Lead Routing
Interpret a request and route it with useful context.
05 / AI AUTOMATION
AI automation that reduces repetitive work without losing control
Connect triggers, data, AI, business rules and destination systems into observable workflows with approval and exception handling.
WHO IT IS FOR
For sales, support, operations and document teams moving or interpreting the same information every day.
OUR APPROACH
Define the outcome, scope and acceptance criteria before choosing technology, so the investment solves a real problem and remains maintainable.
PROBLEM
Problems to solve before adding another tool.
- People copy data between systems every day
- Work waits without clear status or approval
- Rule-only automation cannot interpret documents
- There is no traceable log or baseline
OUTCOME
What the project should make better.
- A process map with bottlenecks and risk
- Clear separation between AI judgement and business rules
- Approval, retry, fallback and notification paths
- Logs and measurement for continued improvement
USE CASES
Useful scopes shaped around the work.
Document Workflow
Receive, extract, validate and send for approval.
Customer Support
Ground draft responses and escalate sensitive cases.
Management Reporting
Combine regular inputs and prepare a sourced summary.
WORKFLOW EXAMPLE
Example: route quotation requests to the right team
Separate AI-assisted reading from business rules and approval decisions.
- 01
Receive the request
Use the agreed intake channel and identify repeat inputs.
- 02
Classify the information
Summarise the service and check required details.
- 03
Review exceptions
Send incomplete or uncertain inputs for human review.
- 04
Notify the owner
Create a draft task with defined retries and failure states.
Explore sample input and expected output
Sample input
Sample: the same quotation request arrives twice with a service and problem statement but no intended start date.
Expected output and review
One draft task with follow-up questions. If the destination fails, retain a pending state and retry under agreed rules without duplicate tasks or notifications.
An illustrative scope example. Integrations and acceptance tests follow each business’s data and permissions.
SYSTEM THINKING
A workflow that separates AI from business rules
Connections, permissions and failure states are designed alongside the interface, so a simple front end does not hide operational risk.
Inputs
- Trigger
- Input data
- Queue
System & outcomes
- Business rules
- Approval
- Action & analytics
DELIVERABLES
Deliverables that can be accepted and operated.
Workflow audit
Integration and data mapping
Automation prototype
Approval and exception handling
Monitoring and operating guide
SCOPE & INVESTMENT
Project scope and pricing inputs
Begin with one workflow that has a measurable baseline, then expand only after quality, cost and operational risk meet the agreed threshold.
- Triggers and connected systems
- Unstructured data
- Exceptions and approvals
- Volume and API usage
ACCEPTANCE & OPERATION
Acceptance and operating costs
Project acceptance criteria
- Repeated triggers do not duplicate actions or messages
- Incomplete inputs reach human review rather than invented decisions
- Test retries, failure alerts and disabling the workflow
Ongoing costs to itemise in the proposal
- Usage-based model/API and connected-tool fees
- Evaluation and knowledge-source maintenance
- Monitoring, permissions and workflow changes
Test coverage and operating responsibilities are agreed for the project before work begins.
From scope to acceptance and handover
01 /
Understand
Discuss goals, audiences and constraints to define a useful scope.
02 /
Design
Make structure and interactions clear before development begins.
03 /
Build & test
Develop the system and test critical journeys across real devices.
04 /
Launch & support
Launch, share documentation and use evidence to guide improvements.
Agree test cases, reviewers, account ownership and support before work. Test critical journeys with approved data before launch.
FAQ
Questions before the work begins.
How is AI automation different from normal automation?
Traditional automation is best for fixed rules. AI can interpret text or documents. We combine them, keeping critical conditions in explainable business rules.
Which workflow should we start with?
Choose frequent work with available inputs, measurable output and manageable risk. The first pilot should prove value without forcing organisation-wide change.
What happens when the AI is uncertain?
The workflow should stop, request more information, escalate to a person or follow an agreed fallback. It should not guess and continue with a critical action.
Does automation require replacing our existing systems?
Not always. First check authorised APIs, webhooks and supported data exchange. Separate business rules from AI-assisted work and test repeat inputs, destination failures and human handoff. Unsupported connections are identified before proposing the scope.
Have a problem worth scoping clearly?
Share the context, outcome and systems involved. We will help identify the questions to answer before investing.