Glossary
Plain-English definitions of the AI, data & delivery terms buyers actually search.
What Are Embeddings?
Embeddings turn text, images or records into numbers that capture meaning, so machines compare by similarity. The foundation of RAG and semantic search.
Read →What Is a Data Pipeline?
A data pipeline moves and transforms data from source to where it's used, reliably. Why it's the unglamorous foundation of every AI and analytics system.
Read →What Is a Supply-Chain Control Tower?
A supply-chain control tower is one live view of your whole chain (inventory, orders, delays) with alerts and recommended actions. When you actually need one.
Read →What Is a Vector Database?
A vector database stores embeddings so you can search by meaning, not keywords. It's the retrieval engine behind RAG chatbots and semantic search.
Read →What Is Agentic AI?
Agentic AI is software that pursues a goal by planning its own multi-step actions, how it differs from chatbots and single agents, and where it pays off.
Read →What Is AI Hallucination?
AI hallucination is when a model states false or fabricated information as fact. Why LLMs do it, and how production systems reduce it with grounding and evals.
Read →What Is an AI Agent?
An AI agent is software that pursues a goal by choosing its own next steps and calling tools, not following a fixed script. Where it pays off, and the risks.
Read →What Is an LLM (Large Language Model)?
An LLM is an AI model trained on vast text to predict and generate language. What it can and can't do in production, in plain business terms.
Read →What Is an MVP (Minimum Viable Product)?
An MVP is the smallest version of a product that delivers real value and tests the core assumption. Why minimum and viable both matter, and how AI MVPs differ.
Read →What Is Business Intelligence (BI)?
Business intelligence turns company data into reports and dashboards that inform decisions. Why most BI fails to change behaviour, and what to build instead.
Read →What Is Computer Vision?
Computer vision is AI that interprets images and video: detecting, classifying and reading what's in them. Real business uses, from inspection to capture.
Read →What Is Demand Forecasting?
Demand forecasting predicts how much of each product you'll sell, so you buy, make and stock the right amount. Where AI helps and where it fails.
Read →What Is Dynamic Pricing?
Dynamic pricing sets prices from data (demand, competition, cost) instead of fixed lists. Where it lifts revenue and margin, and the risks to manage.
Read →What Is Fine-Tuning?
Fine-tuning further-trains an LLM on your examples to change its style or teach a narrow skill. When it beats RAG, and when it's the wrong tool.
Read →What Is Intelligent Document Processing (IDP)?
IDP uses AI to read documents (invoices, forms, contracts) and turn them into structured data. How it cuts manual data entry and where it still needs a human.
Read →What Is MLOps?
MLOps is the practice of deploying, monitoring and maintaining ML and AI models in production reliably: the difference between a pilot and a system that lasts.
Read →What Is NLP (Natural Language Processing)?
NLP is AI that works with human language: classifying, extracting, summarising and generating text. How it relates to LLMs and where it's used in business.
Read →What Is Predictive Analytics?
Predictive analytics uses historical data and models to forecast what happens next (demand, churn, price) so teams act early instead of reacting late.
Read →What Is Prompt Engineering?
Prompt engineering is designing the instructions you give an LLM to get reliable, useful output. Why it matters in production and where its limits are.
Read →What Is RAG (Retrieval-Augmented Generation)?
RAG grounds an LLM's answers in your own documents by retrieving relevant passages at query time: how most production GenAI apps avoid making things up.
Read →What Is RPA (Robotic Process Automation)?
RPA automates repetitive, rule-based tasks by mimicking clicks and keystrokes. Where it excels, where it breaks, and how it differs from AI agents.
Read →What Is Semantic Search?
Semantic search finds results by meaning rather than matching keywords, so 'money back' finds your refund policy. How it works and where it beats keyword search.
Read →- Comparisons: The build-vs-buy, X-vs-Y decisions every AI project runs into, answered.
- Cost guides: What AI actually costs to build: real number ranges, not vague estimates. USD for US and Singapore teams, ₹ for India.
- Use cases: What we build, by industry and function, each backed by a shipped, anonymized case.
- Locations: Hiring an AI development company: where we build from, and who we build for, across the US, Singapore and India.
- What Finzarc builds: solutions →
- The delivered-build ledger →
30 minutes with the founding team. Bring the problem; leave with a scope and a timeline.