1) Beyond RAG: Processing Very Long Documents

Enterprise teams live in long-form content—contracts, playbooks, SOPs, policy manuals. Standard RAG often breaks under this weight because the “right” context isn’t fully or correctly retrieved.

• Core issues we set out to tackle:

  • Missing or partial context across large documents

  • Weak structure awareness (sections, clauses, annexes)

  • Latency and token constraints in long-context workflows

• Emerging directions builders explored:

  • Hierarchical retrieval and semantic chunking with overlaps to preserve section meaning

  • Context compression and pruning for long sequences to keep only evidentially relevant content

  • Multi-pass retrieval with diversity to avoid brittle single-shot fetches

  • Agentic orchestration to route between clause-level retrieval and section-level comparison

Why it matters: Long-document accuracy isn’t optional in legal and operations. Teams need grounded answers, traceability, and speed—at once.

References for further reading:

• Long-document RAG best practices: semantic chunking and hierarchical indexing, context compression and pruning, diversity-aware retrieval, and dynamic context optimization (see representative discussions and papers on topics like evidential context compression, attention-guided pruning, and dynamic context optimization in 2025 research conversations).

2) Format-Preserving Editing in Word With AI

AI can rewrite and improve content fast—but maintaining exact formatting in Word templates, clause labels, styles, numbering, tables, and cross-references is still a sticking point.

• Friction we targeted:

  • Losing template styles or list hierarchies after edits

  • Breaking references, TOCs, and complex layouts

  • No transparent “what changed” view for reviewers

• Patterns participants tested:

  • Strict style-bound edits: generating content that adheres to predefined Word styles and schema

  • Side-by-side diffs and tracked changes to preserve reviewer trust

  • Programmatic safeguards: protecting headers/footers, tables, and numbering while allowing content edits

Why it matters: In regulated and enterprise environments, format is part of compliance and brand integrity—not just aesthetics.

Reference examples:

• Real-world focus on preserving format and tracked changes in Word during AI-assisted editing, with tooling trends emphasizing “keep formatting” polish and robust change extraction.

3) True Redlining for Legal and Enterprise Negotiations

In legal and procurement, you never start from scratch. You work from a version, add changes, and need redlines that are precise and reviewable.

• Gaps we addressed:

  • Clean, clause-aware redlining—beyond raw text diff

  • Semantically meaningful comparisons (substance over punctuation/spacing)

  • Playbook-aligned suggestions that reflect organizational positions

• What builders explored:

  • Semantic diffing to highlight material changes while minimizing noise

  • Plain-language summaries of changes by clause

  • Playbook-driven proposals and risk flags that fit how legal teams actually negotiate

Why it matters: Redlining is where time is won or lost. Better diffs + better suggestions = faster deals, less risk, and happier teams.

Reference examples:

• Best-practice redlining includes AI-assisted clause analysis, collaborative track changes, integration with Word, and playbook alignment.