AI Agents Architecture
How We Work
From Strategic Blueprint to Production Architecture
We are a vertically integrated Software Company, with AI expertise. We develop tailored made solutions in respect of GDPR regulations and privacy rules like EU AI Act. We can act from requirements to deployment and maintenance.
We engineer AI Solutions
AI projects fail for predictable reasons. The architecture doesn’t fit the business. The data strategy is wrong. Hallucinations go unmanaged. The system works in a demo but breaks in production. We engineer against all of these failure modes — by design, from day one.
Phase #1
Assessment & Architecture
Before a single line of code is written, we understand your problem and design the right solution.
We start by deconstructing your business workflows to identify where AI can deliver real, measurable ROI — not where it sounds impressive, but where it actually changes outcomes. We assess whether the current state of your data, your infrastructure, and the available technology can support what you need. We are honest about what is feasible and what is not.
Then we design: agent orchestration, memory management, database strategy, tool integration, and the guardrails that keep the system reliable. This blueprint becomes the contract between your business objectives and our engineering.
The output of Phase 1 is a technical roadmap you can evaluate, challenge, and approve before any development begins.
Phase #2
Engineering & Build
We do not build wrappers. We build production-grade AI systems.
Reasoning engines — custom logic layers that allow agents to solve multi-step problems with controlled hallucination risk, using adversarial design and, where required, neuro-symbolic approaches that go beyond what standard LLM solutions can guarantee.
Integrated tooling — agents that don't just "chat" but actively interact with your databases, CRMs, ERPs, APIs, and legacy systems. Your AI solution works inside your business, not beside it.
The right data architecture — not everything is a vector database. We select and combine the right database strategy for each step of your process: vector, graph, SQL, document, key-value. One size does not fit all, and getting this wrong is why most RAG implementations underperform.
State management — robust architectures that maintain context and continuity across long-running business processes, not just single conversations.
Phase #3
Testing, Deployment & Production
An AI system that works once is a demo. An AI system that works reliably is engineering.
We test for the specific challenges AI introduces: stochastic behaviour, edge cases, hallucination under load, and integration failures. Unit testing, adversarial testing, end-to-end testing, human review — each project gets the testing strategy its risk profile demands.
We deploy with cost-conscious architecture — the right model at the right step, GPU where needed, serverless where appropriate — so your system scales without breaking the bank. And we stay involved after deployment, because production is where real engineering begins.
Who is behind this
This work is led by Sylvain Liège, PhD in Symbolic AI, author of AI: The Hunt for Intelligence — Beyond the Hype and Fear, AWS AI certified, with 25 years of enterprise IT delivery. The person who designs your architecture is the person accountable for its success.
Every project starts with a conversation

See it in action
How we delivered a 200-page international launch blueprint for an ultra-luxury brand using a multi-agent architecture with adversarial fact-checking.
Our latest Publications
Is Ai the 2026 Musket?
Our latest Publications