Model selection and benchmarking
Open-weight models such as Qwen, Llama, Mistral and Gemma compared on your own tasks for quality, speed and hardware cost.
Private & On-Prem LLMs · Canada
Private AI that keeps your data in Canada and under your control
Some data should never be sent to a third-party API. We deploy open-weight language, embedding and speech models inside your own environment, whether that's a Canadian cloud region, a private VPC or hardware in your office, and build the same agentic and RAG capabilities on top.
Why it matters
Why private & on-prem llms needs more than a demo
Legal, healthcare, financial and public-sector teams often can't use hosted AI services because of privacy law, client contracts or internal policy. Open-weight models are now good enough for many document and workflow tasks, but running them well takes careful model selection, quantization, serving infrastructure and evaluation.
What you get
- Model benchmark report on your tasks
- Deployed private inference stack
- Private RAG or agent application
- Security, privacy and operations documentation
What we deliver
Our private & on-prem llms services
Serving infrastructure
High-throughput serving with vLLM on GPUs, Ollama for simple deployments, and MLX on Apple Silicon for fully local workstations.
Canadian cloud and on-prem deployment
Deployment into Canadian regions of GCP, AWS or Azure, a private VPC, or on-premises hardware, with no data leaving the boundary you set.
Private RAG and agents
Local embeddings, vector stores and graph databases so document search and agents run entirely inside your environment.
Privacy-aware design
Data-flow mapping, retention rules, PII redaction and audit logging designed around PIPEDA, Quebec Law 25 and sector rules.
Local speech and voice
On-device speech recognition and text-to-speech for narration, accessibility and voice interfaces, with no audio uploaded.
Use cases
Where teams use it
- Law firms reviewing privileged documents
- Clinics and health organizations handling personal health information
- Financial institutions with strict vendor and data-location rules
- Sanctions and compliance screening over sensitive watchlist matches
- Manufacturers keeping engineering IP off third-party services
Kitchener · Waterloo · Cambridge · Guelph
Private AI for Ontario organizations
Ontario clinics, law firms, municipalities and financial firms often need AI that never sends data outside Canada. We deploy open-weight models in Canadian cloud regions or on-premises for organizations across Waterloo Region and Ontario, including on a single office workstation for small teams.
Service area
Waterloo Region: Kitchener, Waterloo, Cambridge, Guelph, Elmira, St. Jacobs, New Hamburg, Baden, Ayr, Wellesley, Breslau
Nearby, in person: Stratford, Brantford, Hamilton, London, Woodstock, Milton, Toronto and the GTA
Remote: everywhere in Canada, including Ottawa, Montreal, Calgary, Edmonton, Vancouver and Halifax
Process
How a project runs
- 01
Discover
We map the workflow with the people who run it, find the highest-value automation points and agree measurable success criteria.
- 02
Prototype
A working prototype on your real data in weeks, not months, tested with real users against the agreed criteria.
- 03
Harden
Security review, evaluation suite, monitoring, permissions and integration with your systems, ready for production.
- 04
Run and improve
We hand over with documentation and training, or stay on to operate, measure and extend the system.
Private & On-Prem LLMs: frequently asked questions
Are open-source LLMs good enough for business use?
For many document, extraction, classification and summarization tasks, yes, especially when paired with good retrieval. For the hardest reasoning tasks, frontier hosted models still lead. We benchmark on your own tasks so the decision is based on evidence.
Can AI be used in a PIPEDA-compliant way?
Yes, with the right design: a clear purpose, consent where required, data minimization, safeguards, retention limits and transparency. Running models privately removes the question of a third-party processor entirely. This is general information, not legal advice.
What hardware do we need to run an LLM on-premises?
It depends on model size and volume. Smaller quantized models run on a single workstation or Apple Silicon Mac. Larger models and many users need one or more data-centre GPUs. We size it to your workload.
Can you set up private AI for a small Ontario office?
Yes. Smaller teams can run capable open-weight models on a single workstation or Apple Silicon Mac in the office, with no data leaving the building.
Related
Related services
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Readiness assessments, use-case prioritization, vendor selection and a roadmap your team can actually execute.
Learn moreFurther reading
7 min read
RAG vs Fine-Tuning vs Long Context: Choosing the Right Approach for Enterprise Documents
RAG vs fine-tuning vs long context for enterprise documents: when each fits, why citations and access control matter, graph RAG, and how to evaluate it.
Read the guide7 min read
Running AI on Sensitive Data in Canada: Data Residency, PIPEDA, Law 25 and Private LLMs
How to run AI on sensitive data in Canada: PIPEDA, Quebec Law 25, PHIPA, Canadian cloud regions, private LLM options and a practical data residency checklist.
Read the guideTell us about the process you want to automate.
A 30-minute call is enough to tell whether AI is a fit, what a first project would look like and what it would take to run it in production.