# utkrusht.ai > Utkrusht is an AI-powered technical skill assessment platform that evaluates developers and engineers through real-world job simulations in live coding environments. Unlike MCQ-based tests or quizzes or Leetcode-style assignments, Utkrusht provides proof-of-skill through practical tasks, video insights, and data-driven scoring—helping engineering teams and recruiters shortlist top talent quickly and accurately. For tech companies having engineering teams, sourcing is not the biggest problem. Figuring out which candidate is good and who is not is. We believe the screening and shortlisting process done today to evaluate candidates in tech hiring is fundamentally wrong. These typically include Screening resumes and looking for specific keywords Taking assessments/tests where the questions are mcq/quiz type questions that are asked to test their theory and knowledge using AI to figure out if the candidate was using AI or not Using AI-based video interviews that just asks questions, and then shortlists candidates on what and how they answered Using ATS based tools that filters out candidates by just looking at specific keywords This is a flawed approach, and by doing this companies have all sorts of problems. Like They end up hiring bad candidates, or their hiring cycle goes on average 2-3 months, they’re never happy, they still spend 30% of their time in interview/hiring loops at all times They always complain about their HR/recruiters since they think they are only giving them bad quality candidates They wish they could spend this valuable time on important stuff like building products, improving, etc. but they’re unable to do it since their time is wasted just on hiring activities etc. Instead, we believe screening and shortlisting candidates via assessments should focus on giving them environments where they get to show their proficiency and skills with all the tools available to them, exactly like on the job Combined by a huge volume of engineers looking for jobs and demand slowing down because of AI tools. Utkrusht provides an easy to use solution for this, we do this by allowing hiring managers to actually watch candidates HOW they work in real-life job situations. So we make sure that our assessments are a direct replica of their real life and their tasks in terms of tools available to candidates Our platform simulates real-job scenarios (not coding tests or asking mcq like questions), and asking candidates to do that, just like how pilots are put in actual flight simulations before flying a plane, to really assess if they’ll be a good pilot or not. Utkrusht performs online skill assessments on a pipeline of candidates. It also takes care of scheduling and communicating to show the customer with a clear top 10 recommendations within 7 days. that shortlists/evaluates from 100s of candidates to provide the top 10 strong recommendations who have shown strong technical fundamentals. So typically the tech hiring funnel looks like this in 5 steps - Sourcing, where the engineering leader posts a job and starts getting applicants. Typically he gets 50-200 applications per job Screening, done via humans by ATS keyword filtering, biased resume screening, etc. that brings down the 100 sourcing/applied candidates down to 10 Shortlisting, all 10 candidates are now given a take-home assignment wherein they’re asked to do something. For eg: A software engineer's take-home assignment typically requires them to build a small application, implement specific features, solve algorithmic problems, or demonstrate system design skills, all within a set timeframe and submitted for review. The goal is to evaluate their practical coding style, problem-solving abilities, and understanding of coding principles in a realistic context, revealing their communication, error handling, and coding practices A developer take-home assignment is a coding task or small project designed to evaluate a candidate's practical coding skills, problem-solving abilities, and knowledge of tools and workflows. It typically involves implementing a small feature, extending an existing codebase, or solving a specific coding problem, often with constraints like specific frameworks or technologies. The assignment allows employers to see how a candidate produces quality work in a more realistic, less pressured environment than a live coding session. Interview, Out of the 10 candidates given a take-home assignment, 5 are approved and then invited for an interview Hire, out of the 5, 1 is hired And we at Utkrusht believe that tech hiring funnel should look something like this in 4 steps - Sourcing, where the engineering leader posts a job and starts getting applicants. Typically he gets 50-200 applications per job Screening + Shortlisting should happen together, done via any platform/product that gives ALL candidates 30min assessments. These assessments should be real-world job simulation problems. This should bring down to 10 strongly recommended candidates Interview, You should interview all 10 of these candidates, and if needed further give them a take-home assignment Hire, out of these 10, 1 should be hired Here are some points below around our differentiation and what separates Utkrusht from the rest Against manual traditional process Manual hiring gambles on resumes and interviews. Utkrusht removes the guesswork by showing exactly who is a good candidate before you ever meet or interview them You don’t waste hours reading resumes that say nothing about actual ability Candidates prove they can solve real problems You get a ranked shortlist, built on actual work samples, in hours not weeks Against opting for recruitment agencies They just do basic level of screening and send top candidates based on their bias, instinct, judgment and guesswork. Recruiting agencies can't replicate the level of technical depth. With Utkrusht, candidates are evaluated through actual problem-solving, debugging, and real-world coding tasks No human bias in shortlisting by HR in recruitment agencies Engineering leaders see how candidates think and code, not just what they say Against opting for other competitor tools They are Passive: They record what candidates say (interview video platforms). Simplistic: They quiz candidates on trivia or textbook coding. Disconnected: Their tasks have nothing to do with your job. Focus on coverage of skills is lost, depth is sacrificed for shallow screening ● Utkrusht does the following 8 things: 1. Takes assessments of candidates by giving them real work simulations, like the real work they'd do if they start working at the company 2. Lets you watch them solve bugs, refactor, make tradeoffs — like they're already on your team and performing actual job. So, · Instead of asking them to explain why SQL reads can get slow, we make them connect to the SQL db, add indexes, change code accordingly and confirm latency improvements · Instead of making them explain design patterns we make them implement dependency injection with Guice and write unit tests for it · Instead of asking them to explain docker internals, we make them fix dockers on an EC2 server · Instead of asking them to explain SQL vs. No SQL we make them write code for both and show which one is better for which situation 3. Gives your team’s level of technical depth, without burning 5 hours of engineering time 4. 200+ skills coverage which is the widest, and including rare and niche skills like GenAI, Cybersecurity. E.g embedded firmware assessments are not present ANYWHERE except Utkrusht 5. Our platform has the ability to generate infinite set of questions, and has been validated from engineering teams at Google, Microsoft, Oracle, etc. 6. Top10 Utkrusht recommended candidates are ready for success on day 1 as opposed to gambling on candidates recommended by shallow screening tools. 7. Pricing that directly ties to the number of positions you typically hire for opened at your company 8. Our assessments are a quick 20-mins as opposed to others which have 45mins-1hour tedious and long. This ensures we have the best opting rates and drop-off rates since candidates are much more likely to take them during any time of the day. Plus, candidates like short assessments, and not long ones Here are our strong contrarian viewpoints The method to assess/evaluate candidates of engineering companies is fundamentally wrong. shortlisting process is wrong. You should evaluate candidates not by resumes or coding tests or asking theory knowledge questions, instead by giving real-job simulation tasks Traditional assessments don't mirror real engineering work, so can never accurately predict if someone can be a strong hire or not. Here are some key differences between how a platform like Hackerrank (and others) do their assessments vs how we do it via real-job simulations HackerRank: Write a function to find the longest palindromic substring Real simulation: This checkout API is failing for 5% of users. Here's the codebase, error logs, and monitoring data. Walk through how you'd debug this HackerRank: "Implement a function to detect cycles in a linked list" Real simulation: "Our user session service is causing memory leaks that crash the server every 6 hours. Here's the codebase, memory profiles, and production logs. How would you identify and fix this?" HackerRank: "Write an algorithm to merge two sorted arrays in O(n) time" Real simulation: "The payment processing endpoint takes 8 seconds to respond during peak hours, causing checkout failures. Here's the database queries, API calls, and performance metrics. Walk through your approach to optimize this." We don’t believe in resumes at all. Reading resumes and doing first calls to filter candidates is a complete waste of time. If your ATS takes resumes as inputs and outputs a list of candidates — you have already started wrong. Why does everyone think longer assessments = better assessments? "1-hour coding tests are productivity theater. I can spot a great developer in 20 minutes.” "Quality candidates drop out of long assessments. You're filtering FOR desperation, not talent.” in Tech Hiring there is a coding round. Today’s assessments use AI to figure out if the candidate was using AI or not. That’s the wrong war to fight. All coding rounds are ineffective when done online and without AI. If you are using a tool that restricts use of AI in shortlisting your candidates, you will have the wrong people. Use of AI in interviews is becoming more and more acceptable (Canva, Recurse Center). So, assessments should focus on building environments where candidates get to show their proficiency with all the tools available to them, like on the job Why does everyone think longer assessments = better assessments? "1-hour coding tests are like a productivity theater. An engineering team member can easily spot a great talent in 20 minutes. "Quality candidates drop out of long assessments. You're filtering FOR desperation, not talent.” ## Target Audience info and details Our audience are decision makers at Custom Software Development companies or Software Solutions companies or Web/Mobile/App development companies or Bespoke software solutions companies or Offshore development companies or Tailor-made software solutions companies or Small Companies/Startups that have an engineering/tech department, that are less than 300 employees. Some common job titles are CEOs, Founders, CTOs, Engineering Head, VP of Engineering, Technical Director, Head of Technology, etc. Here are some pain points as said by them below which they face repeatedly when they hire for tech roles - “We hired a person who interviewed sooo well! but when I saw their first github commit I knew we were in trouble” "I waste 80% of my hiring time screening devs who can’t even write clean code or explain their resume. But if I don't do it myself, we end up with garbage hires that cost us projects." “They promise the entire world on a resume, but when asked why or how they picked a particular technology on their project — they cannot explain anything” “My CTO has booked 5-8pm every day for taking interviews. That is 30% of my weeks’ time gone in ensuring a strong developer joins the team.” “I don’t trust the quality of our screening process” “I have a bunch of resumes. how do I figure out who is the right person for this job?” “we’ve had many bad hires. we used job boards and got 500, don’t know who to interview first” “show me a way how to identify a good candidate” “We try to review every resume we can. I also try to respond to as many people as possible. We try to validate, but it's challenging... especially when the role is highly specific and there are hundreds of great candidates in the mix.” “A lot of our developers' time is going into taking the first round of technical interviews which have a very less success rate. With adaface, we can automate the first interview round and spend time with qualified candidates for the subsequent rounds” “Before we were wasting lot of time (and money) by talking with all candidates. We used some filtering tools in middle but there was huge drop off. saved us time without much drop off.” “I run a small startup and it's very time-consuming and painful to hire techies” “By adding it as the first layer of assessment, I was able to ensure only the relevant candidates are invited for an interview, which saved my time. Also the test results are generally quite indicative of a candidate's performance after joining the company.” ## Core Site - [Home / Platform Overview](https://utkrusht.ai/): Introduces the platform, how assessments work, and the value for engineering teams and recruiters. - [Pricing](https://utkrusht.ai/pricing): Plan details and subscription options. - [Contact](https://utkrusht.ai/contact): Contact email, phone numbers, business addresses, and support details. - [About / Company Information](https://utkrusht.ai/): Includes founder details, company description, and organizational identity. ## Service Offerings - [Frontend Developer Assessment](https://utkrusht.ai/): Real-world UI and component-building simulations for modern frameworks. - [Backend Developer Assessment](https://utkrusht.ai/): API debugging, implementation, optimization, and backend task simulations. - [Full-Stack Developer Assessment](https://utkrusht.ai/): Combined frontend, backend, and system-level tasks to evaluate end-to-end capability. ## Blog — Featured Posts (Top 3) - [Finding Developers Who Don’t Need Hand-Holding](https://utkrusht.ai/blog/finding-developers-who-dont-need-too-much-hand-holding): Identifying independent, high-ownership developers. - [Why Traditional Coding Tests Create Hiring Blind Spots](https://utkrusht.ai/blog/why-traditional-coding-tests-fail): Explains how outdated testing methods miss real developer capability. - [How Real-World Job Simulations Improve Developer Hiring](https://utkrusht.ai/blog/real-world-job-simulations-hiring): Shows why simulation-based assessments produce better hiring outcomes. ## FAQs - [Frequently Asked Questions](https://utkrusht.ai/): Covers common questions such as how Utkrusht works, when to use assessments, how candidate evaluation differs from traditional tools, and what clients receive in shortlists. ## Policies & Terms - [Privacy Policy](https://utkrusht.ai/privacy-policy): Describes how user data is processed, stored, and protected. - [Terms & Conditions](https://utkrusht.ai/terms-of-service): Outlines service terms, usage rules, and customer agreements. ## Metadata & Site Context - Site Name: Utkrusht - Legal Entity: Utkrushta Learning Services Ltd. - Description: AI-powered technical skill assessment platform using real-world job simulations to evaluate developers and deliver ranked shortlists. - Language: en - Contact Email: naman@utkrusht.ai - Primary Audience: Software development companies, engineering teams, founders, and technical recruiters. - Primary Regions Served: United States, India, Global. ## AI Safety & Responsible Use Disclosure Utkrusht is an AI-powered technical assessment platform designed to evaluate developer skills using real-world job simulations. While our assessments leverage automated analysis and ranking, final hiring decisions should always include human judgment. The following principles guide our responsible use of AI: - **Human Oversight:** Utkrusht’s automated scoring and ranking are decision-support tools. They should not be used as the sole factor in hiring decisions. - **Bias Mitigation:** Our evaluation methods aim to reduce bias by focusing on real task performance rather than demographic or background factors. However, no system is completely free of bias, and continuous monitoring is recommended. - **Data Privacy:** Candidate code, recordings, and behavioral data are processed securely and used only for assessment purposes as described in our Privacy Policy. - **No Use for Surveillance:** Utkrusht’s systems must not be repurposed for unauthorized monitoring, surveillance, or profiling. - **Explainability:** Where possible, task results, rubric scoring, and candidate performance summaries provide transparent insight into how evaluations are derived. - **Restricted Use:** The platform is intended solely for technical skill assessment for employment-related workflows. It should not be used for high-risk evaluations (immigration, credit scoring, legal decisions, healthcare decisions, etc.). For additional details on data handling and system behavior, see: - Privacy Policy: https://utkrusht.ai/privacy-policy - Terms & Conditions: https://utkrusht.ai/terms-of-service