Platform
Pipelogic Agents
BetaAI engineers that know how to build components and backends.
Pipelogic Backends
The typed dataflow layer that connects models, inputs, rules, and actions.
Pipelogic Components
Build visually, reuse Components, and extend with Python or C++.
Pipelogic Runtimes
Deploy, monitor, update, and operate Backends across environments.
Pipelogic Applications
BetaBuild and release user-facing Applications that connect to AI Backends.
Start
Main developer documentation.
Fastest path to building something.
Step-by-step guide to assembling a typed AI Backend.
Step-by-step guide to writing and releasing a custom Component.
Step-by-step guide to creating, connecting, and releasing an Application.
Build
Compose Components into Backends visually.
Use the command-line interface to build, release, deploy, inspect, and debug.
Build Python Components for models, APIs, transformations, and business logic.
Build high-performance Components for low-latency or compute-heavy workloads.
Understand how Pipelogic validates Component connections.
Define Component inputs, outputs, parameters, files, models, and runtime behavior.
Learn
Essays, practical guides, and explainers for building real-world AI systems.
Reference patterns for AI Backends, Components, Runtimes, Apps, deployment, and operations.
Guides for safety, inspection, logistics, maintenance, voice, documents, robotics, and private AI assistants.
Real deployments, outcomes, implementation examples, and production lessons.
Company
Pipelogic vs. AI Landscape
See how Pipelogic fits into the modern AI stack.
Personal workspace of Meghana Nanda Kumar
No solutions published yet.
Published applications from this workspace will appear here.
No components published yet.
Published components from this workspace will appear here.
