Legal Archive AI Assistant for a US Law Firm Operating Worldwide

About Our Client

The Client is a US-based full-service law firm serving companies and individuals across a broad range of sectors. With offices in multiple countries and a team of over 100 attorneys, the firm has been providing legal services for several generations.

Simplifying Legal Archive Search With AI

The Client works with a massive volume of legal information, including millions of documents stored across active cases and historical archives. Clerks and lawyers needed to regularly search this data to find similar cases, locate specific documents within a case, and identify materials that matched certain criteria.

The existing search system made this work difficult. It relied on rigid forms and exact search parameters, so users had to know the correct fields, values, and wording in advance. The system lacked semantic search capabilities, which made it hard to find relevant materials when the same legal issue was described in different terms.

ScienceSoft identified an opportunity to improve the Client’s document retrieval process with AI and proposed a proof of concept for an AI-powered assistant that would combine semantic search with a chat-based interface, enabling the law firm’s staff to use natural-language queries to find cases and documents.

AI-Powered Legal Document Search Assistant

ScienceSoft delivered a proof of concept for a conversational AI assistant able to run semantic search across legal cases and documents.

The solution allows legal staff to search the archive in natural language, rather than relying on technical filters or exact keywords. A user can start with a broad query, such as finding a case similar to the one they are currently handling, receive a shortlist of relevant cases with key attributes presented in a card-based format, and then continue the conversation to locate a specific document within the selected case.

An example user-side workflow looks as follows:

• The user opens the assistant and types a natural-language query, for example: Find cases related to a dispute over unpaid service fees.
• The system searches for semantic matches across the case archive.
• It returns several matching cases, shown as cards with key attributes.
• The user reviews the shortlist and picks a relevant case.
• Then they narrow the search and ask for a specific document within that case.
• The system returns matching documents from that case.

At the core of the solution is semantic search, which retrieves documents by meaning rather than exact wording. This means that if a user searches for a dispute related to late payment, the system can also find materials referring to non-payment, payment default, or overdue invoices. The solution also preserves conversation context: once a user selects a case, they can continue refining the search within that case instead of starting over.

To enable this functionality, ScienceSoft implemented automated document ingestion and an embeddings-based retrieval pipeline. Legal documents were parsed and structured with Docling, then indexed in Weaviate together with vector representations generated by an embedding model. A self-hosted Llama-based instruct model, served via vLLM, powered the conversational interface by interpreting user queries and generating responses grounded in retrieved results.

To preserve data privacy, ScienceSoft deployed the system in an isolated on-premises environment.

AI Value Proven, Broader Adoption Planned

The project gave the Client a clear business case for applying AI to legal operations. The proof of concept demonstrated that natural-language search could simplify work with the legal archive, make search more intuitive for clerks and lawyers, reduce dependence on exact keywords and complex search forms, and improve access to relevant cases and documents.

The solution also gave the Client’s CTO tangible evidence to present to management when evaluating AI for routine legal operations. It helped the Client assess the business value of the approach, estimate potential savings and efficiency gains, and begin planning implementation budgets.

As of April 2026, the Client is discussing further cooperation with ScienceSoft on an enterprise AI platform that would extend the legal archive assistant and introduce additional assistants for other business functions, including HR and finance. In total, the Client plans to implement six AI assistants by the end of the year.

Technologies and Tools

Weaviate, Docling, Llama, text embeddings, vLLM, Docker, Python.