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Provides pre-parsed Parquet snapshots of English and French Wikipedia articles with structured fields (sections, infoboxes, tables, references, images) and credibility signals — optimized for large-scale analysis, retrieval-augmented generation, and model development.
Gives LLM agents self-editing memory that persists across sessions, so they keep learning about a user instead of resetting each chat. Model-agnostic: bring your own LLM while it handles the memory and agent state, run via API or open source.
Converts PDFs, Office files, HTML, images and audio into one structured DoclingDocument, with deep PDF layout, reading order, table-structure and formula recognition, OCR, and native LangChain/LlamaIndex/Haystack integrations for RAG pipelines.
Self-hostable alternative to Google NotebookLM: organize PDFs, videos, audio, web pages, and Office docs, then chat over them, take AI-assisted notes, and search via full-text and vector. Routes to 18+ model providers and generates 1-4 speaker podcasts.
Reference architectures and microservices for building GPU-accelerated vision agents that enable natural-language video search, long-video summarization, visual Q&A, and alert verification. Integrates NVIDIA NIM models, embeddings, VLMs/LLMs, and agent workflows for deployable video-analytics stacks.
Provides a local-first Markdown knowledge graph that LLMs and humans can both read and write via the Model Context Protocol (MCP). Features two-way, editable notes, semantic search (embeddings + hybrid ranking), and optional cloud sync and team workspaces.
Connects AI agents to 50+ apps and databases — Notion, Slack, Salesforce, GitHub, Jira — then continuously syncs and indexes their data behind one search API, with auth, ingestion, and retrieval exposed via MCP, REST, and SDKs.
Runs penetration tests autonomously: a multi-agent system (researcher, developer, executor) plans attacks, writes and runs exploit code, and chains 20+ tools like nmap, metasploit and sqlmap in isolated Docker containers — for authorized testing only.
Wires retrievers, rerankers, and generators as standalone MCP servers orchestrated in YAML, so iterative RAG logic fits in dozens of lines instead of glue code. Adds loops, conditional branches, one-command web UIs, and shared evaluation benchmarks.
Generates multi-chapter long-form novels with LLMs, automatically linking context and managing foreshadowing for global coherence. Features vector-based retrieval, character/state tracking and a GUI-driven pipeline; requires LLM/embedding API keys.
Performs automated, citation-backed deep research across web, arXiv, PubMed and your private documents using configurable local or cloud LLMs. Runs locally with per-user SQLCipher encryption, Docker/pip installs, LangChain integrations, and an MCP server for assistant integration.
Transforms unstructured financial content—papers, news, blogs, and filings—into a queryable semantic knowledge graph for retrieval-augmented research. Combines domain-tuned LLMs, embedding-based search, and modular ingestion pipelines; aimed at quant research teams and institutional workflows.