- Connect to SQL, NoSQL, and API-driven data sources.
- Ingest from cloud sources like AWS S3, Google Cloud Storage, Azure Blob, and more.
- Real-time ingestion over WebSocket for continuous updates.
- Extracts and vectorises content from documents, tables, spreadsheets, presentations, and more.
- Supports file types: PDF, CSV, JSON, XML, DOC, PPT, SCORM, and more.
- Parses complex layouts such as charts, diagrams, and nested tables.
- Processes media: video and audio.
- Real-time monitoring and observability via custom dashboards.
- End-to-end encryption to protect data in transit and at rest.
- Fully self-hosted or deployable to your private cloud infrastructure.
- Scales to high-volume indexing with advanced, storage-efficient algorithms.
- Supports hybrid search and semantic reranking for maximum retrieval relevance.
- Works with multilingual content for global datasets.
- Designed for seamless integration with semantic search, RAG pipelines, and AI agents.
Securely transform and prepare data on-prem, keeping sensitive information in-house.
Trismeg’s multimodal vectorisation pipelines extract and structure information from documents, tables, PDFs, charts, images, and diagrams, preserving both layout and meaning. We support data from files, databases, APIs, and streams, automatically handling chunking, enrichment, and embedding generation. The result: high-quality vectors optimised for RAG, semantic search, and agent-based AI. Scalable by design, and ready for real-world production.
Most teams underestimate the complexity of preparing data for RAG, semantic search, or AI agents. What starts as “just embedding some documents” quickly turns into months of building data connectors, parsing logic, chunking strategies, indexing workflows, and monitoring systems.
The result? Delayed launches, broken pipelines, stale data, and valuable engineering time spent reinventing infrastructure instead of focusing on AI.
Trismeg gives you production-grade data vectorisation out of the box — from ingestion and chunking to semantic indexing and embedding evaluation.
Our mission is to help teams deploy faster, stay accurate at scale, and avoid the operational overhead of stitching together multiple tools. We make AI infrastructure easy to manage, secure by default, and ready for growth.
Trismeg supports a wide range of data formats — including documents (PDF, TXT, DOCX), SQL/NoSQL databases, CSVs, JSON, and real-time data streams via WebSocket or API. You can also connect cloud storage platforms like Dropbox, Google Drive, or S3. We offer fine-grained control over what data is used, allowing you to select specific folders, tables, or file types through a flexible and secure interface. Custom data types are supported via configurable adapters and modular extensions.
No – Trismeg offers a no-code setup experience. You can connect data sources and configure vectorisation pipelines through an intuitive UI or API. Custom workflows and integrations can be added with minimal configuration. All data is handled securely using the latest industry-standard encryption protocols, ensuring complete protection during setup and processing.
All embeddings are stored within your infrastructure — whether self-hosted or deployed in your private cloud. By default, Trismeg uses LanceDB for efficient, high-performance storage, but you’re free to integrate any preferred vector database. You maintain full control over where and how your embeddings are stored.
Yes, the entire process is automated — from ingestion to embedding generation — with support for continuous or scheduled runs. You can fine-tune triggers, batch settings, and integrate with existing tools or workflows. The entire pipeline can be monitored in real time through live dashboards, giving you full visibility into data flow, performance, and system health.
No limits. You can process unlimited data volumes. Performance scales with your infrastructure, and the system is built to support high-throughput workloads — including custom streaming sources and large datasets.
Trismeg supports a range of open-source embedding models, including BGE, Nomic, MiniLM, and more.