Use case and measurement
One clear task, the people it helps and simple criteria for judging quality and value.
AI integration for products and operations
Integrate AI where it creates a useful result you can control.
We help you choose one clear use case. Then we connect AI to your product, data or tools without taking control away from your team.
Discuss your project
Your AI integration project
You know where AI could help, but the key choices still need to be made.
You do not need to choose a model or vendor before speaking with us. We start with the expected use and the rules it must follow.
Questions to decide
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Which task or feature should become easier?
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Which data can the AI use?
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Which answers need a person to review them?
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How will you measure quality, time saved or use?
Our method
Four steps from an AI idea to a reliable use.
Each step answers a simple question before the project grows. You see what works, what needs review and what the AI costs to use.
- Choose
Define the expected result
We choose one task, the people who use it and a clear sign that shows whether the AI truly helps.
- Prepare
Connect the right data
We check the allowed data, its quality and the tools to connect. Sensitive information stays protected.
- Test
Compare the answers
We test the AI with real examples. We find errors, edge cases and answers that need a person to review them.
- Launch
Track use and cost
We release the feature with tracking for errors, cost and results. Those facts guide the next decision.
What we build
A first AI integration that completes a real journey.
We build the feature around the task to complete. Every element supports result quality, data safety or control for your team.
Use and data
Useful, allowed data
The sources to use, access rules and information that must never be sent to the AI.
Feature and tools
AI feature in the product
An assistant, search, writing aid or other feature placed in the right screen.
Connections to your tools
The AI can read or send information in your tools when their access rules allow it.
Control and tracking
Tests and human review
Real examples to check the answers and a human step when the risk calls for it.
Tracking after launch
Errors, costs and important results become visible so improvements follow evidence.
Target outcome
A useful AI feature tied to real work and clear enough to evaluate.
The right people can use it in their journey. Your team can review answers, track costs and decide whether the feature should grow.
If the main need is to create the screens and system that support this feature, explore custom web application development .
Explore software development services that can carry an AI capability.
AI integrated into a real product
Pikoo: using AI to prepare quizzes in a mobile app.
Pikoo brings together a family mobile app, the data needed for quizzes and AI tools that help prepare the content.
Mobile application Pikoo
- Situation
- The product needed to offer quizzes made for families in Quebec through a simple mobile experience.
- Decision
- Connect AI tools to the system that prepares quizzes while keeping the answering journey clear in the app.
- Outcome
- A working mobile app with a base ready for new quizzes and activities.
Frequently asked questions
Questions before integrating AI into a product or your operations
Simple answers about price, timing, data, quality, ownership and support after launch.
How much does AI integration cost?
Cost depends on the use case, data, tools being connected and required controls. We first choose one small, useful feature, then estimate the work and running cost.
How long does it take to add an AI feature?
Timing depends mainly on data quality, connections and testing. A focused first feature can be tested before a wider integration.
Which AI use case should we start with?
Start with one clear task whose result can be checked. It should happen often enough or matter enough for the value to become visible.
Can you add AI to our current product?
Yes, if the product can provide access to the needed data and actions. We first check the code, current tools and required changes.
Will our data be used to train a public model?
That depends on the chosen service and its settings. We check its data rules before integration and limit shared data to what the feature needs.
How do you reduce wrong AI answers?
We limit the task, provide useful context and test with real examples. A person reviews the result when an error could have an important effect.
Can we choose the AI provider?
Yes. The choice depends on expected quality, cost, speed, data location and your company’s rules.
Who owns the code and data?
Ownership of the code, data and accounts is defined in the contract. The AI provider’s terms are also checked before launch.
How do we track cost and quality after launch?
We can track request volume, cost, errors and important results. This data shows where to improve the feature and whether it should grow.
Can you maintain the integration after launch?
Yes. We can fix issues, adapt the feature when the provider changes and improve answers based on observed use.
First conversation
Let’s choose the first AI use worth testing.
Tell us about the task, product or tools involved. We will help define the data, risks and a first release that can prove its value.