Generative AI Platforms
From strategic framing to daily run, a sovereign and governed GenAI platform that turns AI's promise into measurable value.
Adopting AI is not enough.
The advantage comes from integration.
The differentiator is not the tool, but the integration between the right models, talent and processes that make technology useful, safe and effective. A CIO organisation augmented by AI is not a few more tools: it is a change of posture, where AI becomes a colleague, not a piece of software.
Why now
AIServio, the augmented approach
An augmented CIO organisation is one where every critical process, from code to support and from security to decision-making, is assisted, accelerated or arbitrated by generative AI. Not a few more tools: a change of posture, where AI becomes a colleague rather than a piece of software.
Automate
Delegate repetitive work to AI agents, from tickets and scripts to tests and reports, and free human time for the engineering that carries value.
Augment
Give every person in the IT department a contextualised agent, whether they are a developer, an architect, on support or in security. Productivity becomes collective.
Accelerate
Compress the delivery cycles, from code to run and from business need to production. Time-to-value turns into an advantage rather than a constraint.
Arbitrate
Steer the department with augmented dashboards: AI surfaces the weak signals and informs the decisions rather than reporting on them after the fact.
The agent platform behind every delivery
Specialised agents for spec, code, test, deployment and observability, orchestrated for the Augmented CIO.
Delivery teams
Client information systems
What changes concretely
A developer alone with their IDE. Manual review. Documentation written afterwards, when time allows.
An AI agent inside the IDE. Review augmented by agents. Documentation generated continuously from the code.
Level 1 qualifies and forwards. Four hours to resolution on average. A static FAQ that is rarely updated.
A RAG agent answers in 30 seconds from the internal knowledge base. Level 1 concentrates on the complex cases.
The SOC analyses alerts by hand. Chronic false positives. New threats qualified in days.
AI correlation of alerts. Analysts handle five times more incidents. Threats qualified in hours.
Monthly reporting in a spreadsheet. The CIO steers through the rear-view mirror, once the drift is already there.
A real-time dashboard. AI pre-analyses, explains the gaps and proposes the trade-offs. The CIO steers ahead of them.
What We Do
Augmented software factory
(01)AI-augmented design and development, industrialisation of a complete software factory, legacy modernisation, code review and automated testing.
AI-first platform engineering
(02)Hybrid multi-model LLM gateway, sovereign RAG platforms, AI and inference-cost observability, eval harness and LLMOps pipelines.
Data engineering
(03)Unified semantic modelling and data products, industrialised pipelines, governance and lineage, data quality, legacy-to-modern migration.
Business copilots & agents
(04)Assistants and agents by function, process automation, self-service analytics, capitalising on every use case.
AI compliance & FinOps
(05)Augmented audit of code and architecture, compliance with the AI Act, NIS 2, DORA and GDPR, cost management and AI governance forums.
Academy & upskilling
(06)Skills development, community of practice and continuous knowledge transfer, so the standards and the know-how live inside your teams.
Four Packages
CIO Discover
4 to 6 weeksFixed-priceStrategic framing, use-case matrix, 12-month GenAI roadmap and a governance framework in place.
CIO Pilot
6 to 10 weeksFixed-priceA measurable MVP in a real environment: the application, the documented foundation, impact KPIs and the scale-up case.
CIO Scale
3 to 6 monthsEngagementIndustrialisation of the shared GenAI foundation, operated LLMOps pipelines, in-house Academy and skills transfer.
CIO Run
continuousManagedGenAI-specialised application maintenance, observability and SLAs, annual compliance audit, model monitoring and updates.
Four packages, four commitments: capped price, clear deliverable, guaranteed duration.
Four Axes of Governance
Trust is not a bonus: it is the condition for GenAI at group scale. Data, sovereignty, ethics, security: every axis is framed so that innovation never stops at the legal department’s door.
Data & GDPR
Minimisation, anonymisation and traceability. No personal data ever sent to a third-party model without a contractual framework.
Sovereignty & reversibility
Arbitrate between cloud and sovereign models according to sensitivity. Reversibility of models and vector indexes planned from the outset.
Ethics & bias
AI Act risk mapping, adversarial evaluations and human oversight proportionate to each use case.
Security & audit
Protection against prompt injection and exfiltration, actionable audit logs, LLM observability in production.
A foundation that holds in production
An augmented IT department on a foundation they own
100% data sovereignty · 40+ agents in the catalogue
Cover the whole software lifecycle with AI, from specification to operations, while sovereignty requirements keep data and models inside a controlled perimeter, and AI Act compliance demands traceable, auditable decisions.
The four phases of the cycle were tooled on a self-hosted LLM platform: spec-driven upstream, coding agents under harness, adversarial tests and evals in QA, augmented runbooks in operations. An agent marketplace capitalises every validated case instead of leaving isolated proofs of concept behind.
A hybrid LLM gateway, compliant from the first sprint
−35% delivery cycle time · 100% AI Act and NIS 2 compliance
Accelerate delivery without adding technical debt, govern the routing of several models by data sensitivity, and hold AI Act, NIS 2 and DORA compliance from the very first sprint, across an organisation running on three different clocks.
A hybrid LLM gateway routes every request to cloud or on-premise according to the sensitivity of the data. Strategic and steering committees, a technical cell and a community of practice work alongside the Adservio Academy, across three waves: MVP, Scale and Run.
A data software factory augmented by GenAI agents
×2 delivery velocity · 100% Row Level Security coverage
A rising volume of business requests that data delivery could no longer absorb, and a data estate piling up. It called for a structuring semantic model, end-to-end security and governance, and AI agents working with the internal teams.
An AI-driven agile methodology: an orchestrator agent coordinates specialised agents across the whole cycle, from analysis and product management to architecture, development and QA. Each role produces its artefacts, with skills transfer from the first sprint.
Insights & Perspectives
Semantic Search and Information Retrieval with Transformers
Embeddings, cosine similarity, the retriever and reranker duo, vector databases and hybrid search: what it takes to build an accurate RAG at scale.
Four Retrieval Techniques to Improve RAG
CRAG, Self-RAG, RAG-fusion and Fast GraphRAG: four advanced retrieval techniques that make a RAG system more accurate, more robust and more scalable.
How to Evaluate an LLM System
Quality metrics, eval datasets, LLM-as-judge, prompt regression testing and continuous drift monitoring: how a generative system is measured in production.
The rest of the AI journey
ExpertiseGenAI Engineering
LLM applications, RAG platforms and AI agents, designed and shipped to hold in production.
ExpertiseAI Security & Governance
AI Act and GDPR compliance, explainability, bias detection and continuous supervision.
Launch your augmented CIO organisation
A six-week framing, a measurable MVP, then industrialisation: with governance that holds and teams that grow their skills.
Frequently asked questions
One where your data and your models stay inside your own perimeter. A hybrid gateway routes each request to a self-hosted model or to a cloud provider according to how sensitive the content is, and the rule is written at design time rather than discovered later.
Because each one rebuilds its own retrieval, its own security and its own monitoring, and none of it is reusable. A shared foundation turns the second use case into a matter of weeks instead of a second project from scratch.
It sits between your applications and the models, and decides where each request goes based on the sensitivity of the data. It also centralises what you cannot afford to scatter: access control, audit logs, cost tracking and the ability to swap a model without touching the applications.
By measuring inference cost per use case rather than as one global invoice, then routing each request to the cheapest model that meets the quality threshold. Without that measurement, the GenAI bill grows faster than the value it produces.
Framing takes 4 to 6 weeks depending on scope, sector and the level of compliance required, and produces a prioritised roadmap and a governance framework. A measurable MVP in a real environment follows in 6 to 10 weeks, with a go or no-go decision before industrialising across the group.
Classifying each use case by risk level, documenting the system, keeping human supervision proportionate to the stakes, and being able to trace a decision after the fact. It is an architecture and governance requirement, not a form filled in at the end.
Yes, provided reversibility is designed in from the start: the gateway isolates applications from providers, and vector indexes stay exportable. Without that, changing a model means rebuilding every application that calls it.