How It Works
QLM selects the best items for each person, faster and more precisely than traditional approaches.
The Problem
Traditional testing selects one question at a time. This leads to redundant questions and longer assessments.
SEQUENTIAL SELECTION: One item at a time
Each step is independent. Redundant questions are asked. The test is long and tiring.
The cost of sequential selection
- Items may overlap in what they measure (redundancy)
- Relationships between dimensions are not leveraged
- Tests take 45+ items to reach confidence
- Respondent fatigue degrades data quality
The Solution: Personalized Precision Selection
QLM considers all candidate items simultaneously instead of choosing them one at a time. The engine finds the best set of items to ask next.
PRECISION SELECTION: All items considered at once
The engine selects the most complementary items, avoiding redundancy and reaching confidence faster.
The result
QLM reaches the same or better measurement confidence with 60–73% fewer items, validated across multiple independent datasets. Response times are ~42ms for typical item banks.
Fewer Items, Better Results
QLM selects items that are complementary across all dimensions being measured. This means each item contributes unique value, reducing the total number of items needed.
Key benefit
Because the engine selects items that complement each other across all dimensions, assessments finish in 60–73% fewer items — without sacrificing measurement precision.
Smart Stopping
The engine automatically determines when enough items have been administered to reach the desired confidence level.
PERSONALIZED ASSESSMENT FLOW
Assessing
The engine selects items to build confidence across all dimensions. Items are chosen for maximum value.
Complete
Confidence exceeds threshold (default 0.95). Session complete. No more items needed.
10 Domains, One Engine
The engine is domain-agnostic. Each domain provides a specialized item bank and dimension structure, but the core engine is identical.
Why one engine works everywhere
The structure is the same: items have difficulty levels, respondents have skill levels, and the goal is always to select the best items with the fewest questions. The engine is agnostic to what the dimensions represent — it only cares about finding the best items to ask next.