Engineering for your business
Process automation and AI
Automate repetitive tasks with clear rules, data integration and human oversight. AI can support classification and response preparation when appropriate; critical decisions still require validation.
Problems I can address
- Copying the same information between tools every day.
- Document and message triage taking time from core work.
What the scope can include
- Task, exception and human-review mapping.
- Automated workflows with logs and failure handling.
- Integrations with input and output validation.
- Documentation of costs, limits and recovery procedures.
When to consider it
For operations with recurring tasks, predictable inputs and measurable time or rework.
Possible applications
Potential scope, not completed-project claims:
- Transfer records between tools with field validation.
- Triage documents by rules and route exceptions to review.
- Prepare AI-assisted response suggestions for human approval before sending.
What affects the estimate
Task frequency, input variety, exceptions, APIs and review requirements define effort. Model usage has its own costs and limits. We assess current time, errors and operating cost before recommending automation; savings cannot be assumed without measurement.
Limits and decisions
AI models can make mistakes and APIs can incur costs. Deterministic rules come first when sufficient; sensitive data needs prior assessment.
The deliverables on this page guide the proposal. Integrations, migration, support, infrastructure and responsibilities are confirmed in the agreed scope.
Automation should know when to stop
Map inputs, decisions and exceptions. Explicit rules may solve the task without AI; a generative model needs sources, tests, supervision and action boundaries. I can build triage, recording and human handoff with limited permissions. Without authorised sources or a review owner, establish those conditions first. No staffing, profit or time-saving promise replaces observation of the implemented process.
How the work progresses
- Observe the recurring task and establish an operating baseline.
- Separate predictable rules from cases needing human judgement.
- Implement validation, logs and recovery paths.
- Compare assisted and manual execution before expanding use.
FAQ
Before we begin.
Does every automation need AI?
No. Rules and APIs are preferable for predictable tasks. AI may support language or classification after assessing errors and supervision.
What happens when the model is wrong?
The workflow should validate outputs and route exceptions to review. The proposal defines automatic actions and those requiring approval.
Can sensitive data be sent to a model?
That requires prior assessment of necessity, permissions and the provider. Scope may require anonymisation, minimisation or avoiding AI in that step.