Technical hiring, with the work behind every decision.
Design practical assessments, evaluate real submissions and review integrity context in one coherent workflow—without turning a score into a black box.
- Instant self-serve setup
- Organisation-branded candidate flow
- Human-reviewable evidence
Implement LRU Cache with O(1) Eviction
# Implement capacity boundary & fast lookup
class LRUCache:
def __init__(self, capacity: int):
self.cache = OrderedDict()
self.capacity = capacity
Built as one system
From assessment design to the final review.
Concrete product capabilities—not vanity business metrics.
- Sandboxed languages
- 0Sandboxed languages
- Question formats
- 0Question formats
- Integrity signals
- 0Integrity signals
- Granular permissions
- 0Granular permissions
For teams that need defensible signal
A clearer operating model for technical hiring
Give each stakeholder the context they need while keeping one source of truth for the round.
Recruiting teams
Standardise technical screens and hand engineering reviewers a clear, consistent decision record.
Campus hiring
Build reusable question pools, schedule assessment windows and monitor high-volume drives from one workspace.
Engineering leaders
Evaluate practical skills with test cases and rubrics while preserving the evidence behind each recommendation.
Connected capabilities
One workflow instead of a stack of disconnected tools
Build, deliver, monitor and review without losing the relationship between a result and how it was produced.
Real code execution
Give candidates a full editor and run submissions against your test cases in isolated sandboxes with question-level limits.
Flexible assessment design
Combine coding, SQL, objective and reviewer-scored formats, then reuse tagged questions across roles and hiring rounds.
Reviewable integrity signals
Capture configured browser, webcam and screen signals while keeping every risk score connected to its underlying events.
Cohort comparison
Compare code submissions by token and structural similarity, with the evidence available for a reviewer to inspect.
Decision-ready results
Move from section and question breakdowns to cohort views and exports without separating scores from assessment context.
Scoped team access
Use role permissions, member overrides and assessment-level scope so each reviewer sees only the work they should handle.
How it works
A straight path from role requirements to review
The platform supports the process; your team keeps ownership of the hiring decision.
- 01Step
Design the round
Define sections, scoring, duration and question pools around the skills the role actually needs.
- 02Step
Invite candidates
Import a cohort or add candidates directly, then deliver unique links and configured requirements.
- 03Step
Run with context
Track progress and configured integrity signals while candidates work in an organisation-branded experience.
- 04Step
Review the evidence
Bring scores, submissions, reviewer notes and integrity events together before making the decision.
Predictable & Transparent
What would it cost you?
Pick your candidate volume and capabilities to see exact figures computed directly by our billing service.
Configure your exact volume
Direct billing engine simulation. No seat tax, no hidden overages.
Only candidates who start an assessment count. Unopened invitations are always free.
Duration for capacity sizing
Determines runtime sandbox
Practical evaluation
Use real execution where the role calls for it
Candidates work in a full editor while your test cases and execution limits define what success means. Objective, SQL and reviewer-scored formats can sit alongside coding in the same round.
- Sandboxed execution across the supported language catalogue
- Visible and hidden test cases with question-level limits
- Autosaved work for more resilient assessment delivery
- Reusable question tags, difficulty and randomised pools
Execution catalogue
Choose the right environment per question
- C
- C++
- Python
- Java
- JavaScript
- TypeScript
- Go
- Rust
- Kotlin
- Swift
- PHP
- Ruby
- SQL
Responsible review
Keep automated signal explainable
Integrity and similarity signals are review inputs—not automatic accusations. The underlying events remain available so a reviewer can understand what contributed to a flag.
- Event-linked integrity scoring rather than an unexplained verdict
- Organisation-controlled capture and assessment settings
- Manual review for subjective responses and consequential decisions
- Role and assessment scope around sensitive candidate information
Evidence attached
Review the events behind a signal.
Access scoped
Limit candidate data to the right team.
Supported assessment formats
- Single choice
- Multiple choice
- Fill in the blank
- Paragraph / comprehension
- Image based
- Code snippet MCQ
- Subjective / long answer
- Case study
- Coding problem
Direct answers
Know how the product behaves before you adopt it
Clear boundaries are part of a professional platform, especially when candidate decisions are involved.
Do candidates need to install anything?
How is coding graded?
Does the platform automatically grade long-form answers?
What does an integrity score represent?
Can the candidate experience use our brand?
How does pricing work?
Build a better technical round today
Create your workspace in seconds to design assessments, invite candidates, and evaluate real submissions with explainable evidence.
Have questions? Email sales@parikshafy.com