Core-AI designs and deploys private AI systems for law and engineering firms in Montreal — modern LLMs running on infrastructure you control, not a cloud someone else owns.
Client files never leave your network.
Want to see the technology in action? We’ll walk you through a working private RAG system on a call — answering questions over real documents, running on infrastructure the client controls — and show you what the same approach looks like for your use case.
Step 1: AI Opportunity Discovery — Identify high-value AI use cases.
Step 2: Architecture Design — Design an AI system tailored to the organization.
Step 3: Prototype Deployment — Build a working system for evaluation.
Step 4: Production Deployment — Deploy a scalable AI system in the client infrastructure.
What is private AI and how does it differ from services like OpenAI or ChatGPT?
Private AI refers to AI systems that run entirely on your own infrastructure rather than sending data to external cloud services. Unlike OpenAI or Azure AI, private AI keeps your data, queries, and documents inside your network — critical for regulated industries and organizations with sensitive intellectual property.
What kind of firms does Core-AI work with?
Law firms and engineering firms in Montreal and the surrounding region — typically 5 to 50 people. Both work under real confidentiality obligations, both are document-heavy in exactly the way AI helps most, and both are covered by Quebec’s Law 25. That focus is deliberate: a firm of that size wants an engineer who understands its work, not a vendor running a playbook. We take work outside that focus when the fit is obvious, but those two sectors are who we are built for.
How does a private AI system help with Quebec's Law 25?
Law 25 puts real obligations on how your firm handles personal information, including an assessment before that information leaves Quebec. A private system removes most of that question by not sending the data anywhere: your documents, your queries, and the model itself run on hardware you control, inside your own network. To be clear, no system makes a firm compliant on its own — compliance is broader than any piece of software. What it does remove is the hardest part of the conversation, which is explaining why privileged client information should cross a border to a third-party provider at all.
How long does a private AI deployment take from start to production?
A typical engagement runs 8–16 weeks from Discovery to Production deployment. Simpler systems — such as an internal chatbot over a defined knowledge base — can reach production in 6 weeks; complex multi-agent or infrastructure-heavy projects take longer. We follow a four-step process (Discovery, Design, Prototype, Production) so you can validate fit at each stage before committing to the next.
Do we need in-house AI expertise to work with Core-AI?
No. We handle design, build, and deployment end-to-end. After handoff, we provide full documentation and hands-on training so your team can operate the system independently. Ongoing support arrangements are available.
What open-weight LLMs do you work with?
We work with the leading open-weight models — Llama 3, Mistral, Mixtral, Qwen, Phi, Gemma, and others. Model selection depends on your use case, latency requirements, and hardware budget. We benchmark options during the Design phase and recommend the best fit for your workload.
