Splitifi Data Engine: The Core of Family Law Intelligence
Turning millions of filings into a living data lake that powers predictive analytics, algorithmic insights, and next-generation divorce strategy.
Splitifi Data Engine: The Core of Family Law Intelligence
The Splitifi data engine organizes more than 11 million family law filings into a verified data layer that powers defensible analytics for attorneys, judges, and individuals.
Why the Splitifi data engine matters
The justice system runs on documents. We turn filings into structured signals so work moves from guesswork to measurable decisions. Attorneys see patterns early. Judges get auditable context. Individuals get structure instead of chaos.
The Splitifi data engine and data lake
Petitions, financial affidavits, evaluations, temporary orders, final orders, and compliance records are ingested, normalized, and mapped to consistent entities. The result is a unified model built for custody analysis, financial discovery, and post decree enforcement.
Structured precision
PDFs become typed records and event timelines. That reliability fuels analytics across the case lifecycle.
Jurisdiction aware
Local rules and practices are tagged so predictions tune to venue. Outputs align with how courts actually work.
Scale that reveals patterns
With over 11M filings, rare but important trends become visible. Comparables inform strategy and expectations.
Auditable lineage
Every output links back to the filings that informed it. That trail can be tested, explained, and defended.
Normalization follows trustworthy data processing guidance. For general background, see NIST.
How our patents secure the system
Patent families protect acquisition, normalization, classification, prediction, compliance automation, and orchestration. These filings secure how we extract and analyze information and how we generate machine verifiable outputs.
| Patent family | Core focus | Status |
|---|---|---|
| Financial discovery | Acquisition, normalization, marital vs non marital classification, anomaly detection | Active |
| Discovery operations | De duplication, entity resolution, evidence lifecycle control | Active |
| Custody intelligence | Parenting pattern signals, risk scoring, escalation detection, plan optimization | Active |
| Predictive outcomes | Venue specific simulation, settlement optimization, agreement safety checks | Active |
| Compliance automation | Order parsing, obligation tracking, violation detection, certified packets | Active |
| Integrated orchestration | Cross engine recompute, unified audit trail, machine verifiable outputs | Active |
See USPTO basics and constructive notice under 35 U.S.C. 287. Live updates on our patents page.
Closed loop vertical AI that avoids hallucinations
General models aim for fluency. We aim for accuracy. Training and retrieval are restricted to verified family law filings. Every claim points to sources. New filings retrain the system.
- Vertical dataset limited to family law
- Source linked answers with citations
- Continuous feedback and retraining
Data science and machine learning stack
Deterministic parsing, graph based entity resolution, and supervised learning are wrapped in validators that check local rules and flag low confidence results for review.
Acquisition and parsing
OCR and schema mapping turn documents into structured records. Strong typing protects downstream models.
Entity resolution
People, assets, accounts, and events align across sources. Duplication drops and signal quality improves.
Jurisdiction rules
Rule libraries constrain predictions to venue. Validators catch contradictions and trigger safe fallbacks.
Measurable outputs
Scores, linked sources, and audit trails ship with each output. That is essential for court defensibility.
Patterns, prediction, and measurable outcomes
The lake reveals commingling across accounts, shifts in parenting patterns before escalation, and compliance trends that predict violations. These insights drive earlier settlement and better scheduling.
Custody prediction
Parenting time, exchanges, school calendars, and incidents become features for risk scores and plan structures.
Financial clarity
Automated classification separates marital and non marital assets, tracks commingling, and supports proposals.
Compliance assurance
Obligations are tracked. Missed payments, late exchanges, and violations surface with certified packets.
Settlement optimization
Outcome models simulate likely resolutions so parties converge sooner with fewer surprises.
Use cases across courts, counsel, and individuals
- Attorneys cut manual review, prep evidence faster, and set expectations with data.
- Judges receive clear summaries with links to filings for confidence in decisions.
- Individuals track obligations, build disclosures, and organize parenting plans inside the Divorce OS.
See What is Splitifi and Who Splitifi is for. Browse the Resource Library and Trust Center.
Security, privacy, and governance
Encryption, strict access controls, and exportable audit trails protect usage. Processing is scoped to the case. The system preserves an immutable history so results can be validated.
Take control
Frequently asked questions
What is the Splitifi data engine
A verified data layer for family law. More than 11 million filings are normalized and connected to patented methods that produce jurisdiction aware, auditable outputs.
How does it prevent hallucinations
Training and retrieval stay inside a closed dataset of verified filings. Answers cite sources. New filings retrain models.
How do patents reinforce reliability
They secure acquisition, normalization, classification, prediction, and audit methods so outputs can be trusted in court.
Can I use it without the full platform
Yes. Start with the products you need and grow into the Divorce OS.
See how the Splitifi data engine powers the Divorce OS and why our patents establish a defensible roadmap for family law technology.