We build AI systems that work with your data and fit into your existing infrastructure. From Bedrock-powered agents and RAG pipelines to serverless deployment and production monitoring – engineered for real systems, not demos.
Guidance on selecting and setting up tools that fit your use case so your team can move faster with fewer blockers.
Design and build AI systems tailored to your needs, from early prototypes to production-ready solutions.
Experienced engineers who plug into your team and contribute directly to ongoing work.
Review and improve existing models, prompts, and configurations to boost performance and maintainability.
Help with day-to-day AI challenges – debugging agents, improving pipelines, keeping infrastructure stable.
Define what data you need, how to structure it, and how to use it effectively across your systems.
AI systems that integrate with existing business platforms, support real production workloads, and can be monitored, evaluated, and improved over time.
See client cases
Bedrock-powered agents RAG over your internal data Serverless AI backends Prompt design and evaluation Guardrails and safety controls Multimodal input handling Knowledge base management Versioning of prompts and configs Monitoring and observability We design, evaluate, and deploy AI systems in a way that connects model work directly to production outcomes.
Shorten the path from agent design or prompt work to deployed systems using AWS-native APIs, Lambda pipelines, and existing integration patterns.
Use structured evaluation setups, Bedrock GuardRails, and configurable system prompts to improve consistency in real-world usage.
Rely on structured logging, per-turn token tracking, and CloudWatch dashboards to understand agent behavior and cost in production.
Primary platform for LLM integration, including Claude models, prompt caching, guardrails, and multi-turn conversations.
For orchestrating AI agents with tool calling, session handling, and containerized runtimes.
For retrieval-augmented generation (RAG) over internal documents with tenant-aware retrieval and ingestion pipelines.
For serverless APIs, async AI workflows, orchestration, and event-driven processing.
For session storage, configuration management, runtime secrets, logging, dashboards, and operational monitoring.
For AI backend services, agent workflows, chat interfaces, and internal tooling.
A structured process to take your AI use case from idea to production, working closely with your team at each step.
Align on the use case, available data, system constraints, and what a successful outcome looks like in production.
Choose the right method (RAG, fine-tuning, prompting, or hybrid) and define how it fits into your existing systems.
Develop the solution, connect it to your systems and data sources, and test it against real workflows and edge cases.
Release to production, monitor behavior, and help your team maintain and improve the system over time.
years of excellence in IT
experts in Java, DevOps, and AI
long-term and repeat clients
Setronica developed an advanced monitoring and control solution for a Norwegian client's modular data centers.
Setronica collaborated closely with a dedicated client-side data science team to craft a dynamic real-time recommendation service.
