AI Automations
Practical AI and workflow automation that removes repetitive work, connected to your systems and governed for security.
Overview
We automate repetitive work with AI and workflow tools, with security, governance and measurable time saved.
- TriggersEmail, form, ticket or schedule events start each workflow.
- OrchestrationPower Automate and Copilot Studio call connectors, REST APIs and Azure OpenAI models.
- IntegrationResults are written back to Microsoft 365, CRM and ticketing systems.
- GovernanceRole-based access, step-level logging and human approval for sensitive actions.
Technical scope
- Process discovery and automation candidates
- Workflow automation with Power Automate
- AI assistants and Copilot extensions
- Document processing and data extraction
- Ticket triage and security-alert enrichment
- Integration with Microsoft 365, CRM and ticketing APIs
- Guardrails: access control, logging and human approval
Architecture and how it works
A trigger such as an email, form, ticket or schedule starts a workflow. The workflow calls connectors and AI models, writes results back to business systems and logs every step. Sensitive actions need human approval.
Delivery process
- 01
Prioritise
Identify and rank use cases
- 02
Design
Design the workflow and data access
- 03
Build
Build and test
- 04
Deploy
Deploy with monitoring
- 05
Improve
Measure results and iterate
Tools and deliverables
Each automation is documented with its trigger, data sources, connectors, approval steps and error handling. Flows are tested against sample data before release, run with least-privilege service accounts, and log every run so outcomes and time saved can be measured.
Deliverables
- Use-case backlog with estimated savings
- Workflow designs
- Test results
- Run and support guide
- Savings report
Common questions
How are AI workflows triggered and connected to our systems?
Workflows start from an email, form, ticket or schedule and call connectors and REST APIs in Microsoft 365, CRM and ticketing systems. AI steps run through Azure OpenAI or Copilot Studio, and the results are written back to the system of record, so every outcome is stored where your teams already work and can be audited later.
How do you stop an automation from taking unsafe actions?
Guardrails include access control with least-privilege service accounts, step-level logging and human approval before sensitive actions run. Flows are tested against sample data before release and use defined error paths, so a failed step stops safely and is flagged for review instead of continuing with bad data.
What data can the AI models access?
Each workflow is scoped to the data sources and connectors it needs, and that access is reviewed before go-live. Copilot extensions respect existing Microsoft 365 permissions, so a data-access review comes first to find overshared sites and files, and those are fixed before any AI assistant can surface them.
How do you decide which processes to automate first?
Process discovery scores each candidate on volume, rule complexity, data quality and risk. Rule-based, high-volume work such as ticket triage, document extraction and security-alert enrichment usually ranks highest, and each automation is logged so time saved can be measured against the baseline set at the start.