AI
AI prepares recurring information work while rules and people remain in control.
Salytiq combines classification, extraction, context rules, drafting and human approval in a controlled workflow.
Salytiq combines classification, extraction, context rules, drafting and human approval in a controlled workflow.
From intake to outcome
- 01Bound cases, risks and the permitted AI task
- 02Structure examples, rules and expected outputs
- 03Implement the AI step with validation and review
- 04Test quality, exceptions and human decisions
Business outcomes
- Less reading and transfer effort
- Consistently prepared decisions
- Human control before external actions
Typical deliverables
- Classification and extraction logic
- Context and validation rules
- Review interface or approval step
- Test cases, limitations and operational documentation
When the solution fits
- Similar enquiries are manually sorted every day
- Draft responses follow recurring rules
- AI should support an existing process rather than become a chatbot
Scope boundaries and prerequisites
Clear boundaries
- No autonomous external action without agreed control
- Quality depends on examples, context and clear boundaries
Required foundations
- Approved or synthetic example cases
- Named review role and quality criteria
Interactive proof
Open interactive demoAI Workflow Automation
Three cases with human-in-the-loop.
How an engagement can work
- 01
01 Understand
Capture the workflow, effort, systems and visible pain.
- 02
02 Prioritise
Align impact, feasibility and economic value.
- 03
03 Implement
Build, test and clearly hand over one bounded workflow.
- 04
04 Operate
Monitor and improve within an agreed scope.
Frequently asked questions
What does AI Workflow Automation include?
Salytiq combines classification, extraction, context rules, drafting and human approval in a controlled workflow. Typical deliverables include: Classification and extraction logic; Context and validation rules; Review interface or approval step; Test cases, limitations and operational documentation.
What information is needed to get started?
A robust initial scope requires: Approved or synthetic example cases; Named review role and quality criteria.
When is this solution a good fit?
A useful starting point is especially likely when: Similar enquiries are manually sorted every day; Draft responses follow recurring rules; AI should support an existing process rather than become a chatbot.
Which boundaries are clarified before implementation?
Transparent scope boundaries are part of the work: No autonomous external action without agreed control; Quality depends on examples, context and clear boundaries.
How can an engagement begin?
We first understand the workflow and prioritise one clearly bounded step. Where the starting point remains unclear, an Automation Audit can prepare the right implementation.
Related solutions
Salytiq
Discuss a projectDoes this solution fit your workflow?
Share the starting point, systems involved and frequency. Salytiq will frame a realistic next step.