AI in Workflows
Build with AI. Run with Kinetic.
AI accelerates workflow creation and participates as intelligent steps within them. But execution stays deterministic — repeatable, auditable, and governed.
The approach
The right role for AI
AI participates in two ways within the Kinetic Platform.
At design-time, AI helps teams generate workflows, define logic, and accelerate configuration — getting from idea to working process faster.
At runtime, AI operates as a targeted workflow step — classifying incoming requests, extracting data from documents, recommending actions, and summarizing information. The workflow engine handles everything else deterministically.
This means organizations get the benefits of AI where it creates genuine value, without sacrificing the reliability, auditability, and cost efficiency of deterministic workflow execution.
Cost
AI tokens are expensive. Repeatable workflows are cheap. Don't burn AI compute on work that follows the same steps every time. Reserve AI for the tasks where its reasoning capability genuinely changes the outcome.
Auditability
AI instructions and results must be traceable. Workflow execution is auditable by default. In regulated environments — government, healthcare, financial services — this is not optional. Every AI call needs a record, and every action taken on that output needs a trail.
Reliability
Repeatable processes need deterministic execution, not probabilistic reasoning. AI is powerful for interpretation and recommendation. But when the same input must always produce the same output, a deterministic workflow engine is the right tool.
AI capabilities within workflows
Use AI where it creates value. Let deterministic workflow execution handle repeatable steps — governed, auditable, and cost-efficient.
Classification & Routing
Use AI to classify incoming requests, tickets, or cases based on content, then hand off to deterministic workflow logic for routing and execution. AI handles the interpretation. The workflow handles the action.
Summarization & Extraction
Summarize long-form inputs, extract key data from unstructured text, and populate structured fields automatically. AI does the heavy lifting on ambiguous inputs so downstream workflow steps operate on clean, structured data.
Decision Support
AI models recommend actions, flag anomalies, or score requests within a workflow. Human reviewers see AI recommendations alongside the data that informed them, then approve, modify, or reject before the workflow proceeds. AI advises. People decide. Workflows execute.
Bring Your Own Model
Connect to OpenAI, Azure OpenAI, AWS Bedrock, or any AI model accessible via API. Use your organization's preferred models and hosting arrangements. Kinetic orchestrates the workflow; you choose the AI.
How it works
Use AI where it creates value. Let workflows handle the rest.
AI also accelerates the design process itself. Teams can use AI tools to generate workflow structures, define conditional logic, and configure integration steps — reducing the time from requirement to working workflow.
An AI-assisted workflow in Kinetic works like any other workflow — with AI model calls as targeted steps where interpretation or reasoning is needed. A user submits a request through the experience layer. The workflow engine processes it, calling an AI model at the appropriate step to classify, summarize, or recommend an action. The AI output is then consumed by subsequent deterministic workflow steps: routing to the right team, pre-populating fulfillment fields, or presenting recommendations to a human reviewer for approval.
AI interacts with the workflow. It does not replace it. This distinction matters for cost, for reliability, and for the audit trail. Every AI model call is logged — including the input sent, the output received, and every workflow action taken downstream. Approval gates can require human review of AI recommendations before action is taken. Role-based access controls determine which workflows can invoke which AI models. Exception handling manages cases where AI outputs are uncertain or fall outside defined confidence thresholds.
The result: AI where it earns its cost. Deterministic execution everywhere else. A complete audit trail throughout.
Frequently asked questions
Yes. AI tools can assist at design-time by generating workflow structures, defining logic, and accelerating configuration. This is separate from AI's runtime role as a workflow step. Kinetic supports both uses.
Kinetic is model-agnostic. You bring your own AI models — OpenAI, Azure OpenAI, AWS Bedrock, self-hosted models, or any model accessible via API. Kinetic provides the orchestration layer that embeds AI into governed workflows. You choose the AI provider that meets your requirements.
Yes. AI-assisted workflows use the same approval mechanisms as any other Kinetic workflow. You can configure approval gates that require human review of AI-generated classifications, recommendations, or actions before the workflow proceeds. This is especially important in regulated environments where AI outputs must be validated before execution.
Every AI model call within a workflow is recorded in the platform's audit trail — including the input sent to the model, the output received, and any subsequent workflow actions taken based on that output. This provides full traceability for compliance, governance, and incident investigation.
Yes, if your AI model is hosted within the same network boundary. Kinetic can call locally hosted or on-premise AI models the same way it calls cloud-hosted models. In air-gapped deployments, you deploy your AI infrastructure alongside Kinetic and configure the integration accordingly.
See AI in workflows
Get a demo showing how Kinetic uses AI to accelerate workflow creation and embed intelligence into governed, cross-system workflows.