Workflow automation for the steps that need judgment, not just rules.
Traditional automation handles fixed rules well but stops at unstructured input and exceptions. We engineer AI decision steps into multi-step processes — reading documents, classifying requests, choosing the next action within set limits — and keep people in the loop where it counts.
- Process map with decision points
- AI decision steps
- Orchestration
- Human review queue
- Process metrics
Where this helps
What we deliver
How it works
- 01
Walk the process
We follow real cases through the current process with the people who handle them, and record volumes, delays and errors.
- 02
Separate rules from judgment
Steps that follow fixed logic are automated with rules; only steps that need interpretation get AI.
- 03
Build and test with real cases
The workflow runs against historical cases so we can compare its decisions with what people actually did.
- 04
Launch with review
At first, people confirm AI decisions. Review is reduced step by step where the evidence supports it.
- 05
Improve from exceptions
Reviewed cases show where the AI is weak, and we adjust prompts, thresholds or rules accordingly.
Design decisions we make with you
AI or rules
If a step can be written as clear rules, it should be. Rules-based processes are covered by [Business Process Automation](/automation-data/business-process-automation).
Confidence and routing
Thresholds decide which cases proceed automatically and which go to a person, set per step and adjusted with evidence.
Workflow engine
We use the orchestration tool that suits your environment — an existing workflow platform, a durable execution engine or plain code.
Actions and autonomy
When a step takes an action in another system, it follows the permission and approval model from our [AI agents](/ai-services/ai-agents) work.
Auditability
Every AI decision is logged with its input, output, model version and any human override.
Applications
Related capabilities
- AI AgentsAgents that take approved actions across your tools, with permissions, review points and monitoring built in.
- AI IntegrationConnect AI models and assistants to your CRM, ERP, databases, knowledge stores and APIs — with identity, logging and fallbacks that production systems need.
- NLP & Conversational AIThe language layer underneath chat, voice and document systems — understanding, extraction, classification, dialogue management and multilingual support.
- MLOps & LLMOpsEvaluation, release, monitoring and cost control for predictive models and LLM applications once they are in production.
Questions buyers ask
BPA automates fixed rules — move this record, send that notification. AI workflow automation adds steps that need interpretation, such as reading a free-text request or deciding which category a document belongs to. Most real workflows combine both.
Not quite. A workflow follows a defined path with AI at specific steps; an agent plans its own steps toward a goal. We choose the simpler design when it does the job. Read more in agent vs. copilot.
Yes. We add AI steps to the tools you already run where that is practical, and only introduce a new orchestration engine when the process needs it.
We record a baseline before launch and track automated rates, review outcomes, cycle times and errors afterward, so improvement is measured, not assumed.
Discuss this capability with an engineer.
Tell us about the workflow or product. We reply with questions, a suggested first step and who would work on it.