Better Search Took This AI Agent From 36% to 98% Accuracy - Aamir Shakir, Mixedbread
핵심 요약
Switching from a traditional Frankenstein RAG stack to Mixedbread's latent interaction and multimodal search model increased a hedge fund's agent answer accuracy from 36% to 98% while cutting LLM token costs by 3x.
하이라이트
Mixedbread ingested data from banks and hedge funds with billions of documents while maintaining SOC 2 and ISO compliance.
Traditional RAG pipelines experience accuracy degradation because parsing, chunking, and single-vector embedding are lossy processes.
A major hedge fund increased agent answer accuracy from 36% to 98% by switching to Mixedbread's search infrastructure.
Multimodal search processes visual page screenshots directly to eliminate information loss caused by standard OCR and text extraction.
Improved search context precision reduced overall LLM token spend by 3x.
타임라인
Introduction to Mixedbread and Enterprise Search Needs
Mixedbread evolved from a research lab focused on retrieval over two and a half years into a platform that handles PDFs, spreadsheets, images, video, and audio across more than 100 languages. Within five months of operation, the company onboarded major banks and hedge funds managing billions of documents. When financial analysts query large knowledge bases containing research materials and interviews, local tools often struggle with relevancy, forcing models to read every document and driving query costs up to ten or twenty dollars.
- Mixedbread provides a multimodal and multilingual search product built specifically for AI agents.
- The platform offers zero data retention, custom ingestion pipelines, and compliance standards such as SOC 2 and ISO.
- Standard local terminal searches like grep fail on binary PDF formats and scans, driving up query costs.
The Bottlenecks of Traditional Frankenstein RAG Stacks
Traditional retrieval-augmented generation setups require evaluating multiple vendors for OCR, chunking, embedding models, vector databases, and re-ranking over several months. Because each processing stage is lossy, the final vector representation lacks the necessary precision to differentiate similar corporate earnings reports across different years. Consequently, agents overcompensate by launching numerous sub-queries, making production scaling cost-prohibitive.
- Every step in a classic retrieval pipeline—including transcription, OCR, chunking, and embedding—introduces cumulative noise and data loss.
- Single-vector embedding models compress complex financial documents and tables into single coordinate points, failing to distinguish fine-grained data.
- Poor retrieval quality forces agents to execute aggressive sub-agent queries, inflating context windows and operational costs.
Latent Interaction Models and Multimodal Architecture
After decades of slow text search progress following BM25, Mixedbread introduced a latent interaction model that maps out specific elements like net income and revenue while retaining overarching contextual data. By leveraging multimodal visual perception, the system enables precise bounding-box citations for generated answers. Furthermore, specialized agentic search models execute multi-hop reasoning efficiently, operating significantly faster and cheaper than comparable alternatives.
- Latent interaction models generate multiple contextualized embeddings for both documents and queries to capture fine-grained page details.
- Visual layout processing bypasses traditional OCR failure points by creating embeddings directly from page screenshots.
- Agentic search primitives decompose complex qualitative questions into multi-hop queries to retrieve accurate data.
Case Study Results and Enterprise Onboarding
Enterprise deployment metrics demonstrate that improving data access solves core agent reliability issues. The hedge fund case study highlights a dramatic jump in accuracy alongside a threefold reduction in LLM spending due to reduced token requirements for reading and parsing. Mixedbread supports quick testing through free credits and rapid bucket integration for security-conscious organizations.
- A large hedge fund with over one billion documents integrated Mixedbread within one and a half weeks.
- Agent answer accuracy increased from 36% to 98% after moving away from traditional vector database stacks.
- Optimized context delivery reduced LLM token expenditure by 3x.
설명
Agents are smart enough. Most of the time, the real problem is getting them to the right data. Aamir Shakir of Mixedbread explains why the usual stack of OCR, chunking, embeddings and a vector database loses information and leaves agents reading every document. He shows multimodal, late-interaction retrieval on Intel earnings reports, and shares a customer whose answer accuracy went from 36% to 98% after switching their search. 0:00 Intro to Mixedbread 1:30 The equity analyst problem 3:30 The key bottlenecks 4:00 The "Frankenstein stack": OCR, chunking and vectors 6:02 Why "just add a vector database" falls short 8:26 The bottleneck is access to your data 9:04 How retrieval has evolved 11:08 Demo: searching Intel earnings reports 14:57 Customer result: 36% to 98% accuracy Aamir Shakir, Mixedbread Join us in SF for the second annual AIE CODE summit: https://aie.codes our last flagship summit of 2026. Applications live now!