Put AI into your own product and systems — and keep it reliable
Agents, retrieval over your own documents, LLM features inside an existing product or workflow — built to run in production, with the evaluation and guardrails that keep a language model honest, not just impressive in a demo.
What you gain
AI that ships, not just demos
The gap between a slick demo and a system you can trust in production is where most AI projects stall. That gap — reliability, cost, observability — is the work we do.
Answers grounded in your own data
Retrieval (RAG) over your documents, contracts, or knowledge base, so the model answers from your facts instead of making them up.
Measured, not hoped for
We build evaluation harnesses that score model output against real data, so you know when a change made things better — or worse — instead of guessing.
Cost and safety under control
Token costs accounted for per run, prompt injection treated as a real threat, and tool access locked down — the operational side that decides whether AI is safe to switch on.
How we build it
We treat an LLM as a component in a system, not the whole system. Around it go the parts that make it trustworthy: retrieval so it answers from your data, tool-use with explicit boundaries, an evaluation harness that measures output against real examples, and cost and error monitoring in production. Prompt injection and untrusted input are designed for from the start — external text is fenced as data, never as instructions.
This is how Agentas builds its own LLM systems — a headless scout that grades listings and tenders with every tool explicitly denied and all external text fenced as data, a research platform that classifies company disclosures and validates every signal statistically before it is trusted. The same engineering goes into your product.
Read how we work →What you can count on
- Model-agnostic: we use Claude and other frontier models where they fit, and open-weight models where they fit better.
- We are an AI-native practice — this whole website is built and operated with AI in the loop, which is why lead times are short and iterations quick.
- Where data cannot go to a public cloud, the same system can run as private AI on your own hardware — offered as an option for energy, healthcare and the public sector.
LLM systems we run in production
From a sandboxed scout that grades marketplace and tender listings with a headless LLM, to a market-analysis platform that classifies official disclosures and gates every signal behind statistical validation — real systems, with real traffic. See the AI & Innovation section.
See the work →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
Can you add AI to the software we already have?
Usually yes. Most LLM work is an integration into an existing product or workflow through its API or database, not a rebuild. We start by finding the one place AI earns its keep and build outward from there.
How do you stop the model from making things up?
Retrieval grounds answers in your own data, and an evaluation harness measures how often the output is right against real examples. You get numbers, not assurances — and we design the system so a wrong answer fails safe rather than silently.
Which model do you use?
Whichever fits the job. We work with Claude and other frontier models, and with open-weight models (Llama, Mistral, Qwen) when cost, latency or data sovereignty point that way. You are not locked to one vendor.
What about prompt injection and security?
We treat it as an operational reality, not a footnote. External text is fenced as data rather than instructions, tool access is denied by default and granted explicitly, and each run is sandboxed. This is how we build our own LLM systems.
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