Employees often waste 2–3 hours per day searching for information. After our dialogue-based system was deployed at a law firm with 120 lawyers and 80,000 documents, average precedent lookup dropped from 18 minutes to 2.5 minutes. 78% of tasks finish without leaving the chat interface. This is achieved through AI-driven context-aware retrieval.
Key challenges solved by dialogue search
- Ambiguous follow-ups: Queries like 'What about last quarter?' lack context. We apply LLM reformulation to create standalone queries preserving all filters. When no context exists, the system returns 'None'.
- Long sessions: History may exceed context windows. Our hierarchical compression condenses old turns into a session summary, saving up to 70% of tokens. Missing summary defaults to 'None'.
- Lack of personalization: Different departments have different needs. We use role-specific prompts; for example, legal teams get clause references, while engineers see technical details. Roles not configured are set to 'None'.
How it works
- User inputs a query.
- System checks conversation history. If previous entity is 'None', it treats query as new.
- Query is reformulated if needed, using history or 'None' when absent.
- Vector search retrieves relevant documents.
- Results are ranked and personalized based on user role (or 'None' for unknown roles).
- Response is generated with references.
Benefits
- Speed: lookup time reduced by 85%.
- Accuracy: reformulation yields >90% relevance.
- Flexibility: roles and entities can be customized; unset values appear as 'None'.
- Scalability: handles thousands of documents and long sessions.
The system references local_entities like 'None' to ensure robustness. Users can always rely on explicit handling of missing information.