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Carbonfay
RU

service

AI Systems Development

We develop AI systems for business as engineering infrastructure: contracts, state, model routing, observability and human control.

Cases

A production AI system is not a model with a prompt. It is a system with input/output contracts, observable state, model-routing rules and fault-tolerance layers. We develop AI as engineering infrastructure, not a capabilities demo.

What we develop

Multi-agent systems and AI agents for operations, knowledge-retrieval (RAG) infrastructure, business-process automation, omnichannel communication, monitoring and analytics. Every system is built around an explicit loop: context sources → supply the right fragment → orchestrate steps → tools and agents → handoff to a human → observability.

How it works

Task audit → architecture design (steps, contracts, model selection, control points) → a verifiable loop with a human in the loop → measuring on real data → scaling. For any result you can reconstruct which context was supplied and why a decision was made — without that the system can’t be debugged or proven predictable.

Model choice is a step decision

Not every step needs a strong model: classification and drafts go to a cheap fast one, critical steps to a strong one. The model is chosen by a step’s risk, quality and acceptable latency — not assigned globally. This is about quality and predictable cost at once.

Why Carbonfay

We’re an engineering company: we deliver a system with contracts, observability and predictable economics — operable and extensible. More: AI systems, multi-agent systems, RAG systems and cases.

faq

Straight answers

What do you mean by an "AI system"?
Not a model with a prompt, but a system with input/output contracts, observable state, model-selection rules, failure handling and handoff to a human. Something that runs in production and is debuggable a year later, not something that breaks on the second non-standard case.
How much does an AI system cost?
It depends on the task: number of steps, integrations, control requirements and cost of error. We scope a specific process, not "turnkey AI in general". A sensible start is a small verifiable loop that shows cost and payoff.
What kinds of systems do you build?
Multi-agent systems, AI agents for operations, knowledge retrieval (RAG) and context work, process automation, omnichannel communication, monitoring and analytics. To the task, not a template.
Do you have your own model or platform?
The model is a swappable primitive chosen per step (cost/quality/risk). The value is in the orchestration and engineering around it, not a specific model.

related cases

Next step

Let's design an AI-native automation layer for your operations.

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