service

AI Systems Development for Business

AI systems development for specific processes: contracts, state, model routing, observability and human control — from audit to running in production.

Cases

We develop AI systems for specific business processes and take them to production. Not “turnkey AI in general”, but an engineered construct around your task: input/output contracts, observable state, model-routing rules and fault-tolerance layers. What an AI system is as a class of infrastructure is covered on AI Systems: An Engineering Approach.

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 — which process, what repeats and can be verified, where the cost of error is high.
Architecture design — steps, contracts, model selection per step, control and escalation points.
A verifiable loop — a small working process with a human in the loop and observability, on real data.
Measuring the effect — workload and overhead before and after, not “a sense of value”.
Scaling — on the same frame to adjacent processes.

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: routine on a cheap model removes a significant share of spend with no quality loss where it wasn’t required.

Cost and timeline

We don’t quote a price for “AI in general”. Cost depends on the number of steps, integrations and control requirements; we scope a specific process. The starting point is a short verifiable loop: it already shows cost and payoff, and only then does scaling make sense.

Why Carbonfay

We’re an engineering company: we deliver a system with contracts, observability and predictable economics — operable and extensible, not rewritten after the first non-standard case. See the engineering cases and our approach to multi-agent systems.

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 AI systems development 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.

How long does it take?

A first working loop with a human in the loop and observability is usually weeks, not months — we build one verifiable process rather than "everything at once", then scale on the same frame.

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.