ConceptsHow a Failed Demo Forced Us to Rethink Our Embedding Strategy
ATG’s lessons learned from our embedding solutions in production
You are a CIO
Keep control of AI. Move from POC to production.
You know your business, your ecosystem and your constraints. We bring the AI foundation, the governance framework and the skills transfer: specification, training, co-development or full delivery depending on your strategy.
Your teams focus on the business. We run the AI infra.
From idea to production in 4 to 6 weeks
Building an in-house AI team takes 12 to 18 months: hiring, infra, models, security, MLOps. And if your industry demands it (healthcare, public sector, industry, finance), sending data to a non-EU LLM is not an option.
ATG takes care of the AI infra (models, RAG, agents, monitoring, evaluation) and co-develops use cases with your product teams. EU hosting by default, on-premise available, open-source models supported. You stay the owner of the code and the data.
Co-development
Your domain experts + our AI platform. Not a black-box agency delivery, but a real skill transfer.
Real sovereignty
EU hosting, on-premise possible, open-source models supported. No default data transfer outside the EU.
Maintenance handled by ATG
Models, RAG pipelines, monitoring, upgrades: we maintain it, we keep it evolving. Your teams don’t carry the debt.
Your workstreams
The four AI workstreams of an IT department
What we work on with IT leadership, from the first workshop through to run.
Industrialise your AI POCs
Most enterprise AI projects die between the demo and production. We take your POCs to production: answer quality evaluation, observability, inference cost control, security and service commitments.
AI governance and roadmap
Prioritise use cases by value, define your enterprise AI roadmap and set clear AI governance: data access rights, answer traceability, GDPR and AI Act compliance.
Building your teams' skills
Your teams gradually take over instead of staying dependent on a supplier. We transfer knowledge as the project goes, and we run dedicated sessions when the need goes beyond the scope of an engagement.
Forward Deployed Engineers alongside you
Our engineers work in your context, with your tools and your data. That forward deployed approach shortens the loop between the business need and the code shipped.
Our approach
The 90-10 approach
An AI assistant can be built in many ways: fully generic (one-size-fits-all) or fully custom (everything built for you). Both extremes have their cost. ATG takes a different path.
90% platform
Chat, semantic search, connectors, agents, security, EU hosting, continuous product evolution. A robust, maintained foundation that keeps improving without you having to maintain it.
10% custom
Your documents, your formats, your APIs, your databases. That’s where real differentiation happens, and only there.
What the 10% custom covers
Tailored processing
Your file formats, your extraction rules, your document taxonomy, plugged into the RAG pipeline.
Tailored tools
Your enterprise APIs (CRM, ERP, internal tools) called by the assistant at the right moment.
Database queries
Natural-language access to your structured data (SQL), with permissions kept under IT control.
Resources
Go further
Selected articles and videos to better understand this solution and see how it works in practice.
Articles
Concepts
AILLM On-Premises for Your Enterprise: What Can You Do with €60k?
Agents, RAG, internal assistants: how to choose between Make and Buy for your AI project. Figures, use cases, and cost analysis to help you decide.
4 min
Digital SovereigntyWhat if the United States Turned Off Your Digital Tap?
How dependence on U.S. infrastructure exposes Europe to growing strategic risks - and what to do to protect yourself.
3 min
GuideEnterprise AI agents: use cases and real costs
Enterprise AI agents: the definition, what an agent can really do, the method that gets it into production, and what it actually costs.