Graph database core
Built on a highly scalable graph database, Langcore captures the relationships in your data and answers fast semantic queries at scale.
Langcore connects your tools and systems of record into one live knowledge graph, so your AI answers from verified, current facts instead of guesswork.
Enterprise users expect AI to just answer. Behind that answer sits a whole knowledge layer: current data, context, permissions, and the plumbing between them.
Modern AI models struggle to give trustworthy answers when they work from stale or siloed data. Langcore fixes that by adding a reliable layer of meaning and context on top of the sources you already have. It connects directly to your live databases and tools, from internal wikis to issue trackers like JIRA or CRMs like Salesforce, and brings their information together into a single knowledge graph.
The graph is dynamic. It handles real-time updates, so the data feeding your AI is always fresh. Your agents and chatbots refer to the latest facts from across the organization and avoid the hallucinations that happen when a model is asked about something outside its training data. The result is answers that stay accurate, current, and grounded in verified data rather than guesswork.
Enterprise data is fragmented across systems and formats: relational databases, spreadsheets, emails, call logs, tickets, documents, images. Langcore is an integration fabric that unifies these disparate sources into one holistic knowledge layer. With mountable storage connectors, structured and unstructured data land in the same graph without manual conversion.
Permissions are modeled in the graph itself, so teams and roles see only the data relevant to them, all on the same unified platform. Efficient permission queries let you audit or adjust who can access specific knowledge in an instant. Langcore's security model makes sure your AI and your users only ever see authorized, compliant data, which makes it fit for sensitive enterprise environments.
Langcore isn't a static knowledge base. It's the backbone for building agents, chatbots, and copilots that actually understand your data. Developers hook it into generative AI frameworks so LLMs can retrieve facts and context from the graph on the fly. This Graph + AI approach gives assistants more accurate, context-aware answers, and combines knowledge from many sources into a richer response.
It's built human-in-the-loop. Langcore automates knowledge integration, but experts can still oversee and refine what the AI produces, so the facts it fetches stay relevant and correct. That feedback loop means Langcore learns from your users: over time it adapts and improves its graph and its suggestions from real-world use.
Built on a highly scalable graph database, Langcore captures the relationships in your data and answers fast semantic queries at scale.
Connects to any source, on-prem or cloud. Structured and unstructured data are unified in one graph without manual conversion.
Live sync with your systems of record. The graph updates in real time to reflect the latest changes.
Auto-detects and aligns schemas, ontologies, and taxonomies, so integration is plug-and-play.
Fine-grained RBAC and JWT authentication. You control data visibility with relationship-based permissions.
Expert feedback at every stage. Users supervise, validate, and correct AI-driven processes.
Plugs into AI applications easily, with a robust API and SDK for developers.
Flowpad is the product we build on Langcore. It brings your team and trusted experts directly into Claude Code and Codex sessions, grounded in the same live knowledge graph, so every session becomes shared, searchable team knowledge instead of dying in one person's terminal.
It's also the quickest way to put Langcore to work. You get Langcore by running Flowpad.
Explore Flowpad →Langcore powers Flowpad today, so the quickest way to use it is through Flowpad. Want to run Langcore on your own stack, or shape where it goes next? Reach out to hear about releases and be among the first to build on it.