On day one, you can see results on a representative cut of your data. For a focused use case, your first context layer can be live within a week and ready for agents. Larger, multi-system deployments roll out in phases, with Landfall partnering through the full build-out.

No. Landfall brings auto-modeling, entity resolution, knowledge graph, retrieval, and reasoning together in one context layer. It works with your existing data stack while reducing the need for separate graph databases, vector databases, Text-to-SQL tools, and context-specific ETL glue.

Most context layers require large models and heavy infrastructure to compensate for poor data quality. Landfall structures context correctly upfront so your agents can run on smaller, cheaper models with higher accuracy. Lower token consumption, lower compute costs, better results, and faster responses.

No. Customer data is never used to train Landfall models. Depending on deployment, data is either isolated in Landfall-managed infrastructure or kept within your own VPC/on-prem environment.

Yes. Landfall supports deployment on your own infrastructure, including customer VPC, on-premises, private cloud, and all major cloud environments (AWS, Azure, and Google Cloud).