Impact
Case studies: measurable results from data and AI
How analytics and AI engineering solutions turn business data into better forecasts, faster operations and lower costs.
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%
more predictable revenue
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higher factory output
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%
of routine tickets deflected
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less manual data entry
AI & MLOps
Machine learning and AI engineering solutions that predict, automate and assist.
Predictive analytics
CASE STUDY 01
Sales forecasting
30%improvement in revenue predictability
- Problem
- Organisations frequently miss revenue targets or misalign operational resources because they rely on static historical averages rather than dynamic data, leading to wasted capital and lost growth opportunities.
- Solution
- Machine learning models analyse historical transaction data, seasonality, sales pipeline metrics and broader market trends to predict future sales volumes with high precision.
- Return on investment
- Improves revenue predictability by up to 30%, optimises supply chain and resource planning, and increases profit margins by aligning operations directly with forecast sales.
Conversational AI
CASE STUDY 02
Intelligent customer support chatbot
60%of routine support tickets deflected
<10saverage customer wait time
- Problem
- High volumes of routine customer enquiries lead to long wait times, frustrated customers and rising support staffing costs.
- Solution
- An AI chatbot connected to internal knowledge bases through retrieval-augmented generation (RAG) resolves first-line queries 24/7 and routes complex issues to human agents.
- Return on investment
- Deflects up to 60% of routine support tickets, reduces average customer wait time to under 10 seconds, and lowers customer service operating costs.
Applied AI
CASE STUDY 03
Intelligent document processing
80%less manual data entry time
98%+data extraction accuracy
- Problem
- Manual data extraction from unstructured documents such as invoices, contracts and vendor forms is error-prone, labour-intensive and delays core operational workflows.
- Solution
- An automated pipeline uses computer vision and large language models (LLMs) to ingest, classify and extract key data points directly into the company's ERP or CRM.
- Return on investment
- Reduces manual data entry time by 80%, improves extraction accuracy to over 98%, and cuts document processing time from days to minutes.
Data engineering
Reliable pipelines that move data on time, checked at every step.
Fintech · Risk
CASE STUDY 04
Fraud and risk monitoring
- Problem
- Suspicious transactions were identified late, only after manual review, increasing financial and regulatory exposure.
- Solution
- A near real-time data pipeline applies anomaly rules to incoming transactions and feeds alert dashboards for the risk team.
- Return on investment
- Suspicious activity is flagged as it happens, so the risk team can investigate and act faster.
Dynamics 365
Finance and operations processes running on Microsoft Dynamics 365.
Fintech · Payments
CASE STUDY 05
Payments and reconciliation automation
- Problem
- Thousands of daily payment transactions were reconciled by hand in spreadsheets, delaying month-end close and creating errors.
- Solution
- Automated matching between the payment gateway, bank statements and the Dynamics 365 general ledger, with exceptions routed to the finance team.
- Return on investment
- Reconciliation runs automatically every day, and the finance team only reviews exceptions.
Data warehousing
Governed data platforms that give the business one trusted version of its numbers.
Fintech · Data platform
CASE STUDY 06
Finance data lakehouse
- Problem
- Finance, CRM and product data sat in separate systems, producing conflicting numbers in management reports.
- Solution
- A medallion lakehouse on Microsoft Fabric with a single finance semantic model shared by every report.
- Return on investment
- One trusted version of the numbers for finance, management and board reporting.
Fintech · Lending
CASE STUDY 07
Lending and credit analytics
- Problem
- Loan book data was spread across several systems, with no single view of portfolio risk.
- Solution
- A lakehouse with a gold-layer loan model and Power BI risk dashboards refreshed on a schedule.
- Return on investment
- Credit and risk teams see the whole portfolio in one place and can act on emerging risk sooner.
Reports & dashboards
Dashboards and reporting that turn data into clear decisions.
Descriptive analytics
CASE STUDY 08
Equipment uptime dashboards
15%potential improvement in overall factory output
- Problem
- Plant managers lack real-time visibility into machine performance and historical downtime, making it difficult to identify production bottlenecks and allocate maintenance resources effectively.
- Solution
- Interactive dashboards aggregate historical sensor and maintenance log data to visualise overall equipment effectiveness (OEE), breakdown frequency and total repair costs.
- Return on investment
- Increases operational transparency, identifies the root causes of past inefficiencies, and enables data-driven decisions that can improve overall factory output by 15%.
Fintech · Compliance
CASE STUDY 09
Regulatory reporting
- Problem
- Regulatory returns were compiled manually every period under tight deadlines, with limited audit trail.
- Solution
- A governed data pipeline produces regulatory returns directly from trusted, reconciled data tables.
- Return on investment
- Faster, repeatable reporting with a clear audit trail from source data to submitted return.
Results vary by organisation, data quality and scope.
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