Inference Engine
Reasoning objects. Context assembly. Provenance tracking.
The orchestration layer that assembles context, routes to the right model, compresses intelligently, and tracks every decision the AI makes.
The layer between your documents and the AI models.
The Inference Orchestration Engine assembles context from multiple sources: your document, the matter history, your firm's training data, shared repositories, into structured reasoning objects before any model call is made. Context compression fits long documents within model windows without losing critical information. Model hot-swapping routes tasks to the optimal model automatically. Every inference is tracked with full provenance.
Reasoning Objects
Structured context, not raw prompts
Before any model call, the engine assembles a reasoning object, a structured package of context drawn from your document, the matter's history, your firm's training data, and shared repositories. Reasoning objects ensure the model receives precisely the right context in the right format. They're auditable and form the foundation of every AI operation in JR3.
Every AI decision starts with structured, auditable context.
Context Assembly
Gathers relevant context from documents, repositories, and training data automatically
Context Compression
Intelligently summarizes long documents to fit model windows without losing critical content
Model Hot-Swapping
Routes tasks to the optimal model based on capability, automatically, without user intervention
Provenance Tracking
Full audit trail for every inference: model used, context assembled, confidence assigned
Assemble
Gather and structure context
Orchestrate
Route to the right model
Audit
Track every decision with provenance
Full Auditability
Know exactly how every output was produced
Every AI-generated output in JR3 carries a provenance record. See which model produced it, what context was assembled, what confidence score was assigned, and the full reasoning chain. Partners can audit any AI-generated section: not just what it says, but why the engine arrived at that conclusion. Provenance records are immutable and exportable for compliance.
Complete transparency. Full reasoning chains. Immutable records.
Confidence scoring
Every AI output receives a confidence score. High-confidence outputs proceed automatically. Low-confidence outputs are flagged for human review. You set the thresholds, deciding how much autonomy the AI gets based on your firm's risk tolerance.
Intelligent compression
Long documents are compressed intelligently, not by truncation, but by semantic summarization that preserves critical legal content. The engine knows which clauses matter most for the current task and prioritizes them in the compressed context.
Multi-source context
Reasoning objects draw context from multiple sources simultaneously: the current document, related matter documents, your firm's shared repositories, training datasets, and agent context files. All assembled automatically based on the task.
Inference comparison (coming soon)
Any inference call will be comparable with different parameters: a different model, adjusted context, modified instructions. Compare outputs side-by-side to find the optimal configuration for each task type.
Common questions
What is a reasoning object?
A reasoning object is a structured package of context that the engine assembles before making any model call. It includes relevant content from your document, related matter documents, firm training data, shared repositories, and agent context files, all organized and formatted for the specific task at hand. Reasoning objects are fully auditable.
How does model hot-swapping work?
What does context compression preserve?
Can I see exactly what context was used for any output?
See the inference engine in action
Book a 15-minute demo. We'll show you reasoning objects, context assembly, and provenance tracking on a live document.