Software, AI & Automation Glossary for Founders

SOFTWARE DEVELOPMENT

API: An API, or application programming interface, is a defined way for two pieces of software to communicate. It specifies what requests a program can make, what data it must send, and what it will receive back, without exposing internal implementation. Web APIs commonly use HTTP and formats like JSON so different systems and languages can work together. APIs let developers build on existing services, integrate tools, and expose their own functionality to others.
Custom software development: Custom software development is building an application tailored to a specific organization's needs instead of using an off the shelf product. It is chosen when existing tools do not fit the workflow, must integrate deeply with other systems, or provide a competitive advantage. It offers control over features, data, and experience, at the cost of building and maintaining the software over time. Success depends on clear requirements, good architecture, and a plan for ongoing support.
Engineering retainer: An engineering retainer is an ongoing arrangement where a business reserves a set amount of development capacity each period for a recurring fee. It covers work such as maintenance, bug fixes, small features, security updates, and support after an initial build is delivered. It gives clients predictable access to a team without hiring full time staff, and gives the provider steady, planned work. It is well suited to software that is live and needs continuous care.
Mobile app build: A mobile app build is the process of designing and developing an application that runs on smartphones and tablets, then packaging it for the app stores. It can be native, built specifically for iOS or Android, or cross platform, using one codebase for both. The scope usually includes user interface, data storage, authentication, and integration with backend services and APIs. Shipping also involves store review, device testing, and ongoing updates after release.
Proof of concept: A proof of concept is a small, focused build that tests whether a specific idea or technical approach is feasible. Its purpose is to answer an open question, such as whether an integration works or whether an AI model is accurate enough, before committing to full development. It is usually narrow, disposable, and not intended for production use. A successful proof of concept reduces risk and informs the decision to move ahead, adjust, or stop.
Technical debt: Technical debt is the future cost created when code is written quickly or with shortcuts rather than in the cleanest way. Like financial debt, it can be reasonable to take on deliberately, but it accrues interest as the shortcuts make later changes slower and riskier. Common sources include rushed deadlines, outdated dependencies, missing tests, and unclear structure. It is repaid through refactoring, and ignoring it tends to slow a team down over time.

SAAS

Multi-tenant architecture: Multi-tenant architecture is a design where a single running application serves many customers, called tenants, while keeping each tenant's data isolated. It is common in SaaS because one shared system is cheaper to operate and update than a separate deployment per customer. Isolation is enforced through database design and access controls so tenants cannot see each other's data. The main trade offs are careful security boundaries and the risk that one tenant's load can affect others.
SaaS MVP: A SaaS MVP is the smallest usable version of a subscription software product that delivers one core outcome to real users. It exists to test whether people will sign up, use, and pay for the product before more features are built. It typically includes authentication, a primary workflow, and billing, while leaving edge cases and secondary features for later. The goal is validated learning, not a finished product.
Total cost of ownership: Total cost of ownership is the full cost of a system across its life, not just the initial price to build or buy it. It includes hosting, maintenance, support, security, upgrades, integrations, and the staff time to operate it. Comparing options by total cost of ownership often reveals that a cheaper upfront choice is more expensive over several years. It is a common lens for deciding between building custom software, buying a product, or using a managed service.

AI

AI avatar / digital twin: In marketing and media, an AI avatar is a computer generated on screen persona, often a realistic presenter, whose voice and appearance are produced by AI from a script. A digital twin in this context is an AI avatar modeled on a specific real person, replicating their look and voice so content can be produced without filming each time. These are used for scalable video, localization, and personalization. The broader engineering term digital twin, meaning a live virtual model of a physical system, is a separate concept.
Fine-tuning: Fine-tuning is the process of further training a pretrained AI model on a specific dataset so it performs better on a particular task or style. It adjusts the model's weights using examples that reflect the target use case, such as a company's tone or a specialized domain. It differs from retrieval augmented generation, which adds external context at query time without changing the model. Fine-tuning can improve consistency but requires quality data and can be costly to maintain as needs change.
Human in the loop: Human in the loop is a design where a person reviews, approves, or corrects an automated or AI driven process at key points. It is used when full automation carries too much risk, such as sending money, publishing content, or making irreversible changes. The system handles routine work and surfaces uncertain or high stakes cases for a human to decide. This balances speed with accountability and provides feedback that can improve the system over time.
Large language model (LLM): A large language model is an AI system trained on very large amounts of text to predict and generate language. It can answer questions, summarize, translate, write code, and follow instructions, based on patterns learned during training. It does not look up facts or reason perfectly, so it can produce confident but incorrect output, known as hallucination. LLMs power chat assistants, and their knowledge is fixed at a training cutoff unless connected to external tools or retrieval.
Prompt engineering: Prompt engineering is the practice of writing and structuring the input given to an AI model to get more accurate and useful output. It includes giving clear instructions, providing examples, supplying relevant context, and defining the desired format. Good prompting can meaningfully improve results without changing the underlying model. In production systems, prompts are often templated, versioned, and tested like any other part of the software.

AI AGENTS

AI agent: An AI agent is a software system that uses a language model to decide and take actions toward a goal, rather than only producing text. It can call tools, query data, and chain multiple steps, observing results and deciding what to do next. Agents differ from simple chat completions because they operate in a loop of reasoning and acting until a task is complete. Reliability depends on clear tool definitions, guardrails, and human oversight for consequential actions.

RAG

Retrieval augmented generation (RAG): Retrieval augmented generation is a technique that supplies a language model with relevant external documents at query time so its answers are grounded in that source material. A retrieval step searches a knowledge base, often using vector embeddings, and passes the top matches into the model's prompt. This reduces hallucination and lets the model answer using private or up to date information it was not trained on. Answer quality depends heavily on how the source content is chunked, indexed, and ranked.
Vector embedding: A vector embedding is a list of numbers that represents the meaning of text, an image, or other data in a way a computer can compare. Items with similar meaning produce vectors that sit close together, which makes semantic search and clustering possible. Embeddings are the foundation of most retrieval augmented generation systems, where a query and stored documents are compared by vector distance. They are generated by a model and stored in a vector database for fast similarity lookup.

AUTOMATION

Webhook: A webhook is a way for one system to notify another automatically when an event happens, by sending an HTTP request to a URL you provide. Instead of repeatedly asking a service whether something changed, your application waits and reacts when the service pushes the event. Common uses include reacting to a completed payment, a new form submission, or a code push. Because the sender controls timing, receivers should verify the request and handle retries and duplicates.
Workflow automation: Workflow automation is the use of software to run a sequence of business steps automatically based on triggers and rules. A typical flow starts with an event, such as a new order or form submission, then moves data between systems and performs actions like notifications or record updates. It reduces repetitive manual work, lowers errors, and makes processes consistent and traceable. Modern automation increasingly adds AI steps to classify, extract, or draft content within the flow.

CLOUD

Serverless: Serverless is a cloud model where the provider runs and scales the servers automatically and you deploy code as functions or managed services. You are billed based on actual usage, such as the number of requests and execution time, rather than for always on machines. Servers still exist, but their provisioning and maintenance are handled by the platform instead of your team. It suits event driven and variable workloads, though long running or latency sensitive tasks can be a poor fit.

HOSTING

Cloud hosting: Cloud hosting runs applications and stores data on servers provided on demand by a cloud provider rather than on hardware you own. Resources such as compute, storage, and networking can be scaled up or down and are typically billed by usage. This removes the need to buy and maintain physical machines and supports rapid provisioning and global distribution. Providers offer varying levels of management, from raw virtual machines to fully managed platforms and serverless services.
Managed hosting: Managed hosting is a service where the provider handles server setup, maintenance, security patching, backups, and monitoring on your behalf. It lets teams run applications without operating the underlying infrastructure themselves, trading some control for reduced operational burden. It sits between self managed servers and fully serverless platforms in terms of responsibility. It suits organizations that want reliability and support without a dedicated operations team.

DEVOPS

CI/CD: CI/CD stands for continuous integration and continuous delivery or deployment, a practice for shipping software changes safely and often. Continuous integration merges code frequently and runs automated tests to catch problems early. Continuous delivery keeps the software in a release ready state, and continuous deployment pushes passing changes to production automatically. Together they form a pipeline that reduces manual steps and shortens the time between writing code and running it live.
Infrastructure as code (IaC): Infrastructure as code is the practice of defining servers, networks, and other infrastructure in configuration files rather than setting them up by hand. Those files are version controlled and applied by tools that create or update the real resources to match. This makes environments reproducible, reviewable, and easier to recreate after failure. Common tools include Terraform and cloud native templates, and the approach is central to reliable DevOps.

WEBSITE/CRM

CRM automation: CRM automation is the use of software rules and workflows to handle customer relationship tasks without manual effort. Common examples include capturing leads from forms, assigning them to owners, sending follow up emails, and updating records based on activity. It aims to reduce data entry, shorten response times, and keep customer information consistent across a team. It usually connects a CRM to other tools such as email, calendars, and websites through integrations or webhooks.
Digital experience platform (DXP): A digital experience platform is a set of integrated tools for building and managing customer facing digital experiences across channels such as web, mobile, and email. It typically combines content management, personalization, analytics, and customer data so experiences can be tailored and measured. It aims to unify what were once separate systems into a single platform for marketing and product teams. Vendors position DXPs as an evolution beyond a standalone CMS.
Headless CMS: A headless CMS is a content management system that stores and organizes content but does not control how it is displayed. Content is delivered through an API, so developers can render it on any front end such as a website, mobile app, or kiosk. This separation lets teams reuse the same content across channels and choose their own presentation technology. It contrasts with traditional systems like classic WordPress, where content and its front end template are tightly coupled.

PREMIUM/LUXURY DIGITAL

Premium digital experience: A premium digital experience is a website, app, or product interaction crafted to feel refined, fast, and distinctive, in line with a luxury or high end brand. It emphasizes polished visual design, smooth motion, thoughtful detail, strong performance, and accessibility rather than only basic functionality. The intent is to signal quality and build trust, since the experience itself communicates brand value. Delivering it usually requires close collaboration between design and engineering and careful attention to craft.

Software and AI, in plain English.

SOFTWARE DEVELOPMENT

SAAS

AI

AI AGENTS

RAG

AUTOMATION

CLOUD

HOSTING

DEVOPS

WEBSITE/CRM

PREMIUM/LUXURY DIGITAL

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