Langflow builds fast and breaks quietly
in production - we fix both.

We start with an audit of what you have. In most cases we can preserve the business logic and refactor the architecture around it - adding error handling, restructuring component boundaries, and connecting proper integrations without rebuilding from scratch. If a flow is too tangled to safely extend, we tell you that directly and explain why before touching anything.

Langflow is a visual builder that runs on top of LangChain and LlamaIndex under the hood. The canvas abstracts away the Python code, which speeds up prototyping but also hides what is actually executing. For most mid-market automation use cases the visual layer is fine. Where it becomes a constraint is in complex conditional logic, custom tool definitions, or performance-sensitive pipelines - situations where we sometimes write custom Langflow components in Python rather than relying only on the built-in node library.

Langflow Cloud removes the infrastructure overhead and is a reasonable starting point for teams without dedicated DevOps capacity. Self-hosting gives you full control over data residency, network access, and cost at scale. The right answer depends on your compliance requirements, your internal IT capacity, and how many flows you plan to run in production. We help you make that call with a clear trade-off analysis before committing to either path.

By default, Langflow does not encrypt data in transit between components beyond standard HTTPS, and it stores conversation memory in whatever backend you configure. If your flows touch PII, financial records, or health data, you need explicit decisions about where that data lands - in the vector store, in logs, in LLM provider context windows. We map those data flows during the audit phase and design the architecture to match your compliance posture.

Yes, through Langflow's API Request component or custom Python tool components. We build authenticated connections to platforms like Salesforce, HubSpot, NetSuite, and others using their REST or GraphQL APIs. The integration work is in the data mapping and error handling - making sure the agent sends correctly formatted payloads and handles API rate limits or authentication failures without silently dropping data.

A focused engagement to productionize one or two existing flows and add proper integrations typically runs four to eight weeks depending on the complexity of your data sources and the number of external systems involved. Greenfield builds of a multi-agent workflow from scoping through deployment run longer. We give you a specific timeline after the audit, not before, because the scope always depends on what we find in your environment.

Yes, and we consider it a requirement for a successful handoff. We document every flow and custom component, run working sessions with whoever will own the system internally, and produce runbooks for the failure scenarios we have seen most often. The goal is that your team can extend and maintain what we build without calling us for every change - though we are available for ongoing support if you want it.