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PromiseQ GmbH, a Berlin-based startup backed by seed funding, is on a mission to redefine surveillance technology. Their flagship solution combines AI-driven detection with crowd-sourced human validation, enabling security systems to drastically reduce false alarms while increasing accuracy and reliability. By integrating seamlessly with leading CCTV and camera technologies, PromiseQ aimed to build a platform adaptable across industries and environments.
To bring this vision to scale, they required to refine their AI models, expand engineering capacity, and create a platform that was not only intelligent but also fast, secure, and enterprise-ready. That is where we partnered with PromiseQ, extending their engineering capabilities through a dedicated remote team and helping turn their bold vision into a market-ready solution.
The challenge was not simply building AI features, but ensuring that the platform could deliver real-time threat detection with enterprise-level reliability. For PromiseQ, this meant optimizing deep learning models to reduce false positives across a wide range of environments, from indoor retail spaces to outdoor industrial sites.
Scaling engineering capacity was another critical hurdle. As a growing startup, PromiseQ needed to move quickly without overstretching resources or sacrificing quality. They required a partner who could provide flexible engineering support while embedding rigorous processes for compliance, security, and performance. Above all, the system needed to integrate seamlessly with a variety of commercial and enterprise camera technologies, making adaptability a key factor in success.
We assembled a dedicated team of full-stack developers and AI engineers who worked as an extension of PromiseQ’s internal staff. Despite geographic boundaries, agile sprints and continuous communication ensured seamless collaboration and productivity.
Our Python engineers focused on refining the deep learning models at the core of PromiseQ’s technology, increasing accuracy and minimizing false alarms. On the backend, we built with NodeJS, TypeScript, and serverless GCP infrastructure, ensuring speed, security, and scalability. The frontend was developed with VueJS and MaterialUI, creating an intuitive operator experience designed for ease of use in real-world security contexts.
To guarantee reliability, we implemented rigorous QA and optimization cycles, testing the platform across diverse environmental conditions and camera types. This continuous validation process ensured that PromiseQ’s solution could maintain consistent performance in dynamic scenarios, from crowded urban spaces to low-light industrial zones.
We assembled a dedicated team of full-stack developers and AI engineers who worked as an extension of PromiseQ’s internal staff. Despite geographic boundaries, agile sprints and continuous communication ensured seamless collaboration and productivity.
Our Python engineers focused on refining the deep learning models at the core of PromiseQ’s technology, increasing accuracy and minimizing false alarms. On the backend, we built with NodeJS, TypeScript, and serverless GCP infrastructure, ensuring speed, security, and scalability. The frontend was developed with VueJS and MaterialUI, creating an intuitive operator experience designed for ease of use in real-world security contexts.
To guarantee reliability, we implemented rigorous QA and optimization cycles, testing the platform across diverse environmental conditions and camera types. This continuous validation process ensured that PromiseQ’s solution could maintain consistent performance in dynamic scenarios, from crowded urban spaces to low-light industrial zones.
The collaboration with PromiseQ has elevated their platform into a smarter, more reliable surveillance solution. By reducing false alarms through improved AI accuracy, we helped security teams focus on real threats, increasing both safety and efficiency. Seamless integration with leading CCTV and camera systems broadened the platform’s adoption potential, making it suitable for global deployment.
By leveraging a remote team model, PromiseQ was able to scale engineering output rapidly without the overhead of expanding in-house capacity. This flexibility allowed them to balance startup agility with enterprise-level robustness, strengthening their competitive edge in the AI surveillance market.
Most importantly, the project has created a future-ready technical foundation for PromiseQ. With scalable cloud architecture, continuously improving AI models, and enterprise-grade reliability, the platform is now positioned to expand its capabilities and lead the next generation of intelligent surveillance solutions.