CloseVector / E-discovery / QLCA methodology
Deterministic communications analysis within e-discovery

QLCA: Quantitative Legal Communications Analytics

Within CloseVector's e-discovery workflow, QLCA turns communication timing, baselines, channel shifts, and response patterns into deterministic, reproducible, evidence-linked signals. It supports review and investigation without replacing professional judgment.

Deterministic Metrics • Structural Break Detection • Hash-Chained Provenance
16 Analytical Studies
9 Detection Frameworks
4 Feature Classes
3 Regime States

QLCA: Quantitative Legal Communications Analytics

Within CloseVector's e-discovery workflow, QLCA turns communication timing, baselines, channel shifts, and response patterns into deterministic, reproducible, evidence-linked signals. It supports review and investigation without replacing professional judgment.

Communication timingMeasure intervals, response latency, silence, and event-linked changes.
BaselinesCompare observed patterns with the matter's own prior communication behavior.
Channel shiftsTrace movement among email, chat, SMS, voice, meetings, and attachments.
Response patternsConnect reproducible metrics to source records, timestamps, custodians, and run IDs.

Illustrative examples: Numeric scenarios and diagrams on this methodology page explain analytical outputs. They are not production performance claims.

Legal and E-discovery Applications

Deterministic communications analysis for review, investigation, and litigation

The Analytical Paradigm

Evidence-linked signals for review, investigation, and litigation

Signal-first

measure first, interpret second

Baseline comparisons

what changed, relative to what

Time-first

when did the shift start, how sharp was the break

Multi-channel telemetry

email, chat, SMS, voice, transcripts, attachments

The goal is not to replace professional judgment. The goal is to make behavioral patterns measurable and defensible.

Behavioral Signal Architecture

Computational frameworks for detecting and quantifying behavioral regime shifts

Illustrative example: Behavioral Range Analysis with Event Detection
Improving Trend
Declining Trend
5-Period SMA
Event Marker

Core Detection Frameworks

Analytical Studies

16 buildable modules with hypotheses and outputs

Each study is a standardized analysis module that consumes canonical records and emits deterministic metrics, plots, evidence pointers, and run-stamped findings. Evidence pointers map each metric to record IDs (message IDs, recording IDs, transcript spans), timestamps, custodians, and extraction provenance, or to time-bounded void intervals when absence of communication is the signal. Narrative is optional and must anchor to evidence.

Deterministic means: given the same source records, configuration, and pinned tool/model versions, the pipeline reproduces identical metrics and evidence pointers. Narrative (if enabled) is generated separately (non-deterministic), labeled non-evidentiary commentary, and source-checked back to underlying records (citations to record IDs). Default is OFF.

Evidence pointer example: reply_latency_outlier → {record_ids[], custodians[], timestamps[], extraction_hash, run_id}

Examine Communication Patterns Within E-discovery

Review the deterministic methodology, evidence pointers, and reproducible run structure with your matter workflow in view.

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Implementation Architecture

From raw data to evidence-linked, reproducible outputs

Processing Pipeline