AI Agent Development Company. Production Systems, Not Demos.

Frequently Asked Questions

• Who owns the IP and source code?

  You do. Full ownership of all custom code, models, data pipelines, and configurations we build. Everything lives in your repositories from day one. When the engagement ends, you have complete control. No vendor lock-in, no permission needed to modify or extend. For projects built on our HyperCore framework, we offer flexible licensing so you can run and scale independently.

• What does the project team look like?

  A dedicated team sized to your scope, typically 3-8 people. For AI agent projects, that includes a senior AI/ML engineer, a full-stack developer, a UX designer, and a project manager. Our average engineer has 8+ years of experience. The team stays consistent throughout the engagement, so there's no ramp-up time lost to rotation.

• What's the pricing model?

  We scope and price after a consultation so you know the cost before you commit. The 4-week AI Feature Sprint is the most common starting point for AI projects. For ongoing work, we use time-and-materials with defined budgets and monthly reporting. No hourly surprises. We also coordinate AWS co-funding on eligible projects to reduce your investment.

• Can we start with a pilot before a full build?

  Yes, that's what the AI Feature Sprint is designed for. Four weeks, one deployed agent, clear KPIs. You get a working system in production, not a slide deck. The Sprint includes governance controls, cost tracking, and a 90-day improvement plan, so you have a clear path forward whether you continue with us or not.

• What happens if the AI makes a wrong decision in production?

  Every AI decision in our systems carries a confidence score. Low-confidence decisions route to human review automatically. High-stakes actions require explicit approval through configurable workflows. We monitor agent accuracy, latency, and conversation quality in real time. Our ZURI agent runs 5,000+ automated assertions on every code change to catch regressions before they reach users. The architecture is designed so AI handles what it's good at (reasoning, conversation, classification) while deterministic logic handles what it can't (arithmetic, compliance checks, rule enforcement).

• What LLMs do you support? Are we locked into one provider?

  We're model-agnostic. Anthropic, OpenAI, Azure, Gemini. We select the right model per task: simpler classification tasks use smaller, cheaper models while complex reasoning uses frontier models. You're never locked into a single provider. When better models become available, we swap them in without rebuilding your system.

• Do you work with companies outside Europe?

  Yes. Our US office is in Chicago and our engineering hub is in Bucharest, Romania. We work with clients across the United States, Europe, the Middle East, and East Africa. Time zone overlap with US Eastern and Central is built into our operating model. For EU data residency requirements, your data stays in Europe.