Machine Learning
A system that improves at a task by learning patterns from data, instead of being explicitly programmed for every scenario. It’s the engine behind most of what people call “AI” in production today — fraud scoring, demand forecasting, recommendation engines.
Large Language Model (LLM)
A model trained on massive amounts of text to understand and generate language. It’s what powers chatbots, copilots and document summarization — but on its own, it doesn’t know your company’s data unless you connect it.
Data Pipeline
The plumbing that moves data from where it’s created to where it’s useful — cleaned, validated and ready for a report, a dashboard, or a model. Most “AI problems” are actually pipeline problems in disguise.
MLOps
The practices and tooling that keep a machine learning model reliable once it’s live — monitoring for drift, retraining on schedule, rolling back when something breaks. Without it, a great model quietly gets worse over time.
Generative AI
Models that create new content — text, code, images — rather than just classifying or predicting. Useful for drafting, summarizing and automating knowledge work, but it still needs guardrails and human review for anything client-facing.
Data Governance
The rules and controls that decide who can access what data, how it’s classified, and how long it’s kept. Not the most exciting part of AI — but it’s the difference between passing an audit and failing one.
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Why this glossary exists
Executives don’t need to become data scientists to make good decisions about AI — but the jargon shouldn’t be the reason a good decision gets delayed. Vendors throw around “LLM,” “MLOps,” and “data governance” as if everyone already knows what’s actually being proposed, what it costs to maintain, and what could go wrong. This page exists so a conversation with your team, your board, or with us, starts from the same plain-English footing.
build vs. buy
Most companies assume building an in-house AI team is the “serious” option and hiring a partner is the shortcut. In practice, hiring internally for AI is slow, expensive, and hard to get right without someone technical already on the team to hire well.
The companies that move fastest usually do both: bring in a partner to ship the first real system in production, while building internal capability alongside it — which is exactly why we offer training and enablement programs, not just delivery.
three AI myths, debunked
“AI just needs more data.” More data helps, but bad data at scale is still bad data. Most AI projects fail on data quality, not data volume.
“A pilot that works is basically done.” A pilot proves an idea works once, on curated data. Production means it keeps working when real users and edge cases show up.
“We’ll figure out governance later.” Governance bolted on after the fact is expensive and incomplete. It’s cheaper, by far, to design it in from day one.
how we actually work
Discovery & data assessment — we make sure the data can actually support the outcome before we promise anything.
Model development & training — real experimentation against your data, not a generic off-the-shelf demo.
Deployment & monitoring — shipped into production with the observability to know if it’s actually working.
Ongoing optimization — models get retrained, dashboards get sharper, and your team gets more capable over time.
the real asset isn't the model
Every company already has a huge source of untapped value sitting in the data it generates every day, and yet so many of these companies never think to treat it as a real asset. What is this valuable asset, really? As we like to put it at ALETERIS, “Given that every company now has access to the same models, the question becomes how do you successfully turn your own data into an advantage no competitor can copy?” It’s not the model. It’s what you feed it.
Quick take: the model is commodity. Your data, your domain knowledge, and how well the two are connected — that’s what actually compounds.