Home / Case Studies / LegalSearch

[ LEGAL TECH ]

LegalSearch
Knowledge Management

Q: How long does it take to index our documents?

LegalSearch shipped in 10 weeks, design through deployment, including ingesting 15 years of accumulated documents. Initial indexing runs in the background; the firm doesn't wait on it to start using the platform.

Q: What does a platform like this cost?

It depends on document volume and integration needs, so there's no fixed number here — every engagement is scoped and quoted before any work starts.

Q: Is our data ever sent to a public cloud AI?

No by default — LegalSearch is self-hosted, on-premise or private cloud, with a self-hosted embeddings option so documents never have to leave the firm's infrastructure. That was a hard requirement for this engagement, not an afterthought.

Q: Who owns the code?

You do — full source, no licensing fees, no vendor lock-in.

Q: Can it integrate with our existing DMS (iManage, NetDocuments)?

Yes. The ingestion pipeline can pull from an existing document management system rather than replacing it outright, so firms can adopt semantic search without a forced migration.

AI-powered semantic search platform that helps law firms find relevant documents across 1M+ files in under 100 milliseconds.

TIMELINE 10 weeks

INDUSTRY Legal Tech

STACK Laravel + Vue

LegalSearch semantic search results for a demo query, showing ranked document matches with match percentage and metadata

<100ms

Search response time

1M+

Documents indexed

$220k

Annual savings

85%

Time saved on research

[ THE CHALLENGE ]

Legal research was
drowning in data

A mid-size law firm with 15 years of accumulated documents was losing billable hours to manual searches.

Hours Lost to Manual Search

Associates spent 2–3 hours per research task sifting through folder structures and outdated naming conventions to find relevant precedents.

Scattered Knowledge Sources

Documents lived across network drives, email attachments, and legacy systems. No unified search meant duplicate work and missed insights.

Keyword Search Limitations

Traditional search couldn't understand legal concepts. Searching "breach of contract" wouldn't find documents discussing "material default" or "failure to perform."

Security & Compliance Concerns

Client confidentiality required on-premise deployment with role-based access. Public cloud AI solutions were not an option.

[ THE SOLUTION ]

Semantic search that
understands legal context

We built LegalSearch as a self-hosted knowledge management platform that uses vector embeddings to understand the meaning behind legal queries, not just keywords. It's one of several platforms delivered through our Elasticsearch and search infrastructure work.

Vector-Based Semantic Search

Documents are converted to high-dimensional vectors that capture legal concepts and relationships, enabling conceptual search across the entire corpus.

>OpenAI embeddings (self-hosted option available)
>Milvus vector database for sub-100ms queries
>Hybrid search combining semantic + keyword
>Automatic synonym and concept expansion

LegalSearch document ingestion pipeline showing demo OCR extraction and entity detection on a scanned contract — Automated ingestion extracts text, metadata, and legal structure from any format (demo data).

Intelligent Document Processing

Automated ingestion pipeline that extracts text, metadata, and structure from any document format while preserving legal context.

>OCR for scanned documents
>PDF, DOCX, email, and image support
>Automatic clause detection
>Entity extraction (parties, dates, amounts)

LegalSearch enterprise security screen showing demo matter-based access controls and audit log — Matter-based permissions and full audit logging keep client work confidential (demo data).

Enterprise Security & Access Control

Self-hosted deployment with granular permissions ensuring client matters stay confidential and audit-ready.

>On-premise or private cloud deployment
>Matter-based access controls
>Full audit logging
>SSO with Active Directory/SAML

[ ARCHITECTURE ]

How it works

Documents

PDF, DOCX, Email

→

Processing

OCR + Extraction

→

Embeddings

Vector Generation

→

Milvus

Vector Database

→

Search API

<100ms Response

LaravelVue.jsMilvusPostgreSQLRedisOpenAI APIDocker

[ SEE IT WORK ]

A search, start to finish

One flow, one recording: type a natural-language query, watch semantic results rank in under 100ms, open the matching clause.

Coming soon

Search walkthrough

Natural-language query → ranked semantic results → matching clause, in under 100ms. Full video in progress.

[ RESULTS ]

Measurable impact on
legal research efficiency

$224K+ Per Year

Recovered Billable Time

Associates now complete research tasks in 20–30 minutes instead of 2–3 hours. At average billing rates, this translates to over $224,000 in recovered capacity annually.

100ms Avg Response

Instant Search Results

Vector similarity search across 1M+ documents returns relevant results in under 100 milliseconds, even with complex natural language queries.

85% Reduction

Research Time Saved

What previously took hours of manual folder browsing now takes minutes. Semantic understanding surfaces relevant documents that keyword search would miss.

15 yrs Indexed

Complete Knowledge Base

The firm's entire document history — 15 years of contracts, briefs, memos, and correspondence — is now searchable through a single interface.

"Our legal team was spending hours searching through documents. Now queries return results in 100ms even with over 1 million documents. That's $224K+ recovered per year in billable time that was previously lost to manual research."

Managing Partner

Mid-Size Law Firm

[ FAQ ]

LegalSearch — frequently asked

How long does it take to index our documents?

LegalSearch shipped in 10 weeks, design through deployment, including ingesting 15 years of accumulated documents. Initial indexing runs in the background; the firm doesn't wait on it to start using the platform.

What does a platform like this cost?

It depends on document volume and integration needs, so there's no fixed number here — every engagement is scoped and quoted before any work starts.

Is our data ever sent to a public cloud AI?

No by default — LegalSearch is self-hosted, on-premise or private cloud, with a self-hosted embeddings option so documents never have to leave the firm's infrastructure. That was a hard requirement for this engagement, not an afterthought.

Who owns the code?

You do — full source, no licensing fees, no vendor lock-in.

Can it integrate with our existing DMS (iManage, NetDocuments)?

Yes. The ingestion pipeline can pull from an existing document management system rather than replacing it outright, so firms can adopt semantic search without a forced migration.

Still have questions?

Talk with a friendly expert

LegalSearch
Knowledge Management