An operating model in which a person supervises, confirms or corrects an automated system’s decisions before they take real-world effect.
In our projects autonomous agents always start under human-in-the-loop supervision: every sensitive action (sending email, writing to a database, payments, deletions) goes through a human confirmation. That is how trust is built and the system’s real behaviour is measured. Only once behaviour is verified and stable is autonomy widened gradually, keeping logs, thresholds and the ability to step in. It is the opposite of a black box: automation stays controllable, traceable and reversible.
Software development driven by a human who directs frontier AI models in natural language, while keeping control over architecture, quality and security.
The term “vibe coding” entered common use in 2025 to describe writing code where the AI does most of the typing and the human directs, reviews and decides. For us it does not mean “let the AI do it”: it means combining model speed with engineering discipline: code review, tests, security hardening, architectural control. The result is real software, in production, built far faster without giving up quality and accountability. The person remains the author and guarantor of the product.
A system based on language models that does not just answer, but plans and executes actions with real tools (APIs, databases, email) to reach a goal.
An AI agent combines a language model with tools and memory: it receives a goal, breaks it into steps, uses tools (querying a business system, sending a notification, updating a record) and evaluates the results. The difference from a chatbot is action: the agent does things, it doesn’t just talk. For that very reason it needs a clear perimeter (which tools it may use, with which limits) and supervision: the same security risks (OWASP Top 10 for LLM Applications, MITRE ATLAS) apply in amplified form once a system can act on the real world.
An attack where malicious instructions hidden in input data hijack the behaviour of an LLM-based application, making it ignore its own rules.
Prompt injection is the number-one risk in the OWASP Top 10 for LLM Applications. Because a model does not natively distinguish “system instructions” from “data to process”, hostile content (in an email, a web page, an uploaded document) can contain commands like “ignore previous instructions” and lead the system to leak data or take improper actions. The defence is not a single trick but a combination: privilege separation, input validation and sanitisation, output guardrails, human confirmation on sensitive actions. It is one reason we design security in from the start.
A technique that has a language model answer from purpose-retrieved documents (your data), instead of only from its training memory.
With RAG, before answering the system searches a knowledge base (manuals, product sheets, contracts, tickets) for the most relevant passages and supplies them to the model as context. Answers are thus anchored to the company’s real data, updatable without retraining the model and can carry verifiable citations. It is the typical architecture for an assistant that “knows” your documentation. It greatly reduces hallucinations but does not remove them: retrieval quality, document permissions and output guardrails still have to be managed.
An artificial-intelligence model trained on large amounts of text to understand and generate language, and to perform tasks described in natural language.
LLMs are the engine of most of today’s AI applications: they understand instructions, summarise, translate, classify, write code. Every frontier lab offers different families, each with its own trade-offs between reasoning, speed and cost: Claude (Anthropic), GPT (OpenAI), Gemini (Google), Nova (Amazon), Llama (Meta), Mistral. There is no single “best model” in absolute terms: there is the right model for a given task. That is why we work multi-provider, with no lock-in, choosing and routing models according to the job to be done.
A public knowledge base of the real tactics and techniques used to attack artificial-intelligence and machine-learning systems.
ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) is, for AI, the equivalent of the well-known MITRE ATT&CK matrix used in cybersecurity. It structures how attackers can compromise an AI system: data poisoning, model extraction, evasion, agent abuse. We use it as a threat checklist when designing applications that use models: it helps reason about concrete scenarios instead of generic security. Together with the OWASP Top 10 for LLM Applications it is one of the references we actually apply.
The OWASP community’s curated list of the ten most critical security risks in applications that use large language models.
OWASP is the world’s reference organisation for application security. Its LLM-specific Top 10 brings attention to AI-specific risks: prompt injection, insecure output handling, training-data poisoning, leakage of sensitive information, excessive agency, unbounded resource consumption. It is a common language to talk about AI security with clients, no fluff. In our projects we use it as a concrete control checklist, alongside the classic OWASP Top 10 and OWASP ASVS for the web and APIs.
An automated control placed around an AI model to filter inputs and outputs and prevent out-of-scope or unsafe behaviour.
Guardrails are the “rails” a system can move within: they validate what comes in (to catch prompt injection or malformed data) and what goes out (to block confidential information, dangerous commands, off-topic content). They can be rules, dedicated classifiers, or a second model that checks the first. They are not a cosmetic optional: they are part of the security architecture, especially when the system can take actions. Together with privilege separation and human confirmation, they define the perimeter within which an agent is allowed to operate.
When an AI model produces a plausible but false or invented statement, presenting it confidently as if it were true.
Hallucinations are an intrinsic limit of language models: they generate statistically plausible text, not verified truth. In business contexts this is a concrete risk: a wrong figure, a non-existent rule, an invented citation. It is managed through architecture: anchoring answers to real data (RAG with citations), adding checks and verification and, for decisions that matter, keeping the human in the loop. That is why on our own site we publish no invented numbers or cases: the same discipline we ask of the models, we apply to our content.
Adapting a pre-trained model to a specific task or style by continuing training on a targeted set of examples.
Fine-tuning is useful when a general model needs to take on a very specific, repeated behaviour, tone or format. It is almost never the first tool to reach for: a good prompt and a well-built RAG architecture often solve the problem at far lower cost and with more flexibility. When it genuinely helps, for instance for a strict output format or to cut cost on a high-volume task, it should be weighed against the alternatives. The rule is the same we apply to models: the right tool for the right problem, no hype.
A numerical representation of a text (or image) that captures its meaning, so the similarity between two pieces of content can be measured.
An embedding turns a sentence into a vector of numbers: content with similar meaning ends up “close” in this space. It is the mechanism that makes semantic search and RAG possible: finding the documents relevant to a question even when they don’t share the same words. Embeddings are stored in a vector database and queried by similarity. Understanding this concept helps explain why a well-built AI assistant can “find” the right answer among thousands of company documents.
An open standard for connecting AI models to external tools and data sources in a uniform, controlled way.
The Model Context Protocol, introduced by Anthropic in 2024 and adopted ever more widely, defines a standard way for an AI application to discover and use external tools (a database, a business system, a service) without bespoke integrations for each. For software builders it matters because it reduces lock-in and makes agents composable. The same security principle still holds, though: giving a model access to real tools requires perimeter, permissions and supervision, because every connected tool also widens the attack surface.
The ability to understand what a system is doing in production through logs, metrics and traces, to diagnose problems and improve it.
Software does not end when it goes live: that is where the hard part begins. Observability is what lets you know whether it works, what it costs, where it slows down and what went wrong. In systems that use AI it matters even more: you must be able to see what an agent decided, which tools it used, what inputs it received, both for debugging and for security and compliance. That is why, when we build an application, we also build the environment that makes it observable and maintainable over time, not just the code.
The practice of deliberately attacking your own system, thinking like an adversary, to find its weaknesses before someone else does.
In the AI context, red teaming means stress-testing a model or agent with hostile inputs: prompt-injection attempts, out-of-scope requests, abuse scenarios. It turns security from a statement into a verification. It complements the frameworks: OWASP and MITRE ATLAS name the known risks, red teaming checks whether your specific system withstands them. We treat it as part of “secure by design”: designing well is not enough, you also have to try to break it, in a controlled way, before production.
An approach where security is a design decision made from the very start, not a coat of paint added at the end.
Secure by design means security choices (privilege separation, input validation, secret management, least privilege) are made while the architecture is being drawn, not chased after an incident. It costs less and protects more, because many risks are simply designed out of the system. It is the principle running through all our work: we apply recognised standards (OWASP, MITRE ATT&CK and ATLAS, NIST AI RMF) from the code to the environment, and we design agents with a perimeter and supervision. Security is not an extra feature: it is the shape of the product.