Key terms

An artificial intelligence agency designs and implements AI inside another company's real processes. Unlike a software vendor, it does not sell you a licence: it audits how the client actually works, finds where AI produces measurable value, builds the solution (agents, automations, integrations) and trains the team to run it. Agencies with full technical depth also take on the digital product itself and the technology leadership behind it.

An AI agent is a system that, on top of generating text, can take action: read a document, query a database, decide based on business rules, update a CRM and trigger the next step of a process without anyone touching it. What separates it from a chatbot is the ability to act. A chatbot answers; an agent gets the job done. The most common uses in companies are lead qualification, document analysis and first-line support.

An LLM is an artificial intelligence model trained on huge volumes of text to understand and generate natural language. GPT (OpenAI), Claude (Anthropic) and Gemini (Google) are the main ones. In a business setting, LLMs act as the reasoning layer inside automations: they classify, summarize, pull out structured data and draft replies. Their behaviour is kept in check with prompts, business rules and a human check at the points that matter.

RAG is the technique that connects a language model to your own knowledge base so it answers using real company information rather than only what it picked up in training. The system first searches the documentation for the relevant passages, then hands them to the model as context. It is the standard architecture for internal assistants and support chatbots because it cuts hallucinations sharply and lets every answer cite its source.

A hallucination is an answer an AI model gives confidently that is wrong or invented. It happens because models produce the most likely text, not the verified one. In company deployments you contain it with RAG (anchoring answers in real documentation), a human check on decisions that matter, and prompts written so the model is allowed to say "I do not know". Ignoring this risk is the most common mistake when taking AI to production.

n8n is an automation platform for connecting applications and building workflows without writing everything from scratch. It is used to orchestrate processes across CRM, email, databases, APIs and AI models. Compared with Make or Zapier, its advantage is that it can be self-hosted (which matters for sensitive data and GDPR) and it accepts custom logic and code at any point in the flow.

GoHighLevel (GHL) brings CRM, marketing automation, email, WhatsApp, landing pages, calendars and reporting into a single system. It is aimed at service businesses with a sales team that need to trace a lead's whole journey from campaign to close. Implementing it means designing the pipelines around how the company actually sells: setting it up on the defaults is the usual reason these projects fail.

A CRM is where a company records and manages its contacts, sales opportunities and conversations. Its value is not in storing data but in making the real state of the pipeline visible: which leads exist, what stage they are at, who is following them and which channel produced them. A badly implemented CRM, with fields nobody fills in and stages that do not match the real sale, ends up replaced by spreadsheets kept on the side.

A chatbot holds conversations with users on a website, WhatsApp or other channels. Today's chatbots lean on language models and on the company's own knowledge base, which lets them answer precisely instead of walking people down a rigid menu tree. A well-designed chatbot always hands over to a person when it detects urgency, a complaint, or a question beyond what it can handle.

AI Overviews are the AI-generated summaries Google shows above the traditional results. They are assembled from several indexed sources and they cite links. Their direct effect is fewer clicks through to websites, what people call zero-click search, which makes being cited inside the summary itself as valuable as ranking first. It is one of the reasons GEO became necessary.

Structured data is JSON-LD markup that states explicitly what a page holds: whether it is an organization, a service, an article or a list of frequently asked questions. Search engines use it to build rich results, and language models use it to identify entities and attribute information with confidence. Without it, both Google and LLMs have to infer meaning from plain text.

The AI Act is the European Union regulation that sorts artificial intelligence systems by risk level and sets obligations to match: transparency, human oversight, technical documentation and traceability. It applies to any company deploying AI in the EU, not only to those building it. In applied AI projects it belongs in the design phase alongside GDPR, not in a legal review at the end.

GDPR is the European regulation on personal data protection. A DPIA (Data Protection Impact Assessment) is the analysis required when a processing activity poses a high risk to people's rights, which is common on platforms handling health data, biometrics or automated profiling. In digital products both belong in the architecture phase: rebuilding a system to comply afterwards costs far more.

Technical debt is the future cost of picking the fast solution over the right one. It shows up as duplicated code, dependencies nobody updated, missing tests and architectures the product has outgrown. It is not inherently bad, since getting to market quickly sometimes justifies it, but it becomes bad when nobody records it or plans to pay it back: eventually every new feature costs twice what the last one did.

Technical due diligence is the audit an investor or buyer commissions before closing a deal, to judge the real state of a company's technology: code quality and ownership, architecture, security, regulatory compliance, dependence on key people and accumulated technical debt. A poor result can cut the valuation or kill the deal, which is why it pays to prepare for it well in advance.

Time to market is how long it takes to go from conceiving a product to having it in customers' hands. In digital products it is a competitive advantage in itself: getting there first means learning from the market first. Heavy use of AI tooling in development has cut this window significantly, closing in weeks the kind of scope that used to take months.

A headless architecture separates the backend that manages data and content from the frontend that displays it, with an API in between. The advantage is that the same content feeds a website, a mobile app and any future channel without being duplicated, and each layer can evolve on its own. It is the usual approach when a project has to serve several channels or expects to grow beyond a traditional website.

A marketplace is an e-commerce platform where several independent sellers offer their products to the same pool of buyers. Compared with a conventional online store it adds real technical complexity: seller onboarding and verification, commission calculation, payouts, per-seller shipping and dispute resolution between parties. It is a business model, not just a type of shop.

An external CTO (also called a fractional CTO) is a technology director who works for a company on a flexible, part-time or project basis, without being a full-time employee. The role is the same as an in-house CTO's: defining the technology architecture, leading the technical team and aligning technology with business goals. The difference is in the model: no hiring process, no severance, available when you need it, and at a fraction of the cost of a full-time CTO.

GEO is a web content optimization method aimed at getting a brand cited in the answers given by language models like ChatGPT, Gemini, Perplexity and Claude. Unlike classic SEO, which optimizes for page rankings on Google, GEO optimizes so that LLMs pull information out of your content and attribute it to a specific source. The main techniques are: direct, self-contained answers at the start of the content, Schema.org structured data, clearly defined entities, FAQ-format content, strong EEAT signals and thorough topic coverage.

SEO is the set of techniques aimed at improving the visibility of a web page in the organic (unpaid) results of search engines like Google. It is divided into three areas: technical SEO (indexing, speed, structured data), on-page SEO (content, H1/H2, title, meta description, keywords) and off-page SEO (domain authority, inbound links). It complements GEO in a comprehensive digital visibility strategy.

EEAT is the set of four quality signals Google uses to judge whether content deserves to rank well: demonstrable experience (real-world cases, first-hand data), expertise (deep knowledge of the subject), authority (outside recognition, mentions) and trust (transparency, verifiable contact details, brand consistency). They matter just as much in GEO: AI models tend to cite sources with high EEAT, because they favor content that makes clear who wrote it, what experience that person has and whether the claims can be checked.

Applied AI refers to the implementation of AI models and systems in concrete business processes, as opposed to research AI. It includes: integrating LLMs into customer service, automatic document analysis, automation of manual workflows with vision or language models, and personalization at scale. The goal is not to use AI because it's trendy, but to apply it where there is a repetitive process with sufficient data and a measurable ROI.

Process automation means getting software to handle tasks people used to do by hand: sending notifications, extracting data, generating reports, classifying documents, keeping tools in sync. There is a difference between classic automation (deterministic rules, with tools like n8n, Zapier or Make) and intelligent automation (which uses AI to process language or images). The test for whether to automate something: the process has to be repetitive, happen often enough to be worth it, and not depend on complex judgment that cannot be modeled.

A custom webapp is a web application built specifically around a company's own processes, rather than a generic SaaS product. It covers user authentication, roles and permissions, real-time data, integrations with external APIs and workflows tailored to the business. It differs from a website in that its main job is running operations, not communicating. Examples: an internal order management portal, a business analytics dashboard, an in-house SaaS platform.

SaaS (Software as a Service) is a software distribution model in which the provider hosts the application in the cloud and delivers it to users on a subscription basis. Users never install or maintain the software; they just open it in a browser. Well-known examples: Notion, HubSpot, Slack. At Elevatec, we build SaaS platforms for clients who want to offer their own software as a service to their users, including authentication, multi-tenancy, billing and dashboards.

An MVP (Minimum Viable Product) is the smallest version of a digital product that still lets you test a business hypothesis with real users. The goal is not to build something of minimum quality; it is to work out which single feature teaches you the most and build only that. A good MVP has a specific, measurable problem, a real user willing to use it (and pay for it) from day one, and clear success metrics. The most common mistake is building too much before validating anything.

A technology stack is the set of languages, frameworks, databases, cloud services and infrastructure that make up an application or system. People talk about the frontend stack (React, Vue, Next.js), the backend stack (Node.js, Python, Go), the database (PostgreSQL, MongoDB) and the infrastructure layer (AWS, Vercel, Supabase). The choice of stack should come down to the problem, the team and how the product is expected to scale, not to whatever is in fashion. The wrong stack creates technical debt that is expensive to undo.