Process Automation
A staged build on the data you already have: connected sources and validation rules, a searchable knowledge system, management dashboards, AI-assisted analysis, and workflow automation with defined approval steps
What it means
One engagement, implemented in stages: data and knowledge first, then reporting, then decision support, then automation, all integrated with the systems you already use. Each working component is demonstrated and validated as it is completed, rather than only at final delivery.
Already have a clear spec from your own analysis or another team's audit? We can start the build directly from it. When the current data and process landscape has not yet been documented, we recommend a Business Audit first.
Four stages of one service
Connected data and a knowledge system
We connect your data sources, agree the data model, and set the validation rules that catch duplicates, mismatched SKU names, and broken reference data. Documents, exports, and written procedures are indexed into a searchable knowledge system.
Management dashboards
Defined metrics for sales, stock, margin, and plan-fact, refreshed on a set schedule, so finance and operations read the same numbers instead of reconciling separate exports.
AI-assisted analysis and decision support
An interface for querying the consolidated data, explaining variances, and preparing summaries, plus rules that flag material deviations for review. The analysis is generated; the decision stays with your managers.
Controlled workflow automation
System integrations and explicit rules handle the deterministic steps — routing, reporting, status updates. Actions with material business impact require approval from an authorized employee, and every run is logged.
These are stages of one build, scoped to what the audit found, or to the spec you bring. See what these look like in practice →
What you receive
The scope of any single engagement is set by your roadmapThese are the deliverables it draws from
- Connected data sources, with the refresh schedule documented per source.
- A data model and the validation rules applied to it.
- A searchable knowledge system over your documents and procedures.
- Management dashboards for the metrics you define.
- An AI-assisted interface for querying data and drafting summaries.
- Workflow rules, approval steps, audit logs, and error handling.
Governance & human review
System integrations and explicit rules handle the deterministic operations. AI handles unstructured work: classification, extraction, search, and drafting. Actions with material business impact require approval from an authorized employee. We scope data access deliberately, only as far as the work needs.
Expected outcomes
- Management reporting that is current, validated, and in one place.
- Operational knowledge searchable instead of held by individuals only.
- Faster decisions because the required data arrives validated and on schedule.
- Repetitive reporting handled automatically, with defined approval steps.
The build starts from a documented process
Book the audit and we'll come back with the staged plan and the scope of the first buildAlready documented it yourself?Bring your spec and we'll start from it