Your data stays yours
The model runs on your servers or in a Canadian cloud region. Prompts, documents, and answers never leave your control and are never used to train someone else's product.
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AI your company can actually trust — running on your terms.
Everyone wants to put AI to work. Few want their contracts, client records, or source code sent to a third party to do it. FirstLayerIT sets up private, local large language models and AI assistants that run on your own hardware or in Canadian cloud — so your people get the productivity, and your data never leaves your control.
- Data stays in Canada
- Open-weight & Microsoft Copilot
- We run the infrastructure
// why private AI
Cloud AI is easy to start and hard to trust with your data.
Public AI tools train on what they can, log what you send, and live outside Canada. For a law firm, clinic, accounting practice, or engineering shop, that's a hard conversation with clients and regulators. A local model gives you the same kind of assistant — without the data leaving the building.
Predictable cost, not per-token surprises
Cloud AI bills by usage and climbs as people adopt it. A model you own is a fixed, known cost — the same per-device thinking we apply to the rest of your IT.
Built for compliance
PIPEDA, client confidentiality, Law Society and healthcare obligations — keeping data in Canada and on systems you control makes the compliance story simple, not scary.
// what we set up
From a private chat assistant to AI that knows your business.
A secure, in-house ChatGPT-style assistant
An open-weight model (Llama, Mistral, Qwen and similar) hosted on your hardware or private cloud, with a clean chat interface for your whole team — drafting, summarising, and answering questions without anything leaving your network.
AI that knows your documents
We connect the model to your own knowledge — SharePoint, file shares, policies, contracts, drawings, tickets — so it answers from your information with citations, instead of guessing. Access follows the same permissions your staff already have.
Microsoft Copilot, done properly
If Copilot is the right fit, we roll it out with the guardrails most rollouts skip: sensitivity labels, permission clean-up, and DLP — so it can't surface data people were never meant to see.
AI built into real workflows
Quote and proposal drafting, document classification, meeting notes, first-line support replies, code and report assistance — we target the few tasks where AI saves real hours and wire it in, rather than chasing hype.
The GPU servers behind it, managed
We spec, install, and run the inference hardware — on-prem GPU servers or a private cloud instance — and keep it patched, monitored, and backed up, like everything else we manage.
Guardrails, logging & an AI policy
Access controls, audit logs of who asked what, content filtering, and a written acceptable-use policy your team and your clients can stand behind.
// how we deploy
A pilot first. Production only when it earns its place.
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01
Assess & pick the use case
We find the two or three tasks where AI saves the most time for your team, and check what data they'd need — and where that data is allowed to live.
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02
Pilot on real work
We stand up a private model and a small pilot group, measure the time saved and the answer quality, and tune it on your actual documents — no big upfront commitment.
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03
Deploy with guardrails
Once it proves out, we roll it to the team on hardware we manage, with access controls, logging, backups, and an AI-use policy in place from day one.
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04
Run & improve
We monitor the infrastructure, keep models current, and add use cases as you find them — all under the same predictable monthly arrangement as your managed IT.
// let's scope it
Curious whether local AI is right for your business?
Book a free AI readiness assessment. We'll look at where AI could save your team real hours, what data is involved, and whether a private model or Copilot fits — with a clear, no-obligation recommendation.
Book an AI readiness assessment