CNTXT

Teams use CNTXT to turn an AI idea into a running edge application with a repeatable workflow. A typical project starts by sketching the logic in the Flow Builder: connect an LLM or vision model, add tools such as search or classification, and wire in the data sources the app needs. Once the flow works locally, the same pipeline is deployed to edge locations so requests are handled near users, which helps with fast responses in field, retail, and on-site environments.

For knowledge-heavy tasks, CNTXT is applied by indexing documents in the built-in Weaviate VectorDB, then retrieving relevant passages during each request to ground answers. This supports practical RAG setups like internal helpdesks, customer support chat, policy lookup, and product search. When a workflow must interact with other systems, teams connect it through GraphQL endpoints or webhooks to trigger actions such as creating tickets, updating records, or sending notifications.

After deployment, operators rely on monitoring to watch live behavior, spot failures, and trace performance changes as traffic grows. Evaluation tools are used to run scenario suites, compare outputs across model or prompt updates, and catch regressions before a rollout. When accuracy depends on better examples, labeling services help produce cleaner datasets so downstream models and retrieval perform more consistently. The outcome is an edge-ready AI service that can be iterated safely: build the flow, connect data, integrate with business systems, deploy close to users, and continuously test and refine.