Precision at Scale: How Aracor Accelerated Legal Due Diligence with Hybrid Vector Search

Precision at Scale: How Aracor Uses Qdrant to Accelerate Legal Due Diligence Resulting in 90% Faster Workflows

How Aracor Accelerated Legal Due Diligence with Qdrant Vector Search

The world of mergers and acquisitions (M&A) is notoriously painstaking, slow, expensive and error-prone. Lawyers spend weeks combing through thousands of documents—validating signatures, comparing versions, and flagging risks.

Lawyers and dealmakers sift through mountains of documents—often numbering into the thousands—to validate every detail, from validating signatures, comparing the documents involved in the deal transaction, flagging risks to to patent validity. This meticulous process typically drains weeks or even months of productivity from highly trained professionals. Aracor AI set out to change that and to solve the M&A transparency gap. The Miami-based AI platform is laser-focused on transforming this painstaking due diligence into an automated, accurate, and dramatically faster operation.

The Challenge: Mountains of Documents, Mountains of Pain

Before Aracor, M&A lawyers faced countless hours verifying signatures, comparing contract versions, and manually summarizing massive troves of legal documentation. Traditional attempts at automation—such as generic summaries from AI tools like ChatGPT—fell short, lacking the critical accuracy, citations, and domain-specific context lawyers demand. The process was expensive, slow, and fraught with potential inaccuracies.

Legal AI at Scale: Hybrid, Filtered, Multitenant Search

Aracor built an end-to-end AI platform specifically tailored for the complex, precise requirements of dealmakers: family offices, private equity firms, venture capitalists, and other high-stakes investors. At the core of their innovation was a robust vector search capability provided by Qdrant.

Lesly Arun Franco, CTO of Aracor, explains, “Search is a massive problem. Our platform ingests thousands of legal documents, each requiring precise retrieval and accurate citations. Without Qdrant, delivering this level of performance and scale was nearly impossible.”

By adopting Qdrant’s vector database, Aracor gained the critical ability to efficiently index and search through massive document repositories. This empowered the platform to automatically generate precise document summaries, accurate comparison reports, signature verifications, and essential citations for every finding—features indispensable in the rigorous legal world.

Why Qdrant?

When Aracor first set out to integrate a vector database roughly eighteen months ago, Lesly and the team evaluated several vendors. Qdrant stood out for several reasons:

• Open-source and Self-hosted: Qdrant provided a developer-friendly, easily deployable solution at a stage when Aracor needed both flexibility and affordability.

• Superior Scalability: Unlike other databases, Qdrant offered seamless scaling and robust handling of immense document volumes—critical as Aracor’s client base rapidly expanded.

• Hybrid and Metadata-Driven Search: Qdrant made it possible to combine semantic search with structured filters—so users can instantly surface the exact clause, obligation, or restriction they need, even inside complex, nested legal documents. This dramatically improves speed, accuracy, and confidence in results.

Tangible Results with Qdrant

Since integrating Qdrant, Aracor has realized substantial operational improvements:

• Massive Time Savings: Tasks such as signature validation and document summarization, previously taking weeks, now complete in mere minutes. Customers report: 90% faster due diligence workflows, 70% reduction in document turnaround time, and 40% fewer legal hours required.

• Increased Accuracy: High-quality citation and retrieval accuracy have significantly increased user trust, a crucial advantage in the meticulous legal environment.

• Scalable Infrastructure: Transitioning from self-hosted to Qdrant’s cloud solution has streamlined operations, allowing Aracor’s technical team to focus on further feature development and optimization, such as integrating multimodal embeddings and hybrid search.

Looking Forward

With Qdrant handling the heavy lifting of scalable vector search, Aracor continues to innovate, working towards even more sophisticated multimodal and domain-specific embedding techniques. As they expand their platform, Aracor is confident in their capacity to support increasingly complex, high-volume document processing needs, all backed by the proven power and reliability of Qdrant’s vector database solution.