Where AI Helps a Business Today

Large language models have moved from demos to everyday tools. The useful question is no longer whether AI works, but which of your tasks it does well enough, at an acceptable cost and risk. Typical candidates:

  • Documents: drafting, summarizing and translating proposals, reports and correspondence.
  • Customer support: assistants that answer from your own documentation and hand off to a person when they should.
  • Data extraction: reading emails, forms, invoices and PDFs and entering the data into your systems.
  • Sorting and routing: classifying requests, tickets or leads and sending them to the right person.
  • Internal search: asking questions across your policies, procedures and past projects.
  • Software development: coding assistants and review tools for your development team.
  • Agents: multi-step workflows where AI uses your tools, under rules you set.

Our Approach

  1. Start from the process, not the tool. We look at how the work is done today and measure it, so there is a baseline to compare against.
  2. Prototype small. A focused proof of concept on your real data, in days or weeks rather than months.
  3. Evaluate honestly. We test on realistic cases, including the hard ones, and report where the model fails as well as where it succeeds.
  4. Keep people in the loop. Where an error is costly, a person approves the output before it goes anywhere.
  5. Ship and monitor. Production integrations with logging, cost tracking and a way to improve over time.

What We Deliver

  • AI opportunity assessment: a workshop with your team and a written report of the use cases worth pursuing, ranked by value, effort and risk.
  • Proofs of concept: a working prototype that answers "does this work for us?" before you commit a budget.
  • Production integrations and agents: AI connected to your applications and data through the APIs of providers such as Anthropic, OpenAI and Google, or self-hosted models when data must stay in-house.
  • Training and usage policies: practical guidelines so your team uses AI tools productively and safely.

Data, Privacy and Risk

Sending business data to an AI provider is a decision to make deliberately. We document which data goes where, choose plans and settings where your data is not used to train models, restrict access to what each integration needs, and take your obligations under Québec's Law 25, PIPEDA or the GDPR into account. To limit wrong answers, we ground models in your own sources, show where an answer came from, and design a fallback for when the model is unsure.

We Use AI in Our Own Work

Morébec uses AI assistants and agents every day, from writing and reviewing code to running our infrastructure. What we recommend is what we have seen work in practice, along with its limits.

Frequently Asked Questions

Do we need a lot of data to get started?

Usually not. Most business use cases start with documents and systems you already have and with existing models. Training a custom model is rarely the first step.

Will AI replace our employees?

In most projects, the goal is to take repetitive work off people's plates so they can spend more time on work that needs judgment. We design around the team you have.

Is our data used to train AI models?

That depends on the provider and the plan. We select options where business data is not used for training, and we tell you exactly what is sent where.

Can you work with our developers?

Yes. We can lead the project, or advise and review while your developers build. See software development consulting.