RAG over your own knowledge
Let the model answer from your docs and data, with sources, the right access rules, and a way to check it's actually getting things right.
LLMs & AI AGENTS
LLM features people can actually trust.
NNCODE plugs language models into products, data, and everyday operations, and makes sure they behave once real people use them. We also build the interface, backend, and data integrations around it, so you get a working product from one team, not a model and a to-do list.
WHAT IT LOOKS LIKE UNDER THE HOOD
Every step checked, every run traced.
Tools only get the access they need, nothing goes out without your approval, and every run logs how long it took and what it cost. The console here runs a real flow: search, read, draft, then it waits for you.
Fig 1Agent console
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idle · waiting for a task
Agents that do real work
Agents that research, browse, fill things in, and run multi-step tasks, with small, safe tools and a human sign-off where it matters.
LLMs inside your product
Streaming chat, structured outputs, fallbacks between providers, and keeping an eye on cost. We build the interface around it too.
Knowing it actually works
Test sets from your real tasks, plus tracing for every prompt, tool call, and dollar spent, so you can see quality over time.
THE HONEST TRUTH
The model is the easy part.
A slick demo can hide the hard parts: what the model is allowed to see, what it's allowed to do, how bad answers get caught, and what happens when the provider is slow or down.
So we put solid guardrails around the fuzzy parts: typed inputs and outputs, permissions on the server, small scoped tools, spending limits, a human in the loop for anything risky, and tests built from the real things your users need done.
And because we build the whole system, from the model choice to the UI to the monitoring, nothing falls through the cracks between teams.
HOW WE CONNECT YOUR SYSTEMS
One tool layer, with permissions and a log.
Your CRM, database, internal APIs, and documents stay as they are. We give each one a standard adapter into one tool layer, so an agent only reaches what it has been granted, and every call is written to an audit log.
Fig 2MCP tool wall
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4 systems · 3 tools granted · 0 calls
THINGS WE GET ASKED TO BUILD
Where LLMs are genuinely useful.
Internal knowledge assistantsPermission-aware search, retrieval, answers, and source citations.
Research and browser agentsControlled tool use for data collection, comparison, and reporting.
Document workflowsExtraction, classification, validation, review, and structured handoff.
AI product featuresCopilots, analysis, generation, and workflow automation inside existing products.
Let's talk
Got something you want to build?
Tell us about it. You talk directly with the person who builds it, from the first chat to everything after.
- Reply within a day
- Straight to the builders
- No obligation
NNCODEAI engineering studio · founded by Nicolai Nielsen