Glossary
Glossary
AI Agent, RAG, ERP, digital transformation — terms we get asked about most often. Each entry gives a one-sentence definition plus the common misconception worth knowing before you decide.
AI Agent
An AI agent is an AI system that can plan steps, call tools, and carry out tasks on its own, rather than just answering single-turn questions like a chatbot.
AI agents suit workflows that are repetitive and rule-bound but still require language understanding, such as customer support, document classification, or internal knowledge lookup. A common misconception is that an agent can handle any task; one-off or highly creative work usually isn't a good fit, so evaluating the use case first matters more than the technology.
API Integration
API integration lets different systems automatically exchange data through application programming interfaces — for example, connecting payment gateways, logistics providers, LLMs, or existing enterprise systems.
It's a core part of most custom software development projects, covering payments, logistics, cloud services, LLMs, and IoT devices. A common misconception is underestimating the testing and debugging cost involved — integrating with existing systems is often the most underestimated part of a quote, and a common cause of project delays.
Custom Software
Custom software is a system designed and built from the ground up for a company's specific needs, turning requirements, workflows, and data into a scalable, maintainable digital asset.
Unlike off-the-shelf software, custom software can fit existing business processes closely, though at relatively higher development cost and time. Typical SMB projects range from USD 10k to 60k. A common misconception is assuming custom software is always far more expensive than packaged software — in reality, cost depends on feature scope and integration complexity, and a simple internal tool can be quite affordable.
Digital Transformation
Digital transformation is the process of converting paper-based, spreadsheet-driven, or legacy-system workflows into traceable, auditable, data-driven digital systems.
For traditional industry, the real challenge is usually not technology but aging systems, fragmented data, and resistance from floor-level operators. A common misconception is attempting a full transformation all at once — a more reliable approach starts with a pilot on the most painful, quickest-to-prove workflow, then expands scope after validation.
ERP (Enterprise Resource Planning)
ERP is management software that integrates a company's core processes — orders, inventory, finance, production — into a single system, replacing work that's otherwise scattered across multiple tools.
Commonly seen in manufacturing and mid-to-large company digital transformation projects. A common misconception is that adopting ERP means replacing every existing system — in practice, it's more common to integrate the one or two most painful data gaps first, running the ERP alongside existing systems and validating before expanding scope.
Fine-Tuning
Fine-tuning retrains a model's parameters on a large set of examples so it consistently responds in a specific tone or performs more accurately on a narrow task.
It suits advanced needs where the use case is stable and enough consistently labeled data has accumulated. A common misconception is expecting fine-tuning to compensate for insufficient data — with too little data or a still-shifting use case, its return on investment is usually low, and RAG is typically the more practical starting point.
IoT (Internet of Things)
IoT connects physical equipment through sensors and networks to collect and transmit operational data in real time, forming the foundation of smart factories and intelligent manufacturing.
Common applications include equipment utilization monitoring, real-time production analytics, and predictive maintenance. A common misconception is buying sensors before deciding what to monitor — the right order is to identify the key metrics worth tracking first, then decide which sensors and data frequency are actually needed, to avoid collecting large volumes of data nobody uses.
LLM (Large Language Model)
An LLM is an AI model trained on large volumes of text that can understand and generate natural language, serving as the core engine behind AI agents and chatbots.
Common leading LLMs include GPT and Claude. A frequent misconception is assuming the LLM itself already knows a company's internal information — in reality it only holds general knowledge from training, and needs a mechanism like RAG to answer questions about company-specific data.
MVP (Minimum Viable Product)
An MVP is the smallest working version of a product that includes only its core functionality, built to validate whether the direction is right at the lowest possible cost.
In custom software development, an MVP typically launches in 6-12 weeks and is used to validate whether the core functionality actually meets a real need. A common misconception is treating an MVP as a 'stripped-down final product' — its purpose is hypothesis validation, not feature-cutting, so scope should be built around the single most critical use case.
PoC (Proof of Concept)
A PoC is a small-scale effort to validate, at minimal cost, whether a technical approach or use case is actually viable — it's not yet complete enough for production use.
In AI agent adoption, a typical PoC takes about 2-4 weeks to produce a working version used to validate use-case viability and data quality. A common misconception is pushing a PoC straight into production — a PoC usually hasn't handled exception cases, access control, or formal monitoring yet, and needs further integration work before it's production-ready.
Prompt Engineering
Prompt engineering is the practice of designing and refining the instructions given to an AI model so its output better matches what you actually need.
This matters most in the ongoing optimization phase after an AI agent goes live — for example, adjusting a support bot's tone or narrowing what it's allowed to answer. A common misconception is that a prompt only needs to be written once — in practice, how users actually phrase questions shifts over time, so prompts typically need continuous iteration based on real feedback.
RAG (Retrieval-Augmented Generation)
RAG is a technique where an AI model retrieves relevant information from your own documents before generating an answer, rather than relying only on what it learned during training.
Most enterprise AI agents run on RAG — existing documents, FAQs, and manuals are enough, with no need to train a model from scratch, and updating knowledge is as simple as swapping documents. A common confusion is treating RAG and fine-tuning as interchangeable: RAG solves 'the model doesn't know your data,' while fine-tuning solves 'the model's behavior needs adjusting' — different layers of the problem.
SaaS (Software as a Service)
SaaS is software delivered over the cloud on a subscription basis, letting users access full functionality without installing or maintaining their own servers.
Common in scenarios like data analytics platforms or online education systems, and well suited to products needing fast launch and multi-tenant architecture. A common misconception is that SaaS is always cheaper than custom software — subscription costs accumulate over time and functional flexibility is limited by the platform, so at larger scale over the long term, custom development isn't necessarily the more expensive total-cost option.
Smart Factory
A smart factory uses IoT sensors, real-time data analytics, and automation to give production processes real-time monitoring and decision-support capability.
Common application tiers include equipment monitoring, real-time production analytics, and quality prediction with predictive maintenance — usually built up gradually rather than all at once. A common misconception is that installing sensors alone makes a factory 'smart' — without a dashboard and someone responsible for reviewing the data, no amount of data collection turns into an actual decision.
System Integration
System integration connects multiple existing systems within a company (such as ERP, MES, CRM, and finance) to close data gaps and let information flow correctly between them.
The goal usually isn't replacing every system with a single monolithic package, but ensuring information is entered once and flows correctly between systems. A common misconception is trying to integrate everything at once — a more reliable approach integrates the one or two highest-impact data gaps first, validates the results, then expands scope.
Technical Debt
Technical debt is the extra cost of maintainability that accumulates when shortcuts are taken to speed up development in the short term — it has to be 'repaid' with extra time and effort later.
Common sources include missing tests, rushed architecture decisions, and incomplete documentation — especially likely in projects delivered by a single freelancer. A common misconception is that technical debt is just 'messy code' — in reality it directly slows down future feature development and adds risk when scaling the system or handing it off to a new team.