Outcomes you get
- One agreed metric definition layer feeding every report
- Automated refreshes replacing manual spreadsheet assembly
- Predictive and anomaly signals surfaced where decisions are made
Solution
AI Analytics
Turn operational data into decisions with rigorous analysis, dashboards and predictive models.
Conceptual visualization only
The problem we solve
Reporting is manual, numbers disagree between teams, and nobody trusts the dashboard enough to act on it.
What we deliver
- Data audit with quality and completeness findings
- KPI dictionary and metric definitions
- Validated data model and refresh schedule
- Dashboards with drill-down and export
- Anomaly and threshold alerting
A good fit if
- Operations teams reporting from spreadsheets
- Leadership needing a single source of truth
- Businesses with data spread across disconnected systems
How This AI Agent Works
The published process for this agent: what triggers it, what it validates, which approved tools it calls, where a human approves and what it delivers.
Dataset profiling and analysis workflow
From an uploaded dataset to validated charts and a dashboard-ready report.
Workflow preview
Saved with the run so the recorded execution trail shows what was asked. Demonstration runs use sample data. They do not touch your systems.
Public visitors can preview the structure and inspect every node. Labelled demonstrations and live runs require a signed-in account with configured providers.
72%
Workflow summary as a text list
- 1. Upload dataset: Input. CSV, Excel or table extract uploaded to the workspace.
- 2. Validate schema: Validation. Check columns, types, keys and row counts.
- 3. Profile data: Approved tool. Compute distributions, nulls, duplicates and outliers.
- 4. Clean approved issues only: Decision. Fixes outside the approved rule set are proposed, not applied.
- 5. Analyse: AI agent. Answer the stated question and record the method.
- 6. Generate charts: Approved tool. Pick the chart type that matches each finding.
- 7. Validate metrics: Validation. Recompute headline metrics before publishing.
- 8. Dashboard and report: Output. Quality report, charts and cleaned dataset export.
Approval gate: none configured. Deliverable: Dashboard.
Workflow audit for this page
Structure only. Run a demonstration to include events.
| Template ID | Nodes | Approval node | Output type | Preview | Demo | Live | Mobile test |
|---|---|---|---|---|---|---|---|
| data-quality-agent | 8 | No | Dashboard | Available | Available (signed in) | Available via Studio · requires ai_provider | Verified at 390px |
Demonstration runs are stored and replayed from recorded events. Live runs execute server-side in the Studio and only start when the listed integrations report healthy.
Capabilities
Delivered as consulting, build, automation or managed service.
Data collection and validation
Exploratory data analysis
Business intelligence
KPI design
Statistical analysis
Predictive analytics
Power BI dashboards
Automated reporting
Anomaly detection
Customer and market analysis
Operational analytics
Decision-support systems
How an engagement runs
Phase 1 · 1 week
Discover
Source inventory, decision questions, metric owners.
Phase 2 · 1–2 weeks
Design
Metric dictionary, data model, dashboard wireframes.
Phase 3 · 3–6 weeks
Build
Pipelines, validation rules, dashboards, alerting.
Phase 4 · Ongoing
Deploy & improve
Adoption support, new metrics, forecast models.
Common questions
Do we need a data warehouse first?
Not always. We can start against existing databases and introduce a warehouse only when volume or history justifies it.
Which BI tool do you use?
Power BI most often, but we deliver into whichever tool your organisation already licenses.
Ready to scope your AI initiative?
Share your objective and constraints. A specialist reviews every request before any recommendation is issued.