Vectorize

Vectorize is an AI data platform designed to connect AI agents with precise, relevant data from both structured and unstructured sources. It provides a unified, no-code pipeline environment that turns complex documents, PDFs, images, and cloud files into agent-ready knowledge. By focusing on agent-first retrieval rather than generic search, Vectorize ensures that AI assistants and copilots get exactly the information they need—without noise, hallucination, or wasteful processing. The platform also stands out with its multimodal extraction engine (Vectorize Iris), which can understand diagrams, tables, and visual content inside documents, not just plain text.

• Agent-First Retrieval: Dedicates every pipeline to serve AI agents directly, filtering out irrelevant content so agents see only task-critical data.
• Multimodal Extraction (Vectorize Iris): Uses a vision model to extract information from complex PDFs, images, diagrams, and scanned documents, preserving context that text-only parsers miss.
• Custom Metadata Filters: Lets users define structured fields (tags, categories, entities) that agents can use as filters to pinpoint the most relevant chunks of data.
• Configurable No-Code Pipelines: Production-ready pipelines can ingest data from Google Drive, Amazon S3, and other sources with zero custom coding, including automated syncing and chunking.
• Real-Time Processing & Adaptation: Pipelines react to data changes instantly; AI agents can incorporate live updates, learn from new information, and refine retrieval strategies.
• Multilingual Document Processing: Automatically handles over 50 languages, maintaining extraction accuracy across Latin, Cyrillic, Asian, and Arabic character sets.
• Advanced Analytics & Monitoring: Built-in dashboards track pipeline health, usage, and performance metrics, helping teams optimize retrieval quality.

1. Sign up and create a project: Go to vectorize.io, register for a Free or paid plan, and set up your first project workspace.
2. Define a RAG pipeline: Click “New Pipeline,” name it, and select the primary data source (e.g., Google Drive folder or S3 bucket). The no‑code wizard will guide you through connection setup.
3. Configure extraction and metadata: Choose extraction settings—enable the Iris vision model for complex PDFs, set chunking rules, and define custom metadata fields (like document type, date, or department) that your AI agents will filter by.
4. Test retrieval with the built‑in sandbox: Use the Playground to ask sample questions and see which chunks are returned. Adjust filters, chunk size, or metadata until retrieval precision meets your needs.
5. Connect your AI agent: Copy the generated pipeline API endpoint and authentication key, then plug it into your agent framework (LangChain, custom app, etc.). The agent can now send queries and receive cleaned, filtered context.
6. Monitor and iterate: Check the analytics dashboard regularly to view page usage, latency, and retrieval accuracy. Refine metadata, add new sources, or enable real‑time sync as your requirements evolve.

Vectorize operates on a freemium model with tiered page‑based limits and additional pay‑as‑you‑go options. All plans include core features, though advanced analytics, support SLAs, and pipeline counts increase with higher tiers. Note that page processing overages on Starter and Pro are billed at usage‑based rates after the included allowance.

• Free: $0/month – 1 RAG pipeline, 1,500 pages/month, basic analytics, limited vision model access, community support.
• Starter: $99/month – 2 RAG pipelines, 15,000 pages/month, unlimited source connectors, advanced analytics, full API, pay‑as‑you‑go for extra pages, community support.
• Pro: $399/month – 3 RAG pipelines, 65,000 pages/month, additional pay‑as‑you‑go processing, performance monitoring, 12×5 support with SLA.
• Enterprise: Custom pricing – unlimited pipelines, tailored page limits, custom connectors, up to 24×7 support with 30‑minute SLA, custom reporting.

Since pricing configuration can change, always check the official Vectorize website for the most up‑to‑date details.

• Web Application: Full platform access via browser for pipeline creation, management, analytics, and Playground testing.
• API: RESTful endpoints to connect any AI agent or application to your pipelines; token‑based authentication.
• Cloud Storage Connectors: Pre‑built integrations with Google Drive and Amazon S3, enabling direct ingestion without custom code.
• SDK Support: Developer libraries (available for Python, JavaScript, etc.) to simplify pipeline configuration and retrieval calls.

• Start with a narrowly scoped pilot pipeline – feed in one document type before expanding, so you can calibrate extraction quality and metadata filters quickly.
• Always define meaningful metadata fields that mirror how your agents will query; for example, “product_category” or “effective_date” drastically improves retrieval precision.
• Use the Iris vision model for scanned documents or diagrams, not just plain text PDFs, to capture information embedded in images, flowcharts, or tables.
• Turn on real‑time sync only for frequently changing folders; for static archives, scheduled daily sync reduces unnecessary processing costs on paid plans.
• Leverage the sandbox Playground before putting a pipeline into production – it’s the fastest way to spot missing context or irrelevant chunks.
• Monitor page usage against your plan limits (especially on Starter and Pro) and set up page processing alerts to avoid unexpected overages.

• AI Development Teams building copilots, chatbots, or internal knowledge agents who need a reliable, low-code data ingestion layer.
• Product Managers & No‑Code Builders who want to prototype RAG applications quickly without writing extraction or chunking code.
• Enterprise Architects looking to unify messy document repositories into a single, queryable source of truth for AI agents across the organization.
• Data‑Driven Marketing & Sales Ops professionals who need agents to pull facts, figures, and competitor insights from slide decks and long reports.
• Legal & Compliance Specialists handling high‑volume document review with strict precision requirements and metadata tagging for audit trails.
• Educators & Researchers who need AI assistants that can accurately retrieve specific experimental protocols or citations from thousands of academic PDFs.