Discuss your project
Business Systems & ERP

AI solutions and intelligent automation

AI can read documents, find answers in large collections of internal information, forecast demand and take routine work off your teams. We help you choose use cases where it genuinely adds value, build them into your existing systems, and keep people in control of decisions that matter.

Discuss your project How the work is delivered

A good fit if

  • Staff spend hours reading invoices, forms or contracts and typing their contents into another system.
  • People struggle to find answers buried in policies, procedures, manuals and past project files.
  • You want to forecast demand, cash flow, maintenance needs or customer churn from your own historical data.
  • Back-office requests arrive by email in many formats and must be sorted, routed and summarized by hand.
  • Leadership wants to use AI but needs a clear, governed approach rather than scattered experiments.

Another approach may suit you better if

  • You want AI to make final decisions about people, credit, health or legal matters without human review. We will not build that.
  • The information the AI would need is not written down or digitized yet. That groundwork comes first.
  • You need results that are always correct. AI outputs can be wrong, so every design includes checks and a person who reviews.

What this service is

We design and deliver practical AI solutions and intelligent automation. Generative AI tools such as ChatGPT have changed how people expect to find information: they ask a question in plain language and expect a direct answer. Inside an organization, the same approach can help staff find answers in their own documents, and it can take on reading, sorting and summarizing work that used to need a person for every item.

We treat AI as one tool among many. Some problems are better solved with a clear workflow automation rule or a report from data analytics. We recommend AI where it clearly adds value and design every solution so a person stays in control of consequential decisions. Our article on AI search and business information looks at how AI is changing the way people find information.

What we build

  • Document processing and OCR: reading invoices, purchase orders, forms, claims and contracts, extracting the fields you need, checking them against your ERP or CRM and sending uncertain items to a person for review.
  • Internal knowledge assistants: assistants that answer staff questions from approved sources, such as policies, procedures, product manuals and past project documents, cite where each answer came from and respect the access permissions those documents already have.
  • Predictive analytics: models that forecast demand, cash flow, stock levels, maintenance needs or customer churn from your historical data, with their accuracy tested before anyone relies on them.
  • Back-office intelligent automation: classifying and routing incoming requests, summarizing long email threads or case notes, drafting routine responses for a person to approve, and matching transactions for reconciliation.

These solutions connect to the systems you already run, including Prometheus and ERPNext, Salesforce, HubSpot, NetSuite and Microsoft 365, through our system integration work.

Platforms

We choose platforms based on your data, security requirements and existing licences. Typical options include Microsoft Azure AI services and Copilot Studio, Amazon Bedrock and Textract, Google Cloud Vertex AI and Document AI, and open-source models hosted in your own environment. Mentioning a product does not mean we are a partner of its vendor.

Responsible AI and governance in Canada

AI solutions handle information that is often personal or confidential, so governance is part of the design, not an afterthought:

  • Privacy law: PIPEDA and provincial laws, such as Quebec's Law 25, apply to personal information used in AI systems. Law 25 also sets transparency requirements when decisions about a person are made exclusively by automated processing. The Office of the Privacy Commissioner of Canada has published guidance on responsible generative AI.
  • Human oversight: people review outputs that affect individuals, money or compliance, and can override the system.
  • Data handling: we document what data each solution uses, where it is processed, how long it is kept and who can access it, and we avoid sending more personal information to a model than the task needs.
  • Transparency and testing: users know when they are working with AI output, and each solution is evaluated for accuracy and monitored after launch.

Federally regulated organizations and public bodies may have additional expectations. This is general information, not legal advice; your legal and privacy advisers should confirm how the rules apply to you.

What is included

The exact list is agreed in writing for each project. These are the usual deliverables and the usual boundaries.

Typical deliverables

  • An opportunity assessment scoring candidate use cases on value, feasibility, data readiness and risk.
  • A data handling design covering what data is used, where it is processed and stored, and who can see it.
  • Document processing that extracts data from invoices, forms and other documents into your ERP, CRM or workflow, with confidence checks and human review.
  • Internal knowledge assistants that answer questions from your approved documents, cite their sources and respect existing permissions.
  • Predictive models for forecasting or risk scoring, with their accuracy measured against historical data.
  • Intelligent automation that classifies, routes and summarizes requests within your existing workflows.
  • An evaluation plan with test cases, accuracy thresholds and monitoring after launch.
  • An AI use policy and governance guidance for staff, if you do not have one.
  • Documentation, training and a runbook for the people who operate each solution.

Not included unless agreed separately

  • AI platform, model and cloud subscriptions, which you buy from the vendor.
  • Legal advice on privacy, employment or sector regulation of AI.
  • Fully automated decisions about individuals without human review.
  • Ongoing tuning and support after handover, unless agreed in writing.

What we will need from you

Most delays in this kind of work come from access and decisions, not from the technical build. Knowing these early keeps the project predictable.

  • A business owner for each use case who can judge whether outputs are good enough.
  • Access to representative documents or data, with personal information removed or protected as agreed.
  • Your privacy, security and data residency requirements, and input from your privacy lead where needed.
  • Access to the systems the solution will read from or write to.
Delivery

How the work is delivered

Each stage ends with something you can review before the next one starts.

  1. Assess opportunities

    Review candidate use cases with the people who do the work, and score them on value, data readiness, risk and cost.

    Output: Prioritized use cases and a recommended starting point.

  2. Prove the concept

    Test the chosen use case on your own data in a controlled environment, and measure accuracy against agreed thresholds.

    Output: Proof-of-concept results and a go or no-go recommendation.

  3. Design for production

    Define data handling, permissions, human review points, fallbacks, logging and integration with your systems.

    Output: Solution design and governance notes.

  4. Build and pilot

    Build the solution into your workflows, then pilot with real users and monitor accuracy, exceptions and feedback.

    Output: Piloted solution and evaluation report.

  5. Launch and monitor

    Roll out, train users, and set up monitoring so accuracy and costs are tracked and issues reach a named owner.

    Output: Live solution, runbook and monitoring.

Testing and handover

  • Each use case has written accuracy thresholds, tested before launch.
  • Low-confidence results are routed to a person instead of being passed through.
  • Knowledge assistants show their sources and only use documents the user is allowed to see.
  • Prompts, outputs and actions are logged in line with your retention and privacy rules.
  • A manual fallback lets work continue if the AI component is switched off.
  • Monitoring tracks accuracy, usage and running costs after launch.

What affects the cost

We do not publish package prices. Each estimate is based on an agreed scope, in Canadian dollars, with taxes shown separately. These are the things that move the number most:

  • The number and complexity of use cases.
  • The volume, variety and quality of documents or data.
  • Integration with ERP, CRM and other systems.
  • Accuracy targets and the amount of evaluation needed.
  • Privacy, security and data residency requirements.
  • Model and cloud usage costs, which grow with volume.

Questions buyers usually ask

Is our data used to train public AI models?

It should not be, and we design to prevent it. We use enterprise AI services whose terms exclude customer data from model training, or models hosted in your own environment, and we confirm each vendor's data handling terms with you before any real data is used.

Can the AI run in Canada?

Many AI services can be deployed in Canadian cloud regions, though model availability varies by region and vendor. Where residency matters we choose services and regions accordingly and document where data is processed.

How accurate will it be?

That depends on the task and your data, so we measure it rather than promise it. The proof of concept tests the use case on your own examples against thresholds you agree, and the design routes uncertain results to a person.

Where should we start?

With a contained, high-volume task where errors are easy to catch, such as extracting data from supplier invoices or answering staff questions from an approved policy library. Early success there builds the governance and confidence for harder cases.

Do we need an AI policy?

If staff already use AI tools, yes. A short policy covering approved tools, what data may be entered, human review and disclosure reduces risk quickly. We can help draft one for your legal and HR advisers to review.

Wondering where AI fits in your organization?

Describe the work you would like AI to help with and the systems involved. We will reply to arrange a conversation about whether it is a good fit and how to test it safely.

Discuss an AI use case