Artificial intelligence
Put AI into production, and keep it there
AI is moving faster than organizations.
Since the arrival of large language models, proofs of concept have multiplied. Yet many struggle to make it into production: inconsistent output quality, unpredictable inference costs, cumbersome integration with existing IT systems and limited buy-in from business teams.
Agentic AI raises the stakes. Agents that can plan, call tools and take action within your applications require robust guardrails: identity and permission management, decision traceability, human oversight and protection against prompt injection and data leakage.
At the same time, the regulatory landscape is taking shape. Québec’s Law 25 governs automated decision-making and personal information, Canada’s AI-specific federal framework is still taking shape, and the EU AI Act sets risk-based obligations for anyone deploying AI in the European Union. Innovating quickly while staying compliant has become a balancing act.
Why industrialize your AI?
- Automate repetitive tasks and refocus your teams on high-value expertise
- Unlock your internal knowledge with assistants grounded in your own documents
- Accelerate software development and content production
- Control inference costs and reduce dependence on model providers
- Mitigate the legal, ethical and security risks of AI
- Create new, differentiating services for your customers
Our artificial intelligence services
From design to operations, we deliver the entire lifecycle of your AI solutions, combining expertise in data, software engineering and security.
Get started fast
Put a first use case into production on your data within weeks, with measurable success criteria.
- Ready-to-deploy document assistants
- Pilot on your real data
- Value and inference cost tracking
- Model selection and integration
Build the foundation
Deploy a secure, reusable AI platform: model gateway, vector databases, prompt catalog and evaluation tooling.
- LLMOps platform and model gateway
- RAG architectures and hybrid search
- Self-hosted open models or managed APIs
- Observability and cost tracking
Design agents
Develop assistants and agents that reason, call your business tools and integrate into your workflows, with human oversight where it matters.
- Agents and multi-agent systems
- Integration via Model Context Protocol (MCP) and APIs
- Business copilots and internal assistants
- Intelligent document processing
Strengthen security and compliance
Govern your use of AI and protect your systems against emerging threats: prompt injection, data exfiltration and model poisoning.
- Governance framework and AI system registry
- AI Act and Law 25 risk mapping
- Red teaming and adversarial testing
- Guardrails and input/output filtering
Operate and optimize
Continuously measure quality, detect drift and optimize the cost-performance ratio of every use case.
- Continuous evaluation and benchmark test sets
- Model routing and inference optimization
- Drift detection and feedback loops
- Ongoing MLOps and LLMOps operations
Innovate and evolve
Track adoption and explore the next frontiers: multimodal AI, small specialized models and edge AI.
- Adoption and usage dashboards
- Fine-tuning and distillation of specialized models
- Multimodal AI: voice, image and video
- Rapid prototyping of new use cases
Our approach: from idea to measurable value
1. Scope
We qualify the business problem, available data, risks and success metrics before writing a single line of code.
2. Prove the value
Within a few weeks, a pilot built on your real data and assessed against objective criteria validates feasibility and expected benefits.
3. Industrialize
We integrate the solution into your IT landscape, with security, observability, cost control and compliance documentation built in.
4. Scale
We track adoption and quality over time, then extend the platform to new use cases by building on existing components.
The technologies we use
We choose the tools that fit your context, favoring open standards, portability and cost control.
- Amazon Bedrock
- Google Vertex AI
- Microsoft Azure AI
- Mistral AI
- Hugging Face
- PyTorch
- LangGraph
- LlamaIndex
- vLLM
- MLflow
- Databricks
- Qdrant
- pgvector
- Langfuse
Frequently asked questions
Do we need to train our own model to benefit from generative AI?
Rarely. In most cases, an existing model combined with a RAG architecture that grounds it in your documents offers the best value-to-cost ratio. Fine-tuning or distilling a more compact model makes sense when you need to handle highly specialized vocabulary, reduce latency and costs at scale, or operate in an air-gapped environment.
Does the EU AI Act apply to us if we are based outside Europe?
Yes, whenever an AI system is placed on the market or used in the European Union, or its outputs are used there. Its obligations vary by risk level and are being phased in gradually. We help you inventory and classify your systems, then put in place the required documentation, human oversight and risk management, in line with Québec’s Law 25 and PIPEDA.
How do we secure AI agents that take action in our systems?
An agent should be treated as an identity in its own right: least-privilege permissions, access restricted to explicitly authorized tools, logging of every action and human approval for sensitive operations. We add protection against prompt injection, isolated execution environments and regular adversarial testing, both before and after go-live.
Does our data remain confidential?
Yes, provided the architecture is designed for it. Depending on the sensitivity of your data, we favor models hosted in a cloud region of your choice, sovereign offerings or open models self-hosted in your own environment. Encryption, index segregation and enforcement of document access rights ensure that each user sees only what they are authorized to see.
Work
