How is our data protected?

Which data is processed, where it is stored and who can access it are defined in project scope. Data outside that scope is not included.

What if the AI answers incorrectly?

Critical steps keep human approval, and answers are built to be traceable back to their source record.

Can it connect to our current software?

If that system exposes an API, yes. Where it does not, integration scope is assessed separately.

Does this make sense for a small company?

If you have repetitive, time-consuming knowledge work, size does not matter. Start with a small pilot.

Solutions

Solutions / AI

Enterprise AI Solutions

Run AI inside your own data and your own systems.

Enterprise AI creates value when it is connected to the company's own data and processes, not when it stays a general-purpose chat tool. Lucerna builds AI components that connect to your systems over APIs.

Tell Us What You Need Explore Luci AI

What enterprise AI is actually good for

AI pays off when it takes over repetitive knowledge work. In practice that means:

Customer communication: understanding, classifying and answering incoming requests
Document processing: extracting information from contracts, invoices and forms
Data analysis: spotting patterns and outliers in scattered records
Reporting: turning numbers into readable summaries
Internal assistants: letting staff query company systems in plain language

Solutions grounded in your own data

The components we build rely on your records rather than general knowledge, so the setup differs in every project.

Company documents and records used as the source
Communication with existing software over APIs
Traceability of which record an answer came from
Access limits based on user permissions
Clearly defined points where a human approves

Where to start

Start with a small pilot

One process, narrow scope, measured result. This is the lowest-risk way into AI projects.

Pick repetitive work

Tasks done many times a day, with clear rules and checkable output, are the best first candidates.

Security and data ownership

Which data is processed, where it is held and who can reach it are agreed in writing at the start.

Humans stay in the loop

For critical decisions the system suggests and a person approves. Expanding scope is a decision based on measured results.

How we proceed

1
Choosing the process
We identify together the step where AI would genuinely make a difference.
2
Preparing data
Relevant documents and records are collected; access limits are defined.
3
Pilot
A narrow version goes live and outputs are monitored in real use.
4
Rollout
If results hold up, scope grows and other systems are connected.

Related products

Luci products you can start with on the AI side:

Luci AIOur AI development service, integrated into your systems.Luci AI Call AssistantA voice assistant that answers and routes incoming calls.Luci Excel AnalyticsSpeeding up analysis processes that run through Excel.AI Call CenterOur dedicated page for the call side.

Frequently asked questions

How is our data protected?: Which data is processed, where it is stored and who can access it are defined in project scope. Data outside that scope is not included.
What if the AI answers incorrectly?: Critical steps keep human approval, and answers are built to be traceable back to their source record.
Can it connect to our current software?: If that system exposes an API, yes. Where it does not, integration scope is assessed separately.
Does this make sense for a small company?: If you have repetitive, time-consuming knowledge work, size does not matter. Start with a small pilot.

Let's pick the process to start with

If there is no suitable pilot process, we will say so rather than propose a project.