Clear, owned outputs.
- Process map and automation design
- Automated workflow
- Exception review interface
- Integration with systems of record
- Performance dashboard
Many processes involve reading something, deciding what it is, pulling out details, updating a system and telling someone. AI automation handles those steps by combining AI — classification, extraction, generation — with rules, integrations and human review for exceptions.
The problems AI Automation solves.
AI-assisted workflows combining classification, extraction and rules with human review.
Staff spend hours on repetitive reading, sorting and data entry.
Traditional automation fails on unstructured inputs like emails.
Backlogs build up during busy periods.
Errors creep in through manual copying between systems.
What AI Automation covers.
Process discovery
Mapping steps, volumes, exceptions and time spent.
AI steps
Classifying, extracting, summarizing and drafting within workflows.
Workflow orchestration
Connecting steps across systems with error handling.
Exception handling
Routing uncertain cases to people with context.
Integration
APIs and connectors to your business systems.
Measurement
Throughput, accuracy, time saved and exception rates.
Automating a process with AI in the loop.
Most business processes mix rule-based steps with judgement. We automate the rules with ordinary software and use AI only for the judgement steps.
- 01
Map the process
Every step, decision point, system and exception, from the trigger to the outcome.
- 02
Split rules from judgement
Fixed rules become workflow automation; reading, classifying and drafting become AI steps.
- 03
Design the review points
Where a person checks AI output, and how their corrections are captured.
- 04
Build and integrate
Workflow tools such as Power Automate, Logic Apps or n8n, connected to your systems through APIs.
- 05
Measure
Time per case, error rate and exception volume compared with the manual baseline.
Why it's worth it.
Hours returned
People spend time on judgement, not copying.
Consistent quality
Fewer manual errors.
Scalable capacity
Handle peaks without a backlog.
When AI Automation is the right move.
- Processing inbound emails and routing requests.
- Updating CRM records from meeting notes.
- Preparing standard documents from structured data.
Technologies & platforms
- Power Automate
- Azure Logic Apps
- n8n
- Make
- Zapier
- UiPath
- Azure OpenAI
- Python
How AI Automation work runs.
- 01
Scope
The task, users, content sources, risks and how success will be judged.
- 02
Prototype
A working prototype on real examples, reviewed by the people who will use it.
- 03
Evaluate
Test sets for accuracy, safety and edge cases, with human review.
- 04
Harden
Security, permissions, guardrails, logging and cost controls.
- 05
Launch and improve
Phased rollout, feedback loops and regular evaluation.
AI Automation questions.
Will AI automation replace staff?
It's usually used to remove repetitive steps so people can focus on work that needs judgement. How it affects roles is a decision for your organization, and we plan the change with you.
What if the AI gets something wrong?
Confidence thresholds send uncertain cases to people, and results are monitored. Critical steps can always require human approval.
Can we use our existing automation tools?
Yes. AI steps can be added to Power Automate, UiPath, n8n and similar platforms.
Talk to us about ai automation.
Tell us which decision or process you want to improve and what data you have. We'll reply with whether it's a good fit and how we would test it.