Transform communications and digital artifacts (email, chat, SMS, Slack, Teams, voice, meeting transcripts, attachments, and embedded documents) into deterministic metrics with evidence pointers and run-stamped outputs for litigation, compliance, and investigations.
Quantitative behavioral analysis for regulated industries and high-stakes matters
Institutional-grade rigor for high-stakes matters
measure first, interpret second
what changed, relative to what
when did the shift start, how sharp was the break
email, chat, SMS, voice, transcripts, attachments
The goal is not to replace professional judgment. The goal is to make behavioral patterns measurable and defensible.
Computational frameworks for detecting and quantifying behavioral regime shifts
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}
Air-gapped processing. Hash-chained run manifests and reproducible audit trails. Institutional-grade analytics for your highest-stakes matters.
Schedule Discovery ConsultationFrom raw data to evidence-linked, reproducible outputs