Runs approximate nearest-neighbor search over billions of vector embeddings, separating compute from storage so reads and writes scale independently. Offers HNSW, IVF, DiskANN, and GPU CAGRA indexes plus hybrid dense+sparse and BM25 retrieval.
Build, customize, and export professional resumes via a privacy-first web app with real-time preview and client-side PDF export. Offers templates, JSON/DOCX exports, Docker self-hosting, and optional AI-driven content suggestions.
Provides vector search, LLM orchestration and language-model workflows with an embeddings database, RAG pipelines, multimodal indexing and agents. Runs locally or in containers and supports multiple models and language bindings (JS/Java/Rust/Go).
Privacy-first, self-hosted personal knowledge manager with block-level references, Markdown WYSIWYG editing and large-document performance; offers local-first storage, OpenAI-based AI writing/Q&A integration, OCR, mobile apps and Docker deployment.
Exposes a self-hosted WhatsApp HTTP/REST API that runs a real WhatsApp Web instance so apps and AI agents can read/send messages, manage contacts, and automate flows. Offers three engine modes (WEBJS, NOWEB, GOWS), Docker images, and MCP support; relies on WhatsApp Web so blocking risk exists.
Builds business systems like CRMs, ERPs, and internal tools without code. Data-model-driven design keeps data in standard relational databases separate from the UI, avoiding vendor lock-in. A microkernel plugin architecture makes features composable.
Deploys trained SavedModels behind gRPC and REST endpoints, with hot-swappable versioning so new weights load without downtime. Built around servables, loaders, sources, and a manager, plus request batching to cut accelerator cost.
Build internal web apps, dashboards, workflows and AI agents with a visual low-code builder that connects to databases, APIs, SaaS and object storage. Includes AI app generation, AI query builder and AI debugging plus self-hosting and enterprise features like RBAC and audit logs.
Unified metadata platform for data discovery, observability, and governance — central metadata repository, column-level lineage, and a pluggable ingestion framework with 84+ connectors. Suited for teams that need searchable data catalogs, automated lineage, and collaborative data governance.
Library for benchmarking, developing, and deploying deep-learning visual anomaly-detection models — includes ready-to-use model implementations (PatchCore, DINO-based), experiment/HPO tooling, OpenVINO export for edge inference, and a low-code Studio for deployment.
Orchestrates ML training pipelines and production agent workflows from one Python codebase that runs unchanged from a laptop to Kubernetes or any cloud. Auto-versions artifacts, models, and agent checkpoints, with no orchestrator or framework lock-in.