Avora
Discover how we helped Avora transform from a fragmented startup product into a mature analytics solution trusted by major enterprises. Over the years, our team rebuilt unstable components, delivered self-service reporting tools, and improved system performance, laying the foundation for scalability and intelligent data analysis.
Foreword
Our partner first came to us with an underperforming product in need of stability. What began as a technical recovery effort quickly grew into a deeper collaboration where our team not only rebuilt core components but also helped define how the platform should evolve. Over time, we became closely involved in guiding architecture, performance, and feature delivery — a shift from task execution to true product partnership. This case study highlights key milestones from that journey.
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Services provided
Web development, Mobile development, Quality Assurance
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Team
5 Full-stack developers, 1 Mobile developer, 1 QA engineer
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Cooperation & pricing model
Dedicated team
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Duration
July 2016 — October 2019
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Industry
Business intelligence software
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Country
UK
Story
Avora (formerly Truedash) is a UK-based data intelligence company building a self-service analytics platform for businesses working with complex data. To support teams across industries, including ecommerce, retail, marketing, etc., they aimed to simplify reporting, improve data visibility, and accelerate decision-making without requiring deep technical expertise.
To bring this vision to life, Avora initially partnered with an external development team. But results didn’t match expectations: progress was slow, code quality lacked consistency, and the platform remained fragmented and difficult to scale.
Starting in 2016, our team assumed full-cycle development for both their web platform and mobile app. We rewrote unstable components, optimized data processing, and introduced key capabilities, including a flexible, no-code reporting interface for self-service analytics. Over time, our role expanded beyond implementation to include architecture optimization and technical leadership that aligned engineering decisions with product vision.
Together, our team transformed Avora’s business intelligence platform into a scalable analytics resource used by major enterprise clients. We helped reduce the time required by teams to extract and act on relevant data from multiple days to minutes, and improved system performance, including faster report generation and dashboard responsiveness by 5 times, propelling our partner to meet market demands during a critical growth phase.
Requirements & Challenges
Requirements & Challenges
Avora came to us with an urgent need to stabilize and scale their real-time analytics platform after a previous partner fell short of expectations. Our first task was to audit and clean up inherited code, then rebuild core functionality to support advanced reporting and machine learning (ML) features.
As development progressed, our role expanded to include platform-wide performance improvements, big data orchestration, backend restructuring and custom analytics tooling. The complexity of data pipelines, the need for self-service reporting, and the demand for real-time accuracy presented both architectural and usability challenges.
To meet our partner’s expectations and prepare the product for future growth, our tasks included:
- Cleaning up inherited code and stabilizing delivery: We inherited an unstable codebase from their vendor. It required extensive refactoring, bug fixing, and technical debt cleanup before development could commence.
- Implementing an ETL (Extract, Transform, Load) pipeline: To support dynamic analytics, Avora required an internal data warehouse that could extract, transform and load information from a variety of sources. The system had to perform custom calculations, adapt to increasing complexity, and handle multiple storage layers like MySQL, Hadoop, Redshift and Exasol. We simplified the data pipeline for both developers and non-technical users by creating pre-built connectors and automating complex data handling processes.
- Unifying and transforming fragmented raw data: The system needed to automatically pull raw data from diverse sources, unify it and present insights in customizable charts and tables. This was a key step towards contextualizing complex data to make insights accessible to users.
- Accelerating insights through automation: Manual reporting and data preparation were slowing down customers. Our task was to build features that automated data transformation, anomaly detection and root cause analysis, to reduce time-to-insight from days to minutes.
- Redesigning backend architecture to support scalable analytics: o improve performance and build advanced analytics features, like predictive modeling and anomaly detection, we migrated from Spark to Amazon Redshift and redesigned backend logic. It lets process raw data efficiently and generate reports in real time.
Features
Features
Custom reporting with Card Builder
Our team developed a flexible, no-code interface that lets users build custom reports by assembling Cards — modular visual blocks that combine charts, tables, and formulas. These Cards can be grouped into dashboards, formatted for clarity, and cloned for reuse or collaboration.
Secure and scalable ETL pipeline
We built an ETL pipeline that extracts, transforms and loads data from a wide range of external sources using pre-built connectors. And to support large-scale analytics, our team optimized the backend with warehousing solutions like Redshift and Exasol.
Root cause analysis for business anomalies
ML-driven root cause analysis tools let users pinpoint why specific trends occur, explaining a sudden sales spike or a performance drop. By analyzing variables like time, geography or product category, the system highlights contributing factors to reduce guesswork in high-stakes decisions.
Predictive analytics and trend forecasting
To support forward-looking decision-making, we developed predictive models based on historical data and user-defined parameters. The platform visualizes potential outcomes in interactive charts, helping end clients confidently plan pricing, promotions and resource allocation.
Real-time KPI monitoring and insights
We delivered a mobile app that lets users access KPI cards, dashboards and filtered insights on the go. Mobile users can track performance, scan charts and tables, monitor trends and receive smart alerts about progress; all while staying in sync with the larger system and their desktop-equipped counterparts.
Smart alerts and anomaly detection
A smart alert system uses machine learning (ML) to monitor data for irregularities and instantly notify users of unexpected changes. Alerts can be configured manually or generated automatically based on statistical thresholds. Notifications are delivered across web dashboards and mobile devices to support timely, data-driven decisions.
Stack
- — Platform
- — Frontend
- — Backend
- — Mobile technologies & tools
- Java
- Angular
- AngularJS
- Webpack
- Babel
- Gulp
- C3.js
- D3.js
- Highcharts
- Leaflet
- Apache Groovy
- Grails
- Spring
- Clover
- MongoDB
- MySQL
- Amazon Redshift
- Exasol
- Hadoop
- Spark
- Apache Hive
- Snowflake
- ECMAScript 6 Hive
- Auth0 SDK
- Apache Cordova
- Ionic
Project outcomes
Project outcomes
Through years of collaboration, Avora evolved from a fragmented system into a mature analytics platform capable of serving enterprise clients. Our work delivered the following outcomes that improved stability, scalability, and real-world usability:
- Transformed Avora’s system into a high-performance analytics platform:We increased report generation efficiency and dashboard responsiveness 5X by optimizing backend queries and migrating to more efficient databases like Amazon Redshift. Faster and more efficient access to data and analytics improved overall usability and user experience across the platform.
- Sped up time-to-insight with predictive analytics and automation:We reduced the time required for data preparation and discovery processes from days to minutes, and empowered non-technical users to generate insights independently. With ML-driven features like anomaly detection, root cause analysis and smart alerts, users can now access the insights for more accurate decision-making.
- Simplified complex reporting with a visual, no-code builder: The created Card Builder reduces manual reporting, accelerates time-to-insights, and gives non-tech users the autonomy to build comprehensive analytics reports. Using drag-and-drop components, linked tables, and custom formulas, users can now work directly with complex datasets without relying on technical teams.
- Delivered scalable ETL architecture for high-volume data ingestion:To help Avora scale analytics, we improved the ETL pipeline with flexible transformation logic and pre-built connectors for commonly used data sources. Our work laid the foundation for secure, efficient ingestion from a growing number of systems, letting the platform support real-time insights and evolving enterprise demands.
- Extended data visibility and alerting to mobile devices: The cross-platform app gives end users access to dashboards, alerts and KPIs, in real time and on the go. This keeps executives and team leads informed, helps spot anomalies early, and lets them monitor changes, compile reports, and access insights from anywhere, whether in the office or on the move.
- Positioned Avora for market growth: Our technical leadership helped transform Avora into a scalable analytics platform with broad market appeal. By improving system performance, simplifying self-service analytics, and supporting comprehensive features, our partner reaches new customer segments and meets enterprise expectations. Now, Avora can compete more confidently in data-heavy industries to further expand their market presence.
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Contact us directly via
Visit our HQ in
Cyprus, Nicosia, Griva Digeni, 81-83 Jacovides Tower, 1st floor
Meet our representatives in
The UK, Spain, Bulgaria, Poland, and over 15 other European countries