Frequently Asked Questions

We're a product-led AI company. Building and operating our own AI products is the core of what we do: eDecont.ro for expense automation and Romanian fiscal compliance, and genyHealth.ai for continuous health intelligence, with more in development. The same model-agnostic platform also powers selected projects we build with clients.

We're model-agnostic by design. We build across Anthropic Claude, Microsoft Azure AI Foundry, OpenAI, and open-source models, and choose the right one for each problem instead of locking you into a single vendor. We're also a member of the Anthropic Claude Partner Network.

Our products use hashed passwords, signed-token sessions, role-based access and audit logging, and we operate under the GDPR as an EU company. Each product's privacy policy names the providers that process data; for example, eDecont.ro hosts personal data in the EU and reads receipts with AI models hosted in Microsoft Azure. See our Trust & Security page for details.

Implementation timeline varies based on complexity. A simple chatbot integration might take 4-6 weeks, while enterprise-scale solutions with RAG and custom integrations typically take 8-12 weeks. We'll provide a detailed timeline during the discovery phase.

Yes! Our solutions are designed for integration with existing systems. We support REST APIs, GraphQL, database connections, and can work with virtually any tech stack you're currently using.

Yes. We can operate and support what we build, including monitoring, updates and improvements, agreed per project.

It depends on the project: fixed-price, time-and-materials or an ongoing operating arrangement. Contact us and we will propose what fits your requirements.

RAG is a technique that combines large language models with your proprietary data sources. It retrieves relevant information from your documents or databases and uses that context to generate accurate, grounded responses. This reduces hallucination and ensures responses are based on your actual data.

Yes, AI agents can orchestrate multi-step workflows. They can make decisions, call APIs, query databases and handle error conditions, while keeping an audit trail of what they did.

MCP (Model Context Protocol) tools provide a standardized way to extend AI capabilities. They allow AI systems to take actions in the real world, like querying databases, calling APIs, or updating records, making AI agents much more powerful.