Voyage AI Tackles AI Hallucinations with Advanced RAG Tools

AI tends to make things up. That’s unappealing to just about anyone who uses it on a regular basis, but especially to businesses, for which fallacious results could hurt the bottom line. Half of workers responding to a recent survey from Salesforce say they worry answers from their company’s generative AI-powered systems are inaccurate.

While no technique can solve these “hallucinations,” some can help. For example, retrieval-augmented generation, or RAG, pairs an AI model with a knowledge base to provide the model supplemental info before it answers, serving as a sort of fact-checking mechanism.

Entire businesses have been built on RAG, thanks to the sky-high demand for more reliable AI. Voyage AI is one of these. Founded by Stanford professor Tengyu Ma in 2023, Voyage powers RAG systems for companies including Harvey, Vanta, Replit, and SK Telecom.

“Voyage is on a mission to enhance search and retrieval accuracy and efficiency in enterprise AI,” Ma said in an interview. “Voyage solutions [are] tailored to specific domains, such as coding, finance, legal, and multilingual applications, and tailored to a company’s data.”

To spin up RAG systems, Voyage trains AI models to convert text, documents, PDFs, and other forms of data into numerical representations called vector embeddings. Embeddings capture the meaning and relationships between different data points in a compact format, making them useful for search-related applications, like RAG.

Voyage uses a particular type of embedding called contextual embedding, which captures not only the semantic meaning of data but the context in which the data appears. For example, given the word “bank” in the sentences “I sat on the bank of the river” and “I deposited money in the bank,” Voyage’s embedding models would generate different vectors for each instance of “bank” — reflecting the different meanings implied by the context.

Voyage hosts and licenses its models for on-premises, private cloud, or public cloud use, and fine-tunes its models for clients that opt to pay for this service. The company isn’t unique in that regard — OpenAI, too, has a tailorable embedding service — but Ma claims that Voyage’s models deliver better performance at lower costs.

“In RAG, given a question or query, we first retrieve relevant info from an unstructured knowledge base — like a librarian searching books from a library,” he explained. “Conventional RAG methods often struggle with context loss during information encoding, leading to failures in retrieving relevant information. Voyage’s embedding models have best-in-class retrieval accuracy, which translates to the end-to-end response quality of RAG systems.”

Lending weight to those bold claims is an endorsement from OpenAI chief rival Anthropic; an Anthropic support doc describes Voyage’s models as “state of the art.”

“Voyage’s approach uses vector embeddings trained on the company’s data to provide context-aware retrievals,” Ma said, “which significantly improves retrieval accuracy.”

Ma says that Palo Alto-based Voyage has just over 250 customers. He declined to answer questions about revenue.

In September, Voyage, which has around a dozen employees, closed a $20 million Series A round led by CRV with participation from Wing VC, Conviction, Snowflake, and Databricks. Ma says that the cash infusion, which brings Voyage’s total raised to $28 million, will support the launch of new embedding models and will let the company double its size.