Changelog

Deep Search Reliability

Hardening for the Recursive Language Model (RLM) behind Deep Search, so long multi-document investigations finish cleanly and always return an answer.

• Deep Search always completes — the iterative evidence-gathering loop no longer exhausts its rounds without producing a report; gathered evidence always reaches the final synthesis
• Follows your chosen model — Deep Search synthesis uses the model you select instead of a hardcoded default
• More reliable serving — corrected model context window and a pinned inference engine for reproducible RLM and reranking

Structured Legal Intelligence — XETO & Haystack

Sound Suite now understands the structure of a case, not just its text. Cases, motions, filings, parties, and exhibits are modeled as typed, linked entities — so search and AI can reason over how a case actually fits together.

• XETO + Project Haystack data model — cases, motions, filings, persons (judge, movant, respondent, clerk, reporter), exhibits, and hearings are first-class typed entities with tag-based references
• Per-motion model assignment — assign the judge, parties, filing type, and amendment/supersession links to each motion through a structured tag panel; a single case can have a different judge on each motion, and references resolve to live entity records
• Graph-aware retrieval — traverse those relationships (amendment lineage, motions by judge or party) to answer “what connects X and Y” questions that semantic search alone misses
• Smarter search — adaptive query routing, hybrid keyword + vector retrieval with reciprocal-rank fusion and cross-encoder reranking, plus faster, more resilient reranking
• Recursive Language Model (RLM) — bounded, iterative evidence-gathering for deep multi-document questions
• One-command Docker install — official container image plus docker compose; a fresh database is created on first run, no data baked in
• Faster foundation — Prisma 7 (native SQLite driver), Next.js 16.2 (Turbopack production builds), a much smaller Docker image, and automated versioned releases

Initial Release

• 14 MCP analysis tools for comprehensive legal document intelligence
• Draft editor with AI chat, auto-suggest, and track changes
• Automated PDF processing pipeline with OCR
• GPU Sidecar for local AI models via Docker
• Semantic vector search with LanceDB
• Multiple embedding providers (local, OpenAI, Anthropic, Ollama)
• Exhibit extraction and image gallery
• Admin panel with 15 configuration sections
• Word import/export, PDF export

Safe Upgrades & Docker MCP Catalog

The container image, docker compose install, and the automated semver release pipeline shipped in v1.0.1. Next: zero-data-loss upgrades and a slim MCP-only image for Docker Desktop’s MCP Toolkit.

• Safe upgrade mechanics — automatic pre-migration backups, version handshake to prevent accidental downgrades, enforced prisma migrate deploy only
• MCP slim image — lightweight MCP-only container for Docker Desktop's MCP Toolkit, no manual config files needed
• Unified data directory — single volume for SQLite, LanceDB, exhibits, and backups with versioned layout for future-proof migrations

RunPod Cloud GPU Overflow

When your local GPU is fully loaded, automatically route excess work to RunPod cloud GPUs. Pay per-second, zero idle cost.

• Auto mode switching — orchestrator detects workload and switches between indexing and searching modes automatically
• RunPod integration — serverless endpoints for embedding/reranking, on-demand pods for completion/OCR with auto-shutdown on idle
• Cost controls — configurable hourly, daily, and monthly spend limits with automatic enforcement and 80% threshold alerts
• Admin settings page — configure API key, GPU preferences, per-role overflow toggles, and monitor active cloud instances with live cost tracking