Portfolio & Case Studies
Proof from systems that had to work after launch.
Four case studies across data lake migration, AI workflow automation, real-time analytics, and enterprise upskilling. Each one now shows the operating friction, technical decision, adoption decision, and commercial meaning behind the measurable result.
Proof metrics
Enterprise Data Lake Migration
reducing query times by 400%
Enterprise logistics environment
Agentic Workflow Automation
achieving a 78% resolution rate without human intervention
Technical support operation
Real-time Analytics Dashboard
reducing downtime by 22%
Manufacturing operations team
Enterprise AI Upskilling Program
successfully certifying 50+ data engineers
Enterprise data engineering cohort
Case study library
Problem, friction, decision, adoption, outcome
Client names remain anonymized where permission does not exist, but the case structure is more specific about the starting condition and why the result mattered.
Enterprise logistics environment
Enterprise Data Lake Migration
Reporting teams were repeating query logic across siloed sources, so leaders could not get fast, consistent answers.
Problem
Legacy on-premise silos limited reporting speed, data access, and the ability to support real-time BI use cases.
Technical decision
Move the reporting foundation into Azure Data Lake with cleaner source-to-reporting flows and reusable BI access patterns.
Adoption decision
Keep the business vocabulary visible in the model so downstream teams could inspect and reuse the structure.
Stack
Outcomes
- Enabled real-time BI reporting
- Unified fragmented enterprise data sources
- Reduced query times by 400%
reducing query times by 400%
Faster query performance turned reporting from a bottleneck into an operational decision layer.
Technical support operation
Agentic Workflow Automation
Support queues were absorbing predictable Level 1 requests that did not need senior escalation.
Problem
Support teams needed a more reliable way to parse, categorize, and respond to high-volume Level 1 technical support tickets.
Technical decision
Use AI agents for parsing, classification, response drafting, and business-rule routing.
Adoption decision
Preserve human review for ambiguous or escalation-level tickets instead of forcing false autonomy.
Stack
Outcomes
- Reduced repetitive triage load
- Improved response consistency
- Achieved a 78% resolution rate without human intervention
achieving a 78% resolution rate without human intervention
The support team recovered capacity while keeping accountability inside the workflow.
Manufacturing operations team
Real-time Analytics Dashboard
Operators were reacting to equipment issues after the fact because telemetry was not visible in a decision-ready format.
Problem
Manufacturing stakeholders needed a high-performance Power BI suite for streaming telemetry from IoT devices.
Technical decision
Build a real-time Power BI layer on streaming IoT telemetry for predictive maintenance signals.
Adoption decision
Translate telemetry into operational views that maintenance teams could use without reading raw device data.
Stack
Outcomes
- Surfaced operating signals in real time
- Supported predictive maintenance decisions
- Reduced downtime by 22%
reducing downtime by 22%
Reducing downtime protected production capacity and made maintenance planning more proactive.
Enterprise data engineering cohort
Enterprise AI Upskilling Program
Teams needed advanced AI and cloud architecture capability that would last beyond a one-off workshop.
Problem
Data engineering teams needed structured training across advanced cloud architecture and cognitive services integration.
Technical decision
Design a 12-week MCT-led curriculum around cloud architecture, cognitive services, and applied delivery.
Adoption decision
Structure the programme as repeatable capability building, not certification coverage alone.
Stack
Outcomes
- Scaled applied AI capability across teams
- Created a repeatable learning path
- Successfully certified 50+ data engineers
successfully certifying 50+ data engineers
The organisation grew internal delivery capacity instead of depending only on external specialists.
Proof to action
If any of these scenarios match a current challenge, start with a clarity call.
Bring the scenario, current constraints, tools in scope, team size, urgency, and the evidence already available. The next step is a scoped conversation, not a long discovery form.