Vectara

Vectara is an enterprise agent platform that ingests multimodal company data, retrieves relevant context, and powers grounded AI agents, search, and RAG applications. It can be deployed as SaaS, in a customer VPC, or on-premises, and exposes REST API, MCP, and agent-to-agent interfaces. The company also maintains vectara-ingest, an Apache 2.0 open source Python framework for crawling data sources into Vectara.

What It Is

Vectara describes itself as an agent platform built for enterprise security, accuracy, scalability, and observability. Its FAQ states it is not a vector database or a standalone embedding service. It bundles a document processing engine, chunking, embedding models, its own internal vector database, and a retrieval engine as components of the platform. Typical workloads listed include failure analysis, IT service management, knowledge search, customer support, legal document analysis, and document generation.

How the Platform Works

Data such as PDFs, Office documents, images, tables, emails, wikis, and tickets is ingested, parsed, and enriched with metadata. Retrieval combines neural and lexical search with re-ranking, followed by grounded generation. The agent harness covers skills, tools, MCP, memory, and sub-agent workflows. Vectara says answers carry a factual consistency score and a traceable path back to source data, with audit trails and access controls that respect each source's permissions.

Models and Deployment

Users can use Vectara's built-in models or bring their own LLMs, including OpenAI-compatible endpoints, Anthropic Claude, and Google Vertex AI models. The FAQ says the platform supports over 100 languages. Vectara states it holds SOC 2 Type II and HIPAA compliance. On-prem installs are currently offered for North America, APAC, and the UAE.

Ingestion Tooling

The open source vectara-ingest project provides pre-built crawlers for websites, RSS feeds, Jira, Notion, Slack, Docusaurus sites, and more. It runs via Docker, a CLI, or as a Python package. It supports document parsers such as Docling and Unstructured, optional table and image summarization, incremental reindexing, and air-gapped builds.