Does our data need to be ready before we start?

No. If your data can't support a model yet, we start with the foundation: pipelines, data quality, and governance are part of our Data & AI work.

How do you keep the project from becoming another stalled pilot?

Every use case starts with an agreed success metric and with the workflow integration designed from the beginning. Fitting into operations is part of the scope, not a later phase.

Can we engage for a single use case?

Yes. A single-use-case project has a defined scope, price, and metric, runs for up to six months, and ends with a handover.

What happens when the model starts making more mistakes?

Performance is monitored in production, with alerts when it drops and planned retraining. Models degrade over time, and that's built into the operating plan.

Do you use off-the-shelf models or train from scratch?

It depends on the case. An off-the-shelf model when it solves the problem, training on your data when the problem is specific. The choice comes down to feasibility and cost, not preference.

How do you handle data privacy and security?

We work in a governed environment, with traceable decisions and data residency when required. Luby is certified to ISO 27001 and ISO 27701.

Who runs the model after delivery?

Your team can take it over, with a handover and documentation, or Luby's data squad can run it on an ongoing basis, renewed monthly.

Applied AI & Models

From strategy to AI in production

Models that answer a business question and land in the workflow of the people who act on them. Every use case starts with a success metric agreed before the first training run.

Talk to an Expert

A success metric agreed
before the model is trained

One use case, fixed scope
and a defined price

A model serving requests
inside your operation

The problem

Where AI stalls before it reaches operations

The model is rarely the bottleneck. What holds it back is the fit with the workflow, the lack of an agreed number, and no plan for the day the predictions start to drift.

The model works, the business doesn't change

Accuracy looks great in the report, but nobody uses the output to decide anything. The model never fit the workflow of the people doing the work.

The pilot that never goes live

Roughly 95% of AI pilots never leave the experiment stage. What's missing is integration, monitoring, and someone accountable when the model gets it wrong.

No return you can show

The project burns budget and the board asks what changed. Without a metric agreed up front, there's no answer that funds the next round.

A model that degrades in silence

What was accurate six months ago isn't anymore. Without monitoring and retraining, predictions get worse and nobody notices.

How we think

Accuracy isn't the result. It's just a number in the report

Roughly 95% of AI pilots never leave the experiment stage, and the reason is rarely the model. What's missing is the fit with the workflow and the governance around it. That's why the business metric is agreed before the first training run, and integration with the system your people already use is in scope from day one.

If your data can't support a model yet, the path starts with Data & AI.

AI solutions we deliver

Custom models for automation, prediction, and decision-making, always tied to a business number.

Machine Learning Models

Custom ML models for prediction, classification, and recommendation systems.

Intelligent Automation

AI-driven automation for document processing, data extraction, and workflow optimization.

Predictive Analytics

Forecast business outcomes with advanced analytics and machine learning.

Natural Language Processing

NLP solutions for chatbots, sentiment analysis, and text processing.

Computer Vision

Image recognition, object detection, and visual inspection systems.

AI Integration

Bring AI capabilities into the software systems and workflows you already run.

Use cases

AI use cases

Applications already running in production, from financial services to customer experience. Each one started from a business question, not a technology.

From use case to a model in production

A predictable path from choosing the use case to a monitored model in production, with the metric tracked at every step.

01

We pick the right use case

We prioritize by impact and feasibility, and define the success metric before any training. No agreed number, no start.

02

We prove it on real data

A model trained on your data and validated against the agreed metric. This is where you find out whether the hypothesis holds.

03

We wire it into the workflow

The model's output lands in the system your people already use. Workflow fit is what separates a pilot from production.

04

We monitor and retrain

Observability on model performance, with retraining when reality shifts and yesterday's data stops holding.

Stack

Our AI technology stack

Frameworks and platforms chosen by use case and inference cost, not by hype.

ML Frameworks

TensorFlow

PyTorch

Scikit-learnKeras

AI Platforms

OpenAI

Google AI

AWS AI/ML

Azure AI

Languages & Tools

PythonR

Java

C++

Why Luby

The model has to fit the operation

Building the model is the part most teams already know how to do. What decides the outcome is getting it into the workflow of the people who act on it, and keeping it working afterward.

The metric before the model

Every use case starts with an agreed success number and is measured against it. Without that, accuracy ends up in a report instead of a decision.

Built into the workflow

The most common reason a pilot stalls isn't the model. It's that the model doesn't fit what people already do. We design the integration alongside it.

Production with observability

A monitored model, with planned retraining and alerts when performance drops. We don't hand over a notebook; we hand over a running operation.

Auditable decisions

Every model output leaves a trail of how it was produced, with data privacy controls and data residency when your industry requires them.

The metrics we chase

The number the model answers to

Accuracy is a means, not the end. The business metric is agreed before training and measured once the model is in production.

Accuracy in production
Operational adoption
Decisions automated
Response time
Use-case ROI
Inference cost