AI architecture for systems that have to work in production
Before you build with AI, decide how it fits: which model, what data it may see, how answers are checked and what it costs to run. Agentas designs that architecture as your AI architect, then builds and operates it if you want, from a single LLM feature to agents that act on your systems.
What you gain
A plan before the code
An architecture review shows which AI use cases are worth building, what they need and what could go wrong, before money goes into a prototype that cannot grow.
The right model and the right data
A frontier model, an open-weight model or no model at all: we choose per use case, and design retrieval (RAG) so answers come from your own documents.
Quality you can measure
Every design includes an evaluation harness that scores output against real examples, so quality becomes a number you can track.
Security and cost designed in
Prompt injection, tool permissions, data boundaries and token cost are decided in the architecture instead of patched on after launch.
How we build it
We start with the use case, not the model. Together we map the data involved, who may see it, what the AI is allowed to do and how you will know it works. The result is an architecture you can act on: components, data flows, model choice, an evaluation plan, security boundaries and running costs. We hand it to your team, or build it ourselves.
It is the same architecture we use for our own systems in production: a tender platform that grades public tenders against a team's profiles with Claude while prices and bid requirements stay in code, and an AI watcher where every tool is denied and all external text is fenced as data.
Read how we work →What you can count on
- An architect who also builds: the same people design, build and run the system, so the design is tested against reality.
- Model-agnostic: Claude and other frontier models, or open-weight models, chosen by data, cost and requirements.
- Where data cannot leave the building, the architecture can target private AI on your own hardware, offered as an option.
- Deliverables you own: the architecture, the decisions and trade-offs written down, and an evaluation plan.
Agentas Consult: architected, built and run by us
A multi-tenant SaaS platform where AI grades public tenders and job openings against a team's profiles, while prices and bid requirements are computed in code. One database per customer, 313 automated tests, live since September 2026.
Read the case study →What businesses search for
Some of what Norwegian businesses type into Google when they need this — shown here as plain content, not hidden keywords.
Questions
What does an AI architect do?
An AI architect decides how AI fits into your systems: which use cases to build, which models and data to use, how quality is measured and how security and cost are controlled. The result is a design your team can build from, or that we build for you.
Do we need an AI architecture before we build a prototype?
Not a thick document, but the key decisions, yes: data access, model choice, evaluation and security. Settling them early is what lets a prototype grow into a production system instead of being thrown away.
What is a RAG architecture?
RAG (retrieval-augmented generation) lets a language model answer from your own documents: the relevant passages are found first and given to the model as context. The architecture work is deciding how documents are split, indexed, secured and kept current.
Can AI run without our data leaving the company?
Yes. Open-weight models can run on your own hardware behind your firewall, with retrieval over your own documents. We offer that as an option when data cannot go to a public cloud.
Have a project in mind?
Tell us what you are trying to do — you get an honest read on feasibility, and a fast one.
Get in touch