Build a dependable data product
Establish source freshness, grain, joins, and metric ownership. Make quality exceptions visible before the data enters a dashboard, model, or operational workflow.
Data & AI teams / CanadaBetter information.
Better information.
Intelligence you can evaluate.
Your team needs dependable sources, useful data products, and a way to judge AI beyond a promising demo. We connect data engineering, knowledge retrieval, and evaluation around the tasks and information boundaries you own.
Your role. Your priorities.Build around the work
Build around the work
your team owns.
Connect the right disciplines around a defined problem, then verify the result with the people responsible for it.
Make knowledge retrieval useful
Connect approved documents and records to source-linked answers. Define access scope, retrieval quality, and what the system should do when the evidence does not support an answer.
Evaluate models against the task
Build representative examples and failure cases. Compare correctness, groundedness, review effort, latency, and operating cost using the provider choices the buyer authorizes.
A practical first engagement
A data foundation or AI evaluation workstream.
Choose a data product or AI task with a clear owner. Inspect the sources, define representative acceptance examples, and build a first pipeline or evaluation that the team can reproduce.
- 01A source contract and quality exception record
- 02A working pipeline, retrieval layer, or evaluation set
- 03Reproducible checks and a decision-ready results report
Define success before deliveryAgree what a good
Agree what a good
result should show.
Use these as starting points for acceptance measures in a new engagement.
- Source freshness, completeness, and definition agreement
- Answer support and behavior on missing evidence
- Task quality, review effort, latency, and cost
A brief we can work withStart with the context
Start with the context
your team knows best.
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
Before we begin.
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.
Connect the next stepThe right capabilities.
The right capabilities.
The relevant context.
A good place to start
Discuss your projectLet’s define the first useful result.
Bring the workflow, the current constraints, and the decision or service your team needs to improve.