NatusAI connects your organisation’s knowledge into AI your people can verify, on infrastructure you control. We are a delivery team and a platform builder: we start with the work, prove the result, and build on a foundation you keep.
Illustrative image.
We work with regulated, data-heavy organisations: telecom operators, grid operators, banks and public institutions. Their questions are concrete: where the time goes, which decision can be defended, and where data is allowed to run.
Our answer has two parts. A senior team that delivers a working result on one business question. And a connected-knowledge platform, so each new application builds on what the last one connected.
What you can buy today is a delivery engagement led by our team, built on the engine proven in our client projects, with NatusAI Lyra interfaces. Products marked Roadmap show where the platform is going. Products and status
Independent by design. Connected by nature.
We scope the work with you instead of assuming one rollout fits every organisation. Each stage ends with a decision you control.
The same four phases as on our home page (analyse, design, build and integrate, adopt and improve), grouped into three decisions.
Who is in the room
Your process owner, team leads and IT architect, joined by compliance and security from the design stage. From NatusAI: a lead consultant who owns the outcome, a solution architect and the engineers who build inside your environment.
Discovery · Analyse
We clarify the outcome, the stakeholders and the constraints. Then we review sources, definitions, permissions and data quality with the people who know them.
Pilot · Design, build & integrate
We build a focused application inside your environment and connect it to the systems you already run. It is measured against the KPIs agreed in discovery.
Scale · Adopt & improve
We document responsibilities, train people team by team and refine with real cases. The next question builds on the data and connections the pilot already put in place.
The right mix depends on your question, your existing systems, your people and where the solution must run. Every service is scoped to make the first release useful and understandable.
People only trust what they understand. Training, documentation and support are scoped with the build, not added at the end.
team training at a telecom operator
post-launch support, same operator
Turn a broad ambition into a practical question, a first scope and decisions your team can act on.
Inventory, reconcile and document the information the workflow needs, reviewed by the people who know it.
Shape a focused application around agreed information, rules, permissions and actions.
Map system connections and operating assumptions, then confirm the deployment setup that fits the scope.
Review behaviour, access and evidence before rollout, and help users and technical owners understand their responsibilities.
Map processes, dependencies and the points where work slows down. Choose improvement methods that fit the problem and the people doing the work.
Support levels, response expectations, training and ongoing optimisation are agreed in the engagement scope.
They are written into the architecture and the contract, not only into this page.
Your data, your models, your exit.
AI runs in your data centre, a European sovereign cloud or a hybrid setup. You decide what leaves, which models are used and how you would move away. We plan the exit at the start.
0
expensive hardware upgrades for a country-wide grid twin
Every answer has a source.
Assistants answer from sources you approve and show them, so decisions can be defended to regulators, auditors and boards.
Every action has an owner.
AI proposes and people decide. Routine work runs within agreed boundaries; exceptions and consequential steps go to a named person.
89%
of enquiries in the 12 automated categories resolved without human intervention, complex cases routed to people
Our articles explain the architecture and the trade-offs behind the platform, for the technical teams who will evaluate it.
Why keeping data and application logic in one model can reduce integration work, why replicas and shards solve different scaling problems, and why AI answers need a memory of what was known and when.
Bring the business question that costs you most. We’ll tell you honestly whether AI can change it, and how we would measure it.
Or write to contact@natusai.com.
NatusAI