A prominent consumer and market research firm serving multinational corporate clients across diverse industries leveraged Amazon Bedrock foundation models to automate multilingual survey and sentiment analysis, reducing analyst effort and delivering faster executive-ready insights.

The Challenge

The customer sought to modernise their analytics platform by introducing AI-driven insight generation to reduce manual analyst effort and improve real-time market responsiveness. Key challenges included:

• Heavy reliance on manual processes to interpret qualitative survey responses.
• Inability to provide real-time sentiment and trend analysis from online and social data.
• Difficulty in synthesising multilingual feedback across global markets.

Our Solution

Rambunct deployed Amazon Bedrock foundation models to power a scalable AI-driven analysis layer, enabling rapid, multilingual, and automated research insights.

• AI-Generated Insights: Bedrock foundation models fine-tuned on historical reports to create concise executive summaries.
• Real-Time Social Listening: Integration with social data streams to deliver near real-time sentiment and trend detection.
• Multilingual Analysis: Automated translation and interpretation of feedback across 15+ languages.
• Interactive Research Assistant: A natural language query interface that allowed analysts to ask market questions and receive narrative, data-backed responses.

The Result

The solution significantly improved research efficiency, reduced operational overhead, and enhanced client engagement.

• 70% faster turnaround for client insight reports.
• ~1,500 analyst hours saved annually through automated summarisation.
• 40% lower analytical TCO compared to manual processes.
• 22% increase in client re-engagement rates.

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A gaming data-driven organisation needed a reliable and scalable ETL solution to ingest, transform, and load large volumes of customer and transaction data into Amazon Redshift. By implementing AWS Glue jobs integrated with Redshift, the company automated data pipelines, reduced operational overhead, and enabled faster business insights at scale.

The Challenge

The data company faced several challenges with its existing ETL processes:

• Manual data transformation scripts led to inconsistent data quality.
• High operational overhead in maintaining on-premises ETL jobs.
• Difficulty scaling pipelines as data volumes grew.
• Limited visibility into job performance and error handling.
• Slow data availability in Redshift impacted downstream reporting and analytics.

Our Solution

AWS Glue was selected to modernise the ETL framework.

• AWS Glue Jobs to process raw data from Amazon S3 and transactional sources, applying business rules and cleaning before loading.
• Glue Crawlers to automate schema discovery and catalogue metadata in the AWS Glue Data Catalog.
• Integration with Amazon Redshift using Glue’s native connectors to efficiently load transformed datasets.
• Job Monitoring & Orchestration with AWS Glue Studio and Amazon CloudWatch for visibility and alerts.
• Scalability & Serverless Architecture ensured data pipelines scaled automatically with data volume without infrastructure management.

The Result

Automation: Reduced manual ETL maintenance by 70%, freeing up engineering resources.

• Faster Insights: Data availability in Redshift improved from hours to near real-time (sub-15 minutes).
• Data Quality: Implemented data validation rules in Glue, leading to a 95% reduction in downstream data errors.
• Cost Optimisation: ETL costs decreased by 40% through Glue’s serverless pay-per-use model compared to legacy on-premises ETL.
• Scalability: Seamlessly handled peak data loads during month-end processing with no downtime.

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A leading provider of digital solutions for customs clearance and freight management engaged Rambunct to modernise its virtual desktop infrastructure. Leveraging Amazon WorkSpaces, Rambunct delivered a secure, scalable, and cost-efficient cloud environment that accelerated seasonal staff onboarding, enabled remote contractor access, and ensured ISO 27001 and regional compliance.

The Challenge

The client required a secure, scalable virtual desktop environment to support a globally distributed workforce while meeting strict security and compliance needs.

• Existing on-premises VDI was expensive to maintain and lacked flexibility for seasonal or remote staff onboarding.
• Workforce spanned Australia and offshore centres, requiring consistent desktop experiences for consultants, analysts, and customer service agents.
• Needed to process sensitive financial and legal data in line with ISO 27001 and regional data protection regulations.

Our Solution

Rambunct implemented Amazon WorkSpaces to deliver secure, cloud-based virtual desktops accessible from anywhere for the client.

• Provisioned persistent Windows 10 desktops in AWS, accessible from laptops, thin clients, or tablets.
• Integrated with AWS Directory Service for Microsoft Active Directory for centralised authentication.
• Enforced secure, encrypted access via Amazon VPC and AWS VPN.
• Configured Amazon CloudWatch for monitoring, proactive scaling, and cost optimisation.

The Result

The solution delivered measurable improvements in efficiency, cost savings, security, and workforce flexibility.

• Cut onboarding time from 5 days to under 1 hour for new hires and contractors.
• Achieved 30% cost savings over the previous on-premises VDI with pay-as-you-go pricing and right-sizing.
• Enhanced security and compliance by storing all data on AWS with MFA enabled.
• Enabled rapid scaling for seasonal projects and provided consistent, high-performance desktops from any location.

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A leading digital customer service platform leveraged Amazon Bedrock to enhance chat and voice support with multilingual, context-aware conversational AI, reducing operational costs while improving customer satisfaction.

The Challenge

The customer sought to enhance its digital customer service platform to deliver faster, more accurate, and cost-efficient support.

• Improve response accuracy across high-volume customer interactions.
• Scale multilingual support to serve a global user base effectively.
• Deliver more human-like conversational experiences without escalating costs or adding human agents.

Our Solution

Rambunct implemented a secure, AWS-powered generative AI solution to enable multilingual, context-aware conversational assistants across chat and voice channels.

• Deployed Amazon Bedrock foundation models (Anthropic Claude, Amazon Titan) for multi-turn understanding and response generation.
• Integrated AWS Lambda to fetch real-time customer interaction context.
• Used Amazon CloudWatch for monitoring, logging, and operational optimisation.
• Secured the environment with Amazon VPC, AWS IAM, and AWS KMS encryption to meet compliance requirements.

The Result

The solution delivered measurable improvements in customer experience, efficiency, and cost optimisation.

• 18% increase in customer satisfaction (CSAT) scores.
• 45% reduction in human agent handoffs, driving higher self-service containment.
• 60% faster response times for common support queries.
• 35% overall savings in operational costs compared to the previous AI infrastructure.

The post Transforming Digital Customer Engagement Using Generative AI on AWS appeared first on Rambunct Consulting.

A logistics technology provider required a reliable and automated ETL framework to process shipping and transactional data for its e-commerce partners. By leveraging AWS Glue jobs integrated with Amazon Aurora PostgreSQL, the company has modernised its data integration pipelines, reduced operational overhead, and accelerated delivery of analytics-ready data.

The Challenge

The company faced significant challenges with its legacy ETL approach:

• Manual scripts created inconsistencies in data quality.
• ETL pipelines lacked automation and monitoring, leading to delays.
• Scaling was difficult as shipping volumes fluctuated heavily.
• Analysts had limited access to trusted data in near real-time.
• High maintenance costs for infrastructure running data pipelines.

Our Solution

AWS Glue was selected to modernise the ETL framework.

• AWS Glue Jobs: Transformed raw shipping and partner data stored in Amazon S3 and ingested into Aurora.
• Glue Crawlers: Automated schema discovery and maintained metadata in the AWS Glue Data Catalog.
• Aurora PostgreSQL Integration: Used Glue’s JDBC connectors to load transformed datasets directly into Aurora tables.
• Job Orchestration & Monitoring: AWS Glue Studio provided job workflows while Amazon CloudWatch enabled performance monitoring and alerts.
• Scalable Serverless Processing: Glue automatically scaled with workload spikes during peak shipping seasons.

The Result

Automation: Replaced manual ETL scripts with automated Glue jobs, reducing manual intervention by 75%.

• Faster Data Availability: Reduced ETL cycle time from 4 hours to under 30 minutes.
• Improved Data Quality: Standardised validation and enrichment rules applied in Glue led to a 90% reduction in data inconsistencies.
• Scalability: Seamlessly handled seasonal order spikes without additional infrastructure.
• Business Enablement: Analysts gained near real-time access to high-quality operational data for logistics optimisation.

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A leading global software development and consulting firm partnered with Rambunct to adopt Amazon WorkSpaces for secure, on-demand cloud development environments across its worldwide delivery centres. This transformation replaced physical desktops, streamlined provisioning, improved version control, ensured data security compliance, enabled rapid scaling, reduced hardware costs, and unified operations across teams.

The Challenge

The client needed a secure, scalable development environment to replace physical desktops for its globally distributed teams.

• Teams worked on sensitive projects requiring strict data security, version control, and controlled access.
• Physical desktops and laptops created challenges in procurement, security, and environment consistency across geographies.
• Scaling for short-term or project-based staffing was slow and costly.

Our Solution

Amazon WorkSpaces was implemented by Rambunct to deliver secure, standardised, cloud-based virtual desktops for all of the client’s development teams.

• Provided persistent Windows or Linux WorkSpaces with pre-configured tools, VPN clients, and productivity apps.
• Integrated AWS Directory Service for centralised authentication and access control.
• Enabled secure collaboration with Amazon WorkDocs and monitored performance with Amazon CloudWatch.
• Right-sized WorkSpaces by user profile and automated provisioning for rapid onboarding and offboarding.

The Result

The solution significantly improved onboarding speed, security, cost efficiency, and scalability.

• Reduced environment setup from 3–4 days to under 4 hours (60% faster onboarding).
• Improved security by ensuring client data and code remained within AWS and not on local devices.
• Achieved 30% cost savings over physical machines, especially for short-term contracts.
• Enabled rapid scaling to match project requirements while maintaining consistent performance.

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A rapidly expanding digital commerce brand serving a broad consumer base across multiple product categories adopted Amazon Bedrock foundation models to deliver conversational product search, AI-generated content, and personalised recommendations, boosting engagement and increasing conversions across its online channels.

The Challenge

The customer needed to modernise its digital commerce platform with AI-driven personalisation to improve shopping experiences and boost conversions. Key challenges included:

• Reliance on a static, rules-based recommendation system that struggled to adapt to new shoppers or emerging trends.
• Need for natural language product search and generative product descriptions to improve discovery and engagement.
• Desire for customer service chatbots and personalisation without the complexity of managing large-scale ML infrastructure.

Our Solution

Rambunct implemented Amazon Bedrock to provide a generative AI layer integrated into the customer’s commerce platform, enabling dynamic personalisation at scale.

• Conversational Product Search: Bedrock foundation models enabled customers to describe products in natural language.
• Personalised Recommendations: Combined real-time session data with Bedrock-powered natural language generation for tailored suggestions.
• AI-Generated Content: Automated product descriptions that adapted tone and style to target demographics.
• Customer Support Chatbots: AI assistants embedded in the website and mobile app to reduce reliance on live agents.

The Result

The solution significantly improved engagement, conversion rates, and operational efficiency.

• 25% increase in click-through rates for recommended products.
• 15% higher sales conversions from AI-powered product search and recommendations.
• 70% reduction in content creation time through automated product description generation.
• 60% improvement in customer service response times, reducing human agent workload.

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Rabattcorner, a cashback platform, needed an intuitive analytics layer to visualise Daily, Weekly, and Monthly performance across Gross Merchandise Value (GMV), Revenue, Payments, Average Order Value, Number of Transactions, Transaction Statuses, conversion rate, and more. The team had been using Looker, but sought the advantages of Amazon QuickSight: built-in machine learning insights (anomaly detection, forecasting, automated narratives), a simpler interface with natural-language query (NLQ) for non-technical users, high performance with the SPICE in-memory engine, and a cost-efficient pay-per-session model.

The Challenge

Rabattcorner set out to replace Looker with Amazon QuickSight while improving usability, performance, coverage of key KPIs, and overall cost efficiency.

• Built-in ML insights: Achieve anomaly detection, forecasting, and automated narratives without stitching in external tools.
• Ease of use for business users: Reduce reliance on LookML and enable ad-hoc analysis via natural-language query (NLQ).
• Performance at scale: Speed up interactive dashboards and keep response times consistent on large datasets.
• Cost control: Move from a fixed subscription to QuickSight’s pay-per-session model aligned to actual usage.
• Comprehensive KPI coverage: Track Daily/Weekly/Monthly GMV, Revenue, Payments, AOV, Transactions, Statuses, and conversion in one place.

Our Solution

We implemented a QuickSight-based analytics stack and rollout plan tailored for fast performance and broad self-service adoption.

• Role-based dashboard suite: Built Daily/Weekly/Monthly views for GMV, Revenue, Payments, AOV, Transactions, Statuses, and conversion.
• SPICE acceleration: Modelled and loaded data into the in-memory SPICE engine for fast, interactive exploration.
• ML-powered analytics: Enabled built-in anomaly detection, forecasting, and auto-generated narratives.
• Self-service NLQ: Activated natural-language queries so business teams could ask questions directly.
• Data validation & metric parity: Structured verification to ensure complex calculated fields matched legacy definitions.
• Adoption & enablement: Phased pilot with power users, organisation-specific documentation, and recorded training sessions.
• Performance governance: Established baselines, monitoring, and SPICE-capacity management to keep dashboards responsive.

The Result

The migration delivered measurable gains in speed, cost, and adoption, validated by a three-year TCO analysis.

• Faster delivery: Report development time dropped from 2–3 weeks (Looker) to 3–5 days (QuickSight) (~70% faster).
• Snappier dashboards: Load times improved from 5–7s to 2–3s, boosting usability and adoption.
• Lower support effort: Ongoing effort reduced from 20 hrs/month to 8 hrs/month (60% reduction).
• Higher adoption: Active business users increased from ~40% to ~75% thanks to NLQ and simpler workflows.
• Built-in ML value: Anomaly detection, forecasting, and narratives delivered out of the box, with no external tools required.
• TCO savings: Three-year total fell from $336,000 (Looker) to $167,600 (QuickSight): $168,400 saved (~50%), with ~40% lower licensing and 60% lower ops costs.
• ROI & payback: >300% ROI in Year 1, payback in <3 months, and ~$56,000/year in ongoing licensing and labour savings.

The post Rabattcorner: Cashback Platform Streamlines Analytics with Amazon QuickSight appeared first on Rambunct Consulting.

A major regional insurance provider leveraged Amazon Nova models on Amazon Bedrock to deliver policy-aware AI assistants that automate routine enquiries, improving customer satisfaction and reducing operational costs.

The Challenge

The insurer faced growing challenges in delivering clear, efficient, and compliant customer service.

• Modernise how customers access and understand complex policy information.
• Reduce the pressure and workload on call centre teams.
• Maintain strict compliance with industry regulations while scaling service.

Our Solution

Rambunct implemented a secure, serverless AI solution on AWS to deliver policy-aware, human-like customer interactions at scale.

• Deployed Amazon Nova models on Amazon Bedrock to build conversational AI assistants that explain policy documents in plain language and provide personalised recommendations.
• Used AWS Lambda to securely fetch real-time customer policy data from backend systems, enriching each interaction.
• Delivered the solution via serverless Bedrock API calls, ensuring scalability without in-house AI infrastructure.
• Integrated within a secure VPC environment with AWS KMS-encrypted data handling and Amazon CloudWatch telemetry for monitoring and compliance.

The Result

The solution delivered significant efficiency gains, cost savings, and improved customer experiences.

• 60% of routine queries fully automated.
• 35% reduction in operational costs over three years.
• 40% faster customer response times.
• 17% improvement in customer satisfaction ratings.

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Eide Bailly transformed its financial analytics practice with Amazon QuickSight and multi-source data integration, implemented by Rambunct. The solution enables faster, deeper insights across client engagements, supports scalable reporting, and empowers professionals with next-gen data visualisation and advisory tools.

The Challenge

• Fragmented client data across ERP, accounting, and cloud platforms hindered analysis and consolidation.
• Manual report generation delayed insights and limited focus on high-value consulting activities.
• CPA teams lacked familiarity with cloud-based BI tools, slowing adoption and consistency.
• Legacy tools couldn’t scale to support a growing client base, real-time analysis, or collaboration.

Our Solution

• Integrated QuickBooks, Oracle, SAP, SQL Server, and more into Amazon QuickSight via AWS Glue and Amazon S3.
• Built dynamic dashboards for profitability, cash flow, and audit reporting tailored to client industries.
• Deployed role-based access controls and row-level security to protect client confidentiality.
• Launched extensive internal training and mentorship programmes to build QuickSight adoption across consulting teams.

The Result

The QuickSight platform drove measurable improvements in speed, quality, and scalability of Eide Bailly’s financial analytics delivery.

• 80% faster report generation, enabling consultants to focus on strategy and client engagement.
• 95% improvement in visual presentation quality, boosting clarity during executive briefings.
• 90% QuickSight adoption rate across CPA teams after training and mentorship rollout.
• 70% improvement in client satisfaction due to deeper insights and modern dashboard reporting.

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