Build a data product your team can check
Agree source freshness, record detail, joins, and metric ownership. Expose quality issues before records enter a dashboard, model, or workflow.
Data & AI teams / CanadaBuild reliable data tools
Build reliable data tools
and test AI on real tasks.
We build data pipelines, reporting tools, and search systems around the information your team uses. Test AI on real tasks so you can see where it works, where it fails, and what it costs to run.
Projects for your teamWhat we can
What we can
help you do.
Start with a task your team needs to make easier. Build the right tools and check the result together.
Find answers in approved knowledge
Connect documents and records to answers with source references. Test retrieval, permissions, and the response when sources do not support an answer.
Compare models on your actual work
Use representative examples and failure cases. Compare accuracy, source support, review effort, response time, and cost with the providers your team approves.
A practical first engagement
Build one data pipeline or AI evaluation
Choose a data product or AI task with an owner. Inspect the sources, agree test examples, and build a pipeline or evaluation the team can run again.
- 01A source contract and quality exception record
- 02A working pipeline, retrieval layer, or evaluation set
- 03Reproducible checks and a clear results report
Define success before deliveryHow we measure
How we measure
success.
Agree these measures before a new project begins and use them to review progress.
- Source freshness, completeness, and definition agreement
- Answer support and behaviour on missing evidence
- Task quality, review effort, response time, and cost
A brief we can work withTell us about
Tell us about
your project.
Share a general description of the work. Scope, timing, access, and commercial terms are agreed together.
- The data product or AI task to support
- The approved sources and access boundaries
- Representative examples and known failure cases
- The decision and acceptance thresholds to inform
Common questions
Can you compare different models and providers?
We can scope a comparison on the task and examples the team cares about. Provider selection, information handling, cost assumptions, and evaluation criteria are agreed before the experiment; a generic benchmark does not replace task-specific evidence.
When is synthetic data useful?
It can help explore missing scenarios or create controlled evaluation examples. Keep generated examples distinguishable from observed records, document their assumptions, and validate any claimed improvement on appropriate held-out work.
Explore related workRelated services
Related services
and industries.
Discuss a project
Discuss your projectLet’s plan your first improvement.
Tell us what takes too much time, which tools you use, and what a better result would look like.