AI Services

A full assessment takes 3 weeks on average. It includes interviews with business and IT teams, an audit of data, tools and processes, a 360° score per dimension with a sector benchmark, and a prioritised remediation plan. Adservio has conducted more than 50 assessments since 2021.

AI models don't extrapolate: they reproduce the patterns seen during training. Biased, incomplete or noisy data produces a biased, unreliable model. Data quality accounts for 70–80% of an AI project's success. Before any modelling effort, investing in data (cleaning, labeling, lineage, governance) delivers the highest ROI.

GenAI Enablement covers everything needed for a company to adopt generative AI at scale: model selection (Mistral, Claude, OpenAI, Gemini), building a sovereign stack (RAG, vector DB, MCP), team training, governance and security. It is the shift from an isolated PoC to controlled, industrial-grade deployment.

An autonomous AI agent is a software system able to plan, reason and carry out complex actions without constant supervision: it breaks a task down, picks its tools (Git, Jira, Slack, Kubernetes via MCP) and loops until it reaches its goal. Unlike an assistant that answers a question, an agent runs a business process end to end.

The teams who will have to live with the result, not the ones who prototype. A foundation adopted by a single squad stays a prototype with users: the developers, data engineers and SRE who will operate it daily, and the product managers and business owners who arbitrate what it is trusted with, are the ones who decide whether it is really adopted. So we train both populations together, on the same cases, rather than the technical side separately from the business. Leadership comes third, on what it has to arbitrate and what it must be able to contest.

Responsible AI is AI designed to be compliant, secure, ethical and auditable from the outset. It covers: risk classification (EU AI Act), robustness testing (prompt-injection red teaming, model inversion), explainability (XAI, SHAP, LIME), freedom from discriminatory bias, data governance and full traceability of algorithmic decisions. It is a legal and ethical requirement, not an option.

With the arbitration, not with the tool. A use case that holds has three properties: its value is quantified, its input data already exists in a usable form, and its failure is something the organisation can absorb. Projects that stall after the pilot almost always fail on the second one, the input data living in personal spreadsheets or paper files, which no model fixes. The diagnosis exists precisely to set those aside before a budget is committed.