> ## Documentation Index
> Fetch the complete documentation index at: https://www.scrums.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# AI-Assisted Vetting & Matching

> How Scrums.com uses AI to assess, match, and place engineering talent for your delivery needs.

## Overview

Scrums.com uses AI at multiple stages of the talent lifecycle, from initial screening through to ongoing performance tracking.

## Stage 1: Candidate Assessment

When engineers apply to join the Scrums.com network, they go through a structured assessment process that includes:

* **Technical skills tests** — Role-specific coding challenges and architecture exercises
* **Communication evaluation** — Written and verbal English assessment
* **Behavioural assessment** — Structured interview scoring against Scrums.com's delivery culture criteria
* **Reference validation** — Background and professional reference checks

AI tools are used to score technical assessments, identify skill gaps, and calibrate candidates to the correct seniority band (Junior, Mid, Senior, Lead).

## Stage 2: Client Matching

When a client request comes in, the matching engine evaluates the active talent pool against your specific requirements. It weights:

* **Stack match** — How closely the candidate's technical profile aligns with your requirements
* **Availability** — Current utilisation and notice period
* **Delivery model experience** — History with Scrum, Kanban, or the client's preferred process
* **Client history** — Prior successful placements in similar industries or tech environments

The output is a ranked shortlist, reviewed and curated by your Enablement Partner before being shared.

## Stage 3: Ongoing Performance Monitoring

Once deployed, SEOP tracks performance data across all engineers:

* Story points completed vs. committed
* PR cycle time and review participation
* Defect rates and code quality scores
* Sprint ceremony attendance and engagement

Anomalies are flagged automatically for review. This data informs proactive intervention — coaching, workload adjustment, or replacement — before issues affect delivery.