DCD Advisory built a public, source-linked trial archive that combines automated publishing, structured transcripts, evidence organization, analytical scoring, proposition testing, and experimental case intelligence. The larger point is not this one trial: the underlying architecture is reusable anywhere enough reliable source data exists.
A court case is not naturally a database. The information arrives as testimony, transcripts, exhibits, dates, documents, expert opinions, and competing interpretations. The build had to preserve those differences while making the record usable.
A live trial produces a rapidly expanding record: daily testimony, transcripts, expert opinions, factual disputes, timelines, medications, exhibits, media coverage, and competing interpretations. The challenge was to make that record searchable, reviewable, updateable, and analytically useful without collapsing it into a single narrative.
DCD built a Hugo-based public archive with automated daily publishing, structured transcript ingestion, evidence ledgers, source links, medication and timeline data, proposition analysis, Apps Script processing, AI-assisted extraction and classification, and an experimental Case Intelligence layer that can score competing interpretations while retaining contradictory evidence.
The result is a living research platform rather than a collection of articles. New source material can flow into structured datasets, trigger downstream processing, update public pages, feed analytical models, and support new tools without rebuilding the foundation each time.
Each stage creates structure that the next stage can use. That is what allows publishing, automation, analysis, calculation, and future modeling to share one foundation.
Bring in transcripts, public records, exhibits, structured files, manually reviewed source material, and other available case data.
Convert inconsistent source material into structured records with dates, people, claims, sources, event types, and other reusable fields.
Connect claims back to transcripts and underlying source material so analysis remains auditable rather than detached from the record.
Use Apps Script and repeatable processing workflows to discover new material, publish updates, generate records, and maintain datasets.
Test evidence against predefined propositions, retain competing interpretations, calculate weighted scores, and surface high-impact records.
Turn the structured data into recaps, transcript pages, timelines, dashboards, searchable archives, and public-facing research tools.
Build scenario models, trend measures, forecasts, risk indicators, or predictive analyses when the target question and available data justify them.
Add new factors, calculations, APIs, dashboards, reports, or AI-assisted tools on top of the same data foundation.
The public Case Meter is an experimental evidence-analysis layer. It uses predefined propositions and analytical factors to organize competing interpretations while retaining contradictory evidence and links back to the record.
The current system converts trial material into structured evidence records carrying source, timing, relevance, strength, and other analysis fields.
Evidence can support, undermine, contextualize, or be irrelevant to a proposition. The system does not require every record to fit one side's narrative.
Relevance, strength, timing, corroboration, and clustering rules help reduce simplistic counting and repeated descriptions of the same underlying fact.
The domain changes the questions and the rules. The technical pattern stays recognizable: collect reliable records, normalize them, preserve provenance, calculate what can be measured, and build purpose-specific workflows on top.
Transcripts, testimony, exhibits, expert opinions, timelines, competing theories, evidentiary factors, and source-linked proposition analysis.
Pleadings, discovery, communications, damages records, timelines, deposition material, issue tracking, and scenario analysis.
Financial records, asset inventories, communications, discovery, motions, orders, settlement proposals, cash-flow modeling, and structured comparison of disputed positions.
Calendars, exchanges, communications, school and activity records, orders, schedule history, documented events, and parenting-plan scenario comparisons.
Policies, correspondence, investigative records, chronology, claims, supporting evidence, hearing material, and repeatable issue analysis.
If a matter has enough reliable source material, the same architecture can ingest it, structure it, connect it, calculate from it, and turn it into usable decision support.
The value is not limited to summarizing documents. Once the source material has structure, the same platform can automate workflows, integrate systems, calculate metrics, compare positions, and support more advanced analytical tools.
Ingestion, document processing, publishing, report creation, alerts, recurring reviews, and data maintenance.
APIs, spreadsheets, databases, cloud storage, public records, internal systems, and external services can feed or receive structured data.
Count, weight, cluster, compare, score, trend, and track facts or behaviors using transparent rules.
Timelines, financial scenarios, schedules, frequency, duration, compliance measures, deltas, and other case-specific calculations.
Organize competing positions, find evidentiary gaps, identify high-impact records, compare scenarios, and support preparation with source-linked outputs.
Build forecasts, scenario models, risk indicators, and predictive analyses where the question is measurable and the available data supports a defensible methodology.
Legal records can support sophisticated analytics, but the model should match the question. Some tasks are deterministic calculations. Others are scenario models. Some may support forecasting or predictive analysis. The methodology, source quality, uncertainty, and limits should remain visible rather than hidden behind a score.
The Lindsay Clancy project demonstrates how a messy legal record can become a structured operating system for research. The same pattern can be adapted to other court cases and private legal matters: ingest the record, preserve provenance, structure the facts, automate repeatable work, calculate what can be measured, model scenarios, and generate outputs that remain tied to the underlying source material.
DCD Advisory combines creative and technical capabilities around the actual problem - from identity systems and digital experiences to automation, dashboards, AI tools, and custom operational workflows.
The deliverable changes, but the method stays consistent: understand the problem, design the system, build it properly, and connect the pieces that should work together.