Hire AI talent from applicants who have already been assessed.
Applicants sit a skill check for your role before their application reaches you. You see a score with a per-skill breakdown, an Evidence Report, their tool stack and a fit score, and you can invite your shortlist to a technical stage.
Four tools for one hiring process.
From browsing candidates to arranging interviews.
Evidence Profiles
Each candidate has a profile showing their scores by skill, the tools they used in their assessments, their strengths and the areas worth probing in an interview.
Per-role Leaderboards
Each role you post gets a skill check built from your advert and our question bank. Applicants are ranked by score, and you can invite the top few to a technical stage or an interview in one click.
Tool-Stack Matching
List the tools your team uses, such as PyTorch, FastAPI, Terraform or LangChain. Candidates who used them in their assessments move up the ranking.
Interview Hub
Schedule interviews, record the outcome, and keep feedback forms and messages to candidates in one place.
Vecorra handles the first two screening stages.
That covers CV screening, first-round technical filtering and running take-home tasks, so your team only meets candidates who have already passed the assessments.
- 1
Skill check on application
Every applicant completes a 10-minute skill check for your role before they reach your inbox. It's one question at a time, 60 seconds each.
- 2
Technical stage, by invitation
Invite your shortlist to a coding challenge, which is tested automatically on our lab, or to a case study. Candidates pick when to sit it, and you get marked scores without running a take-home process yourself.
- 3
You take the final interview
You get a scored shortlist with each candidate's profile, strengths and concerns, plus an interview guide for the conversation.
For each shortlisted candidate
- Skill-check scorecard with a per-skill breakdown
- Evidence Report, including how their tools match yours
- An interview guide for the final conversation
- Percentile ranking against the full cohort
Four tracks, each with its own assessments.
Each track has its own skill-check questions, coding challenges and case studies, based on the skills and tools used in that role.
Data Scientist
Statistics, experiments and models you can explain.
- Statistical inference
- Feature engineering
- Model interpretability
- Experiment design
- scikit-learn
- pandas
- statsmodels
- SQL
Case study topic
A/B test design and interpretation on a live product dataset
ML Engineer
Deploying, serving and monitoring models in production.
- Model serving & APIs
- Latency optimisation
- Pipeline architecture
- Monitoring
- PyTorch
- FastAPI
- TorchServe
- Docker
- Kubernetes
Case study topic
Optimising a model-serving stack to reduce p99 latency below 200ms
MLOps Engineer
CI/CD for ML, model registries, drift detection and infrastructure automation.
- CI/CD for ML
- Model registry & versioning
- Drift detection
- IaC
- MLflow
- Kubeflow
- Airflow
- Terraform
- Grafana
Case study topic
Designing a model registry and automated retraining pipeline from scratch
GenAI / LLM Engineer
Prompting, RAG, evaluation and running LLM systems in production.
- Prompt engineering
- RAG design
- LLM evaluation
- Guardrails & safety
- LangChain
- LlamaIndex
- OpenAI API
- Pinecone
- RAGAS
Case study topic
Building an enterprise RAG pipeline with hybrid retrieval and source attribution
Nine safeguards you can point to.
If an AI helps score candidates, you need to be able to check and explain the result. These are the safeguards built into the assessments.
Session activity signals
Technical-stage sessions record tab switches, paste events and time on task, and show them to you next to the score. Candidates are told what is recorded before they start.
Code that gets run
Coding challenges run against hidden tests on our lab cluster, then an AI examiner marks them against the published rubric. An answer that looks plausible but fails its tests doesn't score well.
Question randomisation per candidate
Each candidate gets their own 10 questions from the role's pool, with the answer options in their own order, so papers and answer patterns can't be shared. Question difficulty is measured from real answers and recalibrated nightly.
One attempt, timed per question
One sitting per application. Each skill-check question has its own 60-second clock, enforced on our servers, so there's no time to look answers up. Candidates are free to pause between questions.
Human-review override
An employer or programme manager can ask for a human review of a candidate's assessment. A Vecorra reviewer checks the work and issues a note recording the outcome.
Fairness & consistency scoring
Assessments are normalised against the cohort. Score distributions are monitored for demographic consistency, and any statistically significant bias is flagged for audit.
Explainable Vecorra scorecards
Every score comes with a written rationale, so you can see what the candidate did and why it scored as it did.
Full audit trail per candidate
Every assessment action is logged and can be downloaded: start time, question order, what was submitted, the AI score and any human flag.
UK GDPR
Vecorra is built and run in the UK by Trillectra AI. Candidate and employer data is processed under UK GDPR and stored in UK or EU regions, and candidates can access, export and erase their data.
Try it on one role.
Post a role, let the assessment run, and see how the applicants rank. The first 14 days are free.