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Agentic AI Pilots Integrated into Enterprise Data Landscapes
A recurring delivery scenario in regulated and multi-stakeholder environments is to prove value fast with an AI MVP while creating a credible path toward production.
AI consulting for Switzerland and the world
Super AI Labs is based in Zurich and led by engineers trained at ETH Zurich and EPFL. We combine consulting discipline with deep engineering execution to deliver high-accountability software systems in the age of Artificial Intelligence
We've worked with great organizations
Harnessing technology for a better Switzerland
We believe technology is the answer to some of Switzerland's greatest challenges. We deliver robust and scalable software systems using AI-native tools and processes.
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AI-Native Delivery of User-Facing Software with Enterprise Full-Stack Foundations
A common challenge in digital services is not only building a working app, but shipping an experience that real users adopt while maintaining speed of iteration.
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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.
A common error enterprises make with AI is to treat it as a kind of 'bolt on' tool that you access now and then. But the way to get much better results is to make AI an integral part of how you get work done-woven into the whole range of things workers do every day.
Dario Amodei - CEO Anthropic
Services - From requirements to reliable software delivery
We design, build, operate, and maintain AI-native software for Switzerland's private and public sector.
- AI software development. End-to-end delivery of web platforms, data services, and AI-enabled workflows using modern architecture patterns and production-grade engineering standards.
- Agentic AI consulting. Design and delivery of agentic AI systems with retrieval, tool use, human-in-the-loop oversight, and measurable operating boundaries for production use.
- AI workshops. Facilitated workshops for leadership, operations, and engineering teams to turn AI ambition into a prioritized, decision-ready backlog of use cases.
- Document intelligence and workflow automation. Document classification, extraction, and workflow automation for operations-heavy teams that still rely on PDFs, scans, and manual triage.
- Hosting and maintenance. Managed hosting, monitoring, patching, backups, and clear escalation paths so the software we build for you stays stable, secure, and supportable.
Our perspective on agents - Why we believe agents will reshape how organizations operate
Anthropic’s engineering team published a foundational guide on building effective agents. The patterns it describes mirror how we design AI systems for our clients, and they shape the future we are building toward.
The article draws a clear line between workflows, where LLMs and tools are orchestrated through predefined code paths, and agents, where the model dynamically directs its own process and tool use. Both are built from one foundational unit: an augmented LLM with retrieval, tools, and memory the model can actively use.
Anthropic catalogues five composable patterns that, in their experience, cover most useful production systems: prompt chaining, routing, parallelization, orchestrator-workers, and evaluator-optimizer. Each pattern is simple on its own. Combined, they cover sequential decomposition, specialization, parallel execution, dynamic delegation, and self-correcting feedback loops.
The recommendation we most strongly agree with: start with the simplest thing that could work, prefer composable patterns over heavyweight frameworks, and only add complexity when measurably justified. Three principles run through the entire article and through our delivery practice — simplicity, transparency, and disciplined design of the agent-computer interface.
Five patterns that change what software can do
Prompt chaining
Decompose a task into sequential steps, with each LLM call processing the previous output. Trades latency for accuracy on tasks that benefit from intermediate validation.
Routing
Classify the input and direct it to a specialized follow-up. Lets each branch be optimized independently for its category instead of forcing one prompt to cover every case.
Parallelization
Run subtasks simultaneously, either by sectioning independent work or by voting across multiple perspectives. Faster, and often more reliable through ensembling.
Orchestrator-workers
A central LLM dynamically breaks down unpredictable tasks and delegates them to worker LLMs. Right when the structure of the task is not knowable in advance.
Evaluator-optimizer
One model generates a response while another critiques it, looping until the evaluator is satisfied. The closest software analogue to a junior-and-reviewer pairing.
Where this is going
A near future where agents quietly run the back office of every serious organization
We are genuinely excited about what the next few years look like for companies, NGOs, and governments that take this seriously. Not flashy demos, but durable agent infrastructure woven into day-to-day operations: routine workflows automated end-to-end, quality raised through evaluator loops that catch errors before humans do, and operating costs meaningfully reduced because the work happens at machine speed and machine cost.
Public institutions stand to benefit the most. Faster handling of citizen casework, more time freed up for the parts of public service that actually require human judgement, and back-office processes that finally scale with population and demand instead of with headcount. Private companies get the same compounding advantage — every workflow you can encode as an agent is a fixed cost replaced by a marginal one.
Our role in this is straightforward: build these systems with the same engineering rigor we bring to any production software. Measured against acceptance criteria, observable in production, secure by design, and supportable on day 1,000 — not just day one. The patterns are public. The hard part, and the part we love, is delivering them reliably inside organizations that cannot afford to get it wrong.