IntraLLM AI Workflows help your team move beyond isolated prompts and define how inputs are processed, routed and transformed into useful outputs across the platform.
A useful AI process often involves more than sending a prompt to a model. It may need to retrieve relevant context, call a function, route a request according to a condition, transform information or pass work to a specialised pipeline.
IntraLLM AI Workflows provide the execution structure behind those steps. Your team can define how platform capabilities interact, creating a more coherent and repeatable way to process requests across advanced assistants, automation and retrieval-based experiences.
Turn a complex request into an ordered process where each model, tool or transformation has a clear role in producing the result.
Bring model inference, retrieval, functions, tools and pipelines together instead of treating each capability as an isolated component.
Use defined execution logic to support consistent processing across templates, tool-based tasks, pipelines and other automated experiences.
Move through one connected workflow for selecting a model, preparing data,
running GPU training and putting the result to work.
Add models, agents, skills, tools and data-processing components wherever they contribute most. Each node performs a focused role while the connections coordinate them as one complete process.
Select the model, skill or tool behind each node and define the behaviour required at that point in the process. Teams can refine individual stages without losing sight of how they contribute to the wider workflow.
Run the complete workflow and inspect how information moves between its stages. Node-level traces reveal the inputs and outputs produced along the route, helping technical teams understand the execution and refine the configuration where needed.
Save the completed workflow and use its execution logic wherever the same process is needed. Workflows can support Chat experiences, reusable templates, specialised pipelines, tool-based execution and automated tasks across IntraLLM.
A workflow is not an isolated diagram. It defines how IntraLLM processes requests behind practical AI experiences, providing a common execution structure for reusable templates, specialised pipelines, tool-enabled interactions and automated tasks.
Coordinate a sequence of execution stages when a request requires more than a single model response.
Place model reasoning or generation at the appropriate point within a wider processing flow.
Call enabled tools or functions to retrieve information, perform deterministic operations or interact with connected services.
Bring relevant knowledge into the execution path before the model produces or refines a response.
Direct information through different paths according to the workflow's configured conditions.
Use specialised pipelines for custom, extensible or more resource-intensive processing where the deployment is configured to support them.
Retrieve relevant organisational context, inject it into the request and guide the model towards a grounded answer through a defined sequence.
Coordinate model decisions with enabled functions or tools to support repeatable tasks involving data, applications or internal services.
Route inputs through extraction, transformation, retrieval or specialised pipeline stages before producing a usable output.
Structure processes that require several capabilities to work together, from initial input through routing and execution to the final response.
