Key terms

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. Their role is identical to that of an in-house CTO: defining technology architecture, leading the technical team and aligning technology with business goals. The difference lies in the model: no hiring process, no severance, activatable when needed, and at a fraction of a full-time CTO's cost.

GEO is the web content optimization methodology aimed at being cited in the responses of language models like ChatGPT, Gemini, Perplexity and Claude. Unlike classic SEO, which optimizes for Google page rankings, GEO optimizes for LLMs to extract and attribute information 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, robust EEAT signals and comprehensive 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 are the four quality signals Google uses to evaluate whether content deserves good rankings: demonstrable experience (real cases, proprietary data), expertise (deep knowledge of the subject), authority (external recognition, mentions) and trust (transparency, verifiable contact details, brand consistency). They are equally relevant in GEO: AI models tend to cite sources with high EEAT, as they prioritize content that demonstrates who wrote it, what experience they have and whether it is verifiable.

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 involves having a software system execute tasks that people previously performed manually: sending notifications, extracting data, generating reports, classifying documents, synchronizing between tools. A distinction is made between classic automation (deterministic rules with tools like n8n, Zapier or Make) and intelligent automation (with AI to process language or images). The criterion for automating: the process must be repetitive, have sufficient volume and not require complex non-modelable judgment.

A custom webapp is a web application built specifically to solve a company's processes, unlike a generic SaaS. It includes user authentication, roles and permissions, real-time data, integrations with external APIs and customized flows according to the business. It differs from a website in that its main function is operation, not communication. Examples: internal order management portal, business analytics dashboard, your own 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 by subscription. The user does not install or maintain the software; they simply access it from a browser. Well-known examples: Notion, HubSpot, Slack. In the Elevatec context, we build proprietary SaaS platforms for clients who want to offer their software as a service to their own users, including authentication, multi-tenancy, billing and dashboards.

MVP (Minimum Viable Product) is the smallest possible version of a digital product that allows a business hypothesis to be validated with real users. The goal is not to build something of minimum quality, but to identify the single functionality that generates the most valuable learning and build only that. A good MVP has a specific and measurable problem, a concrete 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.

A technology stack is the set of languages, frameworks, databases, cloud services and infrastructure that make up an application or system. We talk about frontend stack (React, Vue, Next.js), backend stack (Node.js, Python, Go), database (PostgreSQL, MongoDB) and infrastructure layer (AWS, Vercel, Supabase). The choice of stack should depend on the problem, the team and the planned scaling scenario, not trends. An inadequate stack generates technical debt that is costly to correct.