Case Study - Real-Time Edge AI Video Intelligence with Applied NLP Automation
Many public-sector and regulated-industry AI scenarios require real-time production output under operational constraints such as latency, constrained networks, and device boundaries.
- Institution
- Operational AI Platform (anonymized)
- Timeline
- Scope
- Edge AI deployment and NLP automation
Context
This engagement focused on hard deployment constraints rather than lab-style demonstrations: real-time response needs, edge hardware limits, and maintainable operational behavior.
Detailed description
Many public-sector and regulated-industry AI scenarios share a difficult constraint: the system must produce results in real time, in production, under operational limits (compute, latency, constrained networks, or device boundaries). In this case, we built a platform specifically aimed at deploying custom video intelligence and deep learning models onto edge devices to provide real-time performance.
This is not AI in the abstract. It is applied engineering around model deployment and operationalization: ensuring a video-intelligence capability can run on edge hardware and still meet responsiveness needs. The platform focus, deploying custom video intelligence and deep learning models to edge devices, directly reflects that operational requirement.
We complemented that edge-first capability with applied NLP automation experience: we were responsible for machine intelligence systems with a strong focus on natural language processing and delivered an automated email response system for sales tasks using models explicitly including GPT-2 and BERT.
This combination of edge AI (video intelligence) and NLP automation is grounded in a strong technical foundation: graduate-level machine-learning work focused on applied mathematical optimization, ML, and Bayesian neural networks for optimization and policy search.
What this demonstrates for Swiss public-sector tenders: we have proven experience delivering AI systems where deployment constraints are real (edge devices and real-time performance) and where language workflows can be automated with modern NLP, capabilities that often underpin public services such as document triage, operational monitoring, and assistive automation in internal administration workflows.
What we delivered
- Custom video-intelligence model deployment to edge devices
- Deep-learning workflows optimized for real-time performance
- Applied NLP automation for operational communication tasks
- Automated email-response system design and implementation
- Production-oriented model operationalization under constraints
Delivery approach
We treated deployment as a first-class engineering concern, ensuring that model behavior, latency expectations, and operations constraints were addressed as part of the implementation, not deferred to a later phase.
Outcome
The result was a proven capability to deliver AI systems where deployment constraints are real, while also automating language-centric workflows with modern NLP methods.