Data & AI
From raw data
to applied intelligence
part of the delivery
in up to four weeks
privacy designed in from day one
The problem
Where data initiatives stall
Most companies don't fail for lack of a tool or a model. They fail because the data behind the decision isn't reliable, isn't governed, and nobody knows what it costs to keep running.
The AI pilot never leaves the lab
The initiative stalls because the data isn't ready. It's a big part of why 95% of pilots never reach production, and no proof of concept fixes that on its own.
Scattered data, slow reports
Every department has its own spreadsheet and its own version of the number. Decisions wait for the month-end close, and nobody can say which source is right.
No governance, no audit trail
No catalog, no rules, no controls. In a regulated industry that turns into privacy and compliance risk, and it's the first thing an auditor asks to see.
Platform costs nobody controls
The cloud bill keeps climbing and nobody knows which workload consumed what. The data platform has no FinOps.
How we think
A pilot that never reaches production is a cost, not a lesson
Roughly 95% of AI proofs of concept never make it into day-to-day operations. The reason is rarely the model: it's the missing workflow fit and governance. That's why we start with the data foundation and define the success metric before we build. No agreed number, no pilot.
What we do
From the data foundation to agents in production. Most clients start with the foundation and expand as the return shows up.
The foundation that has to exist before any model. It's where most clients start.
Data Engineering
The foundation that unlocks the rest
Collecting, cleaning, and moving data between systems, reliably and under governance.
Business Intelligence
Decisions that don't wait for month-end
Dashboards, KPIs, and self-service analytics in Power BI or Tableau, held to the governance standard that auditable data demands.
Models that answer a business question and go to production. They don't stop at the notebook.
Machine Learning & Predictive Analytics
Anticipate churn, fraud, and demand
Models that predict events from historical data, measured against the business metric agreed up front.
Generative AI
Low-risk tasks, with human review
Generative models for text, code, and images, applied where the return is clear and human oversight is designed into the workflow.
Computer Vision
Images and documents become data
AI that reads and checks images and documents. It's the technology behind the AI bill-payment app we helped a U.S. fintech build, with 550K+ payments processed.
Custom AI
When off-the-shelf won't do
Tailored solutions for scenarios no off-the-shelf tool covers, built on a data foundation that's already in order.
When AI needs to run an entire workflow, from trigger to outcome.
From the data foundation to a model in production
A predictable path from assessment to a model running in production, with the metric tracked at every step.
01
Data discovery
A maturity assessment and a map of sources, quality, and governance, with the target architecture designed. Up to four weeks, with a quick win prioritized.
02
We fix the foundation
Pipelines, a data catalog, and quality controls. We don't require your data to arrive organized, because organizing it is part of the delivery.
03
We prove the use case
The first use case starts with a success metric agreed before work begins, so the return is verifiable, not a promise.
04
We run and evolve it
Models and dashboards in production with observability, governance, and FinOps, so platform costs don't grow out of control.
Why Luby
The entry point is the data, not the model
Vendors who sell the model first find out later that the foundation can't hold it up. We reverse the order, and that's what gets the project into production.
We start with what unblocks everything
Most vendors sell the model and find out later that the data can't support it. We start by fixing the foundation, because that's what decides the rest.
Metric agreed before the build
Every use case starts with a success metric that's defined and measured. Without one, the pilot turns into a demo, not an operation.
Governance by design
A catalog, rules, and auditable controls, from a team certified to ISO 27001 and 27701, with data residency options when your privacy requirements call for them.
FinOps built into the platform
Cost per workload under control from the start, so the cloud bill isn't the next surprise once AI scales.
The metrics we chase
The number the data answers to
The metric changes with the use case, but there always is one. It's agreed at the start and measured to the end.
Time to decision
Data quality
Model accuracy
Cost per workload
Processes automated
Dashboard adoption
Industries
Where we apply it
Financial services leads investment in data, followed by retail, healthcare, manufacturing, and agribusiness.
Case studies
Data and AI tied to real results
Models and platforms Luby built and took into production, with the metric at the center.
Verified by our clients, not by us
37 verified reviews on Clutch with a 4.8 average rating. Clutch verifies every reviewer and publishes the full text, including the critical parts.
Common questions about Data & AI
Get in touch
Start with a data assessment
Tell us about your data challenge and we'll respond with a plan within 24 hours. No commitment, no boilerplate proposal.