AI consulting and implementation, built into the systems you already run

Problems we help solve

• Staff already use AI, but nobody has set the rules. People paste work into free chatbots, and leadership cannot say what data has left the organization.
• Copilot licences are bought, but adoption is slow. Answers are generic, or Copilot surfaces files people should never have seen.
• Answers are buried in documents. Policies, procedures, contracts and past project files hold the answers, but finding them takes hours.
• Routine work eats skilled people's time. Invoices, forms, emails and requests are read, keyed in and routed by hand.
• Pilots that never reach production. A demo impressed everyone, but it was never connected to real systems, secured or supported.

Our AI services

Where AI helps first

The best first projects are frequent, well understood tasks where a person can easily check the result. Typical starting points by team:

We are just as clear about where AI is the wrong tool. If a fixed rule, a report or a workflow automation solves the problem more reliably and for less, we will say so.

How we deliver AI projects

1. Assess. We review your goals, systems, data and current AI use, and score candidate use cases on value, data readiness, risk and cost.
2. Prove. We test the most promising use case on your own data, against accuracy and cost thresholds agreed in advance, before you commit to a full build.
3. Build. We integrate the solution with your systems, apply permissions and logging, and design the points where people review or approve.
4. Launch. We train the people who use it, roll it out in stages and monitor accuracy, adoption and running costs.
5. Govern and improve. Policies, risk reviews and monitoring keep the solution safe and useful as models, vendors and your needs change.

Every engagement runs to a written scope with deliverables, acceptance criteria and costs in CAD.

AI with people in control

AI systems handle information that is often personal or confidential, so governance is part of the design from the first day:

• Your data stays yours. Depending on how sensitive it is, we use enterprise AI services whose terms exclude your content from model training (with zero data retention where the provider offers it), models deployed in your own cloud account, or open-weight models on servers you control, where nothing is sent to an outside AI provider. We document where data is processed and stored, and use Canadian regions where data residency matters and the service is available there.
• People decide what matters. Outputs that affect individuals, money or compliance are reviewed by a person, and every solution has a manual fallback.
• Answers you can check. Assistants show the documents they used, and only use documents the person asking is allowed to see.
• Measured, not promised. Accuracy is tested against your own examples before launch and monitored after it.
• Privacy law applies to AI. PIPEDA and provincial laws, such as Quebec's Law 25, apply to personal information used in AI systems. We design with them in mind and work alongside your legal and privacy advisers. This is general information, not legal advice.

Why work with Promatics on AI

AI projects rarely fail because of the model. They fail on permissions, messy data, missing integrations, weak security and nobody owning the system after launch. Those are the things we already do every day as a managed IT, cybersecurity and business systems partner.

• One accountable partner. The same team that manages your Microsoft 365, identity, security and ERP builds and supports your AI solutions.
• Built into your systems. We connect AI to Microsoft 365, Prometheus and ERPNext, CRMs and line-of-business applications through supported interfaces.
• Vendor-neutral advice. We work with Microsoft, Amazon Web Services, Google Cloud, OpenAI, Anthropic and open-weight models such as Llama, Qwen, DeepSeek, GLM and Mistral, and recommend what fits your data, security and budget. Naming a product does not mean we are a partner of its vendor.
• Honest scoping. We tell you when AI is not the answer, and we stop a project that does not meet its agreed thresholds.

Common questions

Where should we start? With a short readiness assessment or a single, contained use case, such as answering staff questions from an approved policy library or reading supplier invoices into your ERP. Early, measurable success builds the confidence and governance for harder cases.

Is our data used to train public AI models? It should not be, and we design to prevent it. We confirm each vendor's data handling terms with you before any real data is used. Our guide to whether AI tools train on your data explains what business terms say.

Can AI run on our own servers, so our data never leaves? Yes. We deploy open-weight models on GPU servers in your own cloud account, such as AWS, Microsoft Azure, Google Cloud or DigitalOcean, or on hardware in your data centre, and connect them to your documents and applications. Prompts, files and outputs stay in your environment. See private AI and self-hosted models and our comparison of self-hosted models and AI APIs.

Do we need Microsoft 365 Copilot, or something custom? Often both. Copilot works well for everyday productivity inside Microsoft 365. A custom assistant or agent makes sense when you need answers from specific systems, tighter control or a process that Copilot does not cover. Our comparison of business AI assistants explains the trade-offs.

What does an AI project cost? It depends on the use case, your data and the systems involved. Our guide to what an AI project costs explains the cost drivers, and every proposal sets out costs in CAD before you commit.