Revolutionising supply chain confidence
Introduction
A Swedish innovator, founded in 2013, initially established itself as a manufacturing consultancy, gaining extensive domain knowledge in Industrie 4.0, Machine Executing Systems (MES), and factory digitalisation. In 2022, leveraging a decade of expertise, the Client embarked on a mission to develop an innovative B2B SaaS platform, designed from the outset for global scalability. This single platform aimed to address two critical industry problems: product genuineness verification, allowing customers to seamlessly protect their brands from counterfeit hazards, and manufacturing quality monitoring, providing a system to monitor the quality of factory line outcomes, such as detecting printing errors, to reduce downtime and waste. The platform’s core utilised a proprietary Digital Twin algorithm to create unique “fingerprints” for products. This technology creates an exact digital replica of each item by using imaging technology and AI to capture and analyse unique features, which are then stored securely in the cloud. This enables real-time authentication and quality monitoring throughout the supply chain via a simple mobile app. The scale of the envisioned solution was significant, with expectations that a single factory system could generate as much as 2TB of data daily (60TB monthly).
The Challenge
The Client possessed deep manufacturing insights, a strong business vision, and a groundbreaking Digital Twin algorithm (core library for image recognition) developed through numerous Proofs of Concept (PoCs). However, they faced several critical challenges:
- System architecture advisory: The Client had a clear vision and the core image recognition library but needed expert system architecture advisory to initiate product implementation. This included defining the entire system architecture, which was complex due to the need to integrate a Cloud component with a Factory component running directly in factories. Selecting appropriate technologies for a scalable SaaS platform and preparing the groundwork for the delivery phase were also key requirements.
- Lack of delivery structure: The Client did not have established delivery structures, including the necessary people, defined processes, or the know-how to set up an effective software delivery capability. This also meant an absence of a project management framework capable of handling the project’s inherent complexity and evolving requirements.
- Time-to-market pressure: The Client aimed to commence product development within a month, ideally as soon as possible. Subsequently, the development team needed to scale rapidly to accelerate product creation and significantly shorten the lead time to market.
- Enhancing core algorithm: The Client’s existing core algorithm, while well-tested and verified through many PoCs, was not ready for production use. It required significant refactoring and integration into a production-ready framework to improve code quality, ensure scalability, and make it a robust, manageable foundation for the new SaaS platform.

Rite NRG's Solution
We partnered with the Client to transform their vision into a market-ready SaaS platform. Our engagement spanned from initial system architecture advisory to full product delivery and handover.
- Platform architecture: Understanding the Client’s vision and existing core algorithm, our architects worked to translate these into a tangible implementation plan. This involved creating a comprehensive System Design Sketch. This foundational document presented a draft of the main architecture for the SaaS platform. The architecture was designed around multiple independent services, strategically divided between those operating in the Cloud and those essential for on-premise operation directly within the Factory. A key Client requirement was the ability to store data in designated data centres, potentially assigned by country, to comply with specific legal conditions. Furthermore, the cloud component was designed to enable centralised monitoring of various globally distributed factories from a single cloud interface. The Factory component was crucial for real-time collaboration with very high-speed cameras, necessitating a portion of the image processing to occur directly on-site to meet performance demands. This distributed approach aimed for scalability, cost-efficiency, and real-time performance where needed at the factory level (using technologies like MQTT for inter-process communication). The design also detailed deployment strategies, including Docker containerisation for the Cloud and robust A/B software update schemes for the Factory component. This initial advisory and design phase defined the overall system architecture, addressed the integration of Cloud and Factory elements including high-speed camera data processing and regional data storage requirements, recommended appropriate technologies (primarily Microsoft Azure), designed for scalability and security, estimated cloud costs, and prepared the technical foundation for subsequent delivery.
- Agile Delivery: With the architecture and technology stack defined, a detailed product roadmap was established, breaking down functionalities into multiple iterative releases and two-week sprints. A SCRUM methodology was implemented to manage the project’s complexity and dynamic nature. This involved:
- Dedicated team: A 12-person dedicated team was assembled, comprising Business Analysts, a Project Manager/Scrum Master, an Architect, and Developers (Python and C++). This team was rapidly formed to meet the Client’s tight timelines and was responsible for refactoring and enhancing the core algorithm code.
- Core team as a bridge: The core team (BAs, PM, Architect) acted as a crucial liaison between the Client and the development team, translating business vision into epics, user stories, and actionable tasks. The Project Manager was responsible for Scrum implementation and establishing project management processes.
- Iterative value delivery: Recognising that the initial System Design Sketch provided a high-level architectural draft, we embraced an agile approach to collaboratively define and refine all product functionalities with the Client. This iterative model was crucial, allowing us to jointly detail new features in regular ceremonies like Backlog Refinement and Sprint Planning, and then present tangible progress during Sprint Reviews to gather immediate feedback on delivered increments. Our full openness to continuous concept changes stemmed from the understanding that we were co-creating the optimal solution with the Client, ensuring the end product truly met their evolving business vision and market needs. Quarterly strategic onsite meetings further solidified this partnership and alignment.
- Culture fit and startup mindset: We understood that partnering with a startup required a specific approach. Their culture was characterised by a high degree of autonomy, which also meant navigating elements of chaos, fewer established processes, and frequent changes in direction. A key aspect was the focus on delivering product value quickly, with less emphasis on perfect code from the outset. Our Project Manager actively worked with our team members, conducting 1-on-1 sessions to instill the appropriate mindset and explain the nuances of a startup environment, including this pragmatic approach to development. This ensured our team inherited and seamlessly integrated with the Client’s dynamic culture, fostering a productive and adaptive collaboration.
- Product handover: Version 1.0 of the unified product platform, encompassing both product genuineness verification and manufacturing quality control functionalities, was successfully delivered. The comprehensive handover to the Client’s in-house team included the source code, detailed documentation, and the full implementation of Software Development Life Cycle (SDLC) processes, including the established SCRUM framework. A key aspect of this phase was a thorough knowledge and know-how transfer, ensuring the Client’s team was fully equipped to take over. To further support this transition and retain critical project expertise within the Client’s organisation, we facilitated the transition of certain members of our team to join the Client’s team directly. This thorough transfer of knowledge, assets, and talent empowered the Client to independently manage future development, allowing them to focus on sales and customer demonstrations.
Technologies Used
SUPPORTED PROGRAMMING LANGUAGES AND PLATFORMS
Python, Django, Microsoft Azure, Azure IoT Hub, Azure Blob Storage, Azure App Service, PostgreSQL, Digital Twin technology (imaging technology and AI), Docker, MQTT, SQL Databases, Live Stream Processing, Pub/Sub Broker.
Results
The partnership with the Client yielded significant outcomes:
- Release 1.0 delivered: Delivery of the product’s version 1.0 enabled the Client to begin showcasing the solution to potential customers and gather valuable market feedback. This foundational version also provided a solid base for further development and adaptation, allowing the Client to create tailored product variations for different industries.
- Processes implementation: A robust SDLC and SCRUM framework was established and transferred to the Client, ensuring sustainable development practices and a complete delivery framework.
- Empowered for growth: The Client was equipped with the necessary tools, source code, documentation, and a strengthened in-house team to independently drive future product enhancements and focus on their core business activities of sales and customer demos.
- Risk mitigation: Collaborating with an experienced delivery partner significantly reduced two key risks for the Client: the risk of ineffective delivery failing to produce a viable product, and the risk associated with market fit. If the project had not found its market, disengaging from an external partner would be considerably more straightforward and less disruptive than downsizing an in-house team.
- Cost reduction: The Client achieved an estimated 30% cost saving by leveraging Rite NRG’s nearshore delivery capabilities from a country with lower labor costs, instead of building an in-house delivery structure.
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