What AI products and systems do we build?
We build goal-driven autonomous agents, retrieval-augmented generation pipelines, context-aware chatbots, and workflow automation systems. Our engineering covers the entire application surface: ingestion workers that clean and index domain knowledge, vector storage with semantic search, tool-calling agents that execute database queries or call external APIs, and responsive user interfaces. Backends are built in Python, Node.js, Go, or Rust, while frontends run on Next.js or mobile platforms. Every system is engineered to perform deterministic actions, track token consumption, and return accurate, auditable outputs rather than unpredictable generic text.
How do we partner with your engineering team?
We offer flexible engagement models based on your organizational setup. You can engage us for complete project delivery, where our studio manages architecture, development, quality assurance, and deployment from start to finish. Alternatively, you can hire dedicated engineers who embed directly into your internal product team, participating in daily standups and committing code straight to your repositories. Because our studio builds and operates its own commercial software products alongside client systems, our engineers bring practical product discipline, strict code reviews, and direct communication to every sprint.
Why is production AI hard, and what is our approach?
Moving an agent from a demo notebook into production fails when systems suffer from hallucinations, high latency, context drift, and brittle integrations. Our engineering addresses these hurdles at the infrastructure level. We implement structured data retrieval, hybrid search with semantic reranking, strict schema validation on model outputs, and deterministic fallback routines. When an agent requires external actions, we isolate tool permissions and validate parameters before execution. Combined with robust caching in Redis, persistent storage in PostgreSQL or MongoDB, and comprehensive observability, our applications deliver consistent responses without uncontrolled costs.
Who benefits most from our AI development services?
Our services are designed for founders building AI-native products, CTOs modernizing legacy business operations, and engineering leaders who lack internal bandwidth for specialized language model infrastructure. If your roadmap requires custom knowledge retrieval over private company documentation, autonomous customer support agents that handle transactional workflows, or internal copilots that query relational databases, we provide the full-stack expertise needed to deliver. Clients value our ability to pair modern AI pipelines with battle-tested application development across web, mobile, and distributed backend environments.
Frequently asked questions
What factors drive the overall cost of developing an AI agent or RAG system?+
Project cost depends primarily on the complexity of your reasoning logic, the number of external integrations, and data preparation requirements. An agent that simply answers questions over clean documents requires less engineering than a system that interacts with internal APIs, executes database writes, and runs evaluation loops. Ongoing infrastructure choices, including model inference tokens, vector database hosting, and caching architecture, also influence ongoing operational expenses.
What variables affect the timeline for deploying a production-ready AI application?+
Deployment schedules depend on data accessibility, pipeline readiness, and security requirements. Connecting to structured APIs and clean knowledge bases proceeds much faster than parsing unstructured PDFs or integrating legacy databases with strict compliance boundaries. Building thorough automated evaluations and testing edge cases also shapes the delivery schedule, ensuring the agent performs consistently under production user loads before release.
Who owns the intellectual property and code created during the engagement?+
You retain full ownership of all custom source code, architecture diagrams, proprietary prompt templates, and data schemas developed during our collaboration. Once milestone deliverables are signed off, everything lives in your private version control repositories and cloud infrastructure. We do not retain claims over your custom business logic or proprietary data assets.
How do you select the models, frameworks, and database architecture for a project?+
We choose technology based on your latency targets, data privacy requirements, and existing technical stack. For retrieval systems, we pair vector indexes with PostgreSQL or MongoDB and use Redis for fast memory caching. On backends, we select Python, Node.js, Go, or Rust depending on system throughput needs. We remain vendor-neutral, recommending model providers or self-hosted models that fit your specific operational constraints.
How can we structure our engagement with Marsys Group?+
Clients collaborate with us through flexible engagement structures suited to their management capacity. You can delegate end-to-end delivery of an entire AI system to our studio, where we handle scoping, architectural design, implementation, and deployment. Alternatively, you can bring dedicated software engineers into your internal team to accelerate development speed while maintaining direct technical control over day-to-day prioritization.