12:40
Copy/pasteText inserted without a paste event31:05
Location & deviceSingle IP · Computer · Chrome · BR58:00
Full audit trail
Every test
Location, device and browser logged
Copy/paste and tab-switch tracking
Full screen recording · 32× playback
Second-screen blocking · AI image analysis
Integrity you can trust
AI analysis · human review when it matters

Coodesh inside your AI stack

LLM and MCP integration

Bring your own model, connect your agents via MCP and keep people data within your policy, with no vendor lock-in.

Integrations — LLMs & MCP
MCP server: online
Get API key
MCPscoreinvitereport
ClaudeAnthropic
GPTOpenAI
Any agentGemini · Llama · custom
Coodesh MCP Model Context Protocol server
18 tools OAuth 2.1
Assessmentscreate · score
AI Interviewerinvite · transcribe
Reportsresults · insights
Your recruiter's agent, talking to Coodesh mcp.coodesh.com
> Invite the top 3 React candidates to a senior assessment
✓ coodesh.search_talent · 128 matches
✓ coodesh.create_invite · 3 invitations sent
Built for AI workflows
API-first
Native MCP server — works with Claude, GPT and any agent
REST API + webhooks for every product
Scoped keys, OAuth 2.1 and full audit log
Plug into any LLM
One protocol · your whole hiring stack

Assessments inside your hiring workflow

ATS integration via API

Trigger assessments when an application changes stage and get results back on the candidate card, with no spreadsheet exports or switching screens.

API integration flow — ATS & HR systems
Webhooks: active
Read the docs
Your platform
ATS · Job board · HRIS
Coodesh
Assessment engine
Candidate
Any device
List available assessments Assessment catalog Send candidate to start Invite to take the assessment Takes the assessment Results webhook GET /assessments POST /invites POST your-webhook/results
API REST · MCP
OAuth 2.1WebhooksSandbox
Docs: coodesh.com/docs
Same journey, your stack
Server-to-server
List assessments and send candidates via API
Candidate experience stays on Coodesh
Results pushed back by webhook in real time
Connect your ATS in days
REST + webhooks · sandbox included

Why companies choose Coodesh

Replacing an employee can cost one-half to two times their annual salary (Gallup). Every feature below exists to avoid that bill.

Better-fit candidates

Assessments built from the real job. Less guesswork, more evidence of skill.

AI-powered automated scoring

Code, written answers, interviews and case studies scored instantly, with a score and rationale.

Anti-cheating and identity checks

Face recognition, webcam, AI-use detection, tab-switch and copy-paste tracking, and screen recording.

Accuracy without bias

The same criteria for every candidate. Defensible decisions that respect privacy laws such as GDPR and LGPD.

Works with your ATS

Greenhouse, Lever, Gupy, LinkedIn and others via open API. Results flow back into your workflow.

Decision-ready reports

Rankings, side-by-side comparison and individual reports to share with the hiring manager.

Roles and skills we assess

By role

By skill

Programming languages and technologies assessed on Coodesh

Fully customizable coding tests and technical challenges.

All your favorite tools, connected

Our ecosystem of deep integrations makes it easy to streamline your technical hiring processes.

SólidesAlura para EmpresasLinkedInUdemyGithubGreenhouseExcelInHireRecrutaiGupy

Frequently asked questions

Is the platform only for tech roles?

No. Our assessments cover every digital role, including sales, marketing, design, product, customer success, software development, cloud, AI and more.

How does Coodesh handle AI use during coding assessments and interviews?

Coodesh was built for an AI-first hiring world. You decide when candidates or employees may or may not use AI in your hiring or certification process.
If a test or interview is designed to allow AI, scoring also considers how well the candidate uses it. If the instructions explicitly forbid it, AI use is flagged as suspicious activity. That means you can run assessments that test fundamentals without AI, or measure a candidate's ability to work with AI.

Can I customize the coding tests?

Yes. You can build role-specific tests that combine questions from our library with your own custom challenges.

How does cheating detection work?

We use active proctoring, IP tracking, video analysis, plagiarism and AI-generated code detection, and behavioral analysis to protect the integrity of results.

Can I integrate Coodesh with my ATS?

Yes. We integrate with Greenhouse, Lever, LinkedIn, Gupy and other leading tools, and any ATS can connect through our open REST API and webhooks.

Can I use the platform to develop my current team?

Absolutely. Beyond hiring, Coodesh helps you spot skill gaps and track the progress of your existing team.

Do I need technical knowledge to use the platform?

No. The interface is intuitive, so any recruiter or hiring manager can set up and run tests easily.

Which programming languages and frameworks can I assess with coding challenges?

Many of our assessments can be taken in any of the following languages and frameworks:

‍Languages: C#, C++, CSS, Go, HTML, Java, JavaScript, TypeScript, Kotlin, Elixir, Swift, PHP, Python, R, Ruby, Scala and Dart.

‍Tools and frameworks: .NET Core, Angular, Spring, Laravel, Mocha, Node.js, React, Next.js, Redux, Django, Flask, FastAPI, Selenium, Cucumber, Serverless, Vue.js and React Native.

‍Other: iOS, Android, Docker, Git, GitLab CI, Helm, Kubernetes, MongoDB, MySQL, PostgreSQL, RSpec, Spark, SQLite and Terraform.

Supported By

Scale UP EndeavorNIU VenturesThe Venture city

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