Vectorize 2.0

Product Introduction

1. Vectorize 2.0 is a RAG-as-a-Service platform designed to accelerate the development of AI applications by handling complex data integration and retrieval challenges. It enables users to build production-ready Retrieval-Augmented Generation (RAG) systems without managing infrastructure or vectorization pipelines. The platform supports seamless ingestion of unstructured data from documents, knowledge bases, and SaaS tools while optimizing vectorization strategies for accuracy.
2. The core value of Vectorize 2.0 lies in its ability to simplify AI application development by automating data synchronization, retrieval optimization, and real-time updates. It eliminates manual data preprocessing and ensures LLMs always operate on the most relevant, up-to-date information. This reduces development time by 10x compared to custom-built RAG solutions while maintaining enterprise-grade scalability.

Main Features

1. Chat Agents & Widgets: Deploy no-code, hosted chatbots with one-line website integration using a customizable JavaScript widget. These agents support multi-turn conversations and automatically leverage context from connected data sources.
2. Remote Model Control Plane (MCP): Integrate with leading LLMs like Claude and Cursor through a unified API, enabling hybrid inference workflows across multiple providers. The MCP manages model routing, cost optimization, and fallback mechanisms.
3. Real-Time Pipelines: Maintain always-on data synchronization with automatic ingestion from SaaS platforms (e.g., Notion, Confluence) and file storage systems. Changes propagate to vector indexes within seconds using WebSocket-based streaming.
4. Hybrid Search Engine: Combine dense vector search with knowledge graph relationships and sparse keyword matching for 23% higher recall accuracy than pure vector-based systems. The engine supports dynamic re-ranking based on freshness and source credibility.

Problems Solved

1. Data Integration Complexity: Resolves the challenge of continuously syncing unstructured data across fragmented SaaS platforms and internal knowledge bases. The platform handles schema conflicts, content chunking, and metadata extraction automatically.
2. LLM Hallucination Reduction: Addresses inaccurate AI responses by implementing context-aware retrieval with automatic relevance scoring. Hybrid search filters low-confidence matches before they reach the LLM.
3. Enterprise Scalability: Solves latency issues in large-scale RAG deployments through distributed vector indexing and GPU-accelerated batch processing. The system scales to handle 50M+ documents with sub-100ms query latency.

Unique Advantages

1. Unified Data Plane: Unlike competitors requiring separate ETL tools, Vectorize 2.0 provides native connectors for 120+ SaaS platforms and file formats with field-level mapping. This enables live data syncs without engineering overhead.
2. Adaptive Vectorization: Automatically tests multiple embedding models (e.g., OpenAI text-embedding-3, BERT variants) to select the optimal strategy for each data type. Continuous A/B testing updates strategies as data distributions change.
3. Compliance-Ready Architecture: Offers built-in data anonymization, PII redaction, and audit trails for GDPR/HIPAA compliance. All data remains encrypted in transit and at rest using FIPS 140-2 validated modules.

Frequently Asked Questions (FAQ)

1. What data sources does Vectorize 2.0 support? The platform connects to databases (PostgreSQL, MongoDB), cloud storage (S3, GCS), SaaS tools (Slack, Salesforce), and documents (PDF, Markdown) via pre-built connectors. Custom REST API integrations can be configured in under 15 minutes.
2. How quickly can I deploy a production RAG system? Users typically deploy chatbots or search interfaces in 2-3 hours using pre-built templates. The platform automatically provisions vector databases, LLM gateways, and monitoring dashboards during setup.
3. Does it handle unstructured data like images or audio? While primarily text-optimized, Vectorize 2.0 supports multimodal data through integration with vision-language models like CLIP. Audio files are processed via Whisper ASR before vectorization.
4. Can I use my existing LLM infrastructure? Yes, the Remote MCP works with self-hosted models (Llama 2, Mistral) and cloud providers (AWS Bedrock, Azure OpenAI). Model switching occurs without code changes through the configuration UI.
5. How are real-time updates managed? Changed data triggers incremental re-indexing via distributed Kafka queues. Critical updates bypass batch processing using priority WebSocket streams, achieving sub-5s index refresh times.