Evaluation infrastructure
Version criteria, evidence and findings so teams can act on them.
AI data and evaluation practice / Toronto
Build trustworthy AI and data systems for research-led technology in Toronto.
AI evaluation needs reproducible datasets, criteria and experiment records so promising research can become a system with defensible production behaviour. In Toronto, this connects directly to cloud platforms that support enterprise and product teams together.
What this practice covers
What AI Data & Evaluation covers
Production AI depends on data workflows and evaluation systems that make quality decisions repeatable, inspectable and useful to engineers.
AI Data & Evaluation priorities in Toronto
Canadian organizations often balance national scale, cross-border dependencies, privacy expectations and teams distributed across several regional technology centres.
Toronto teams often sit at the intersection of enterprise platforms, regulated information and fast-growing digital products. Strong engineering foundations make it possible to evolve each without creating separate operating silos.
Finance, technology, healthcare, media and professional services create strong demand for secure data use and reliable customer-facing systems.
AI evaluation needs reproducible datasets, criteria and experiment records so promising research can become a system with defensible production behaviour.
- Data evidence
- Governed data paths for financial, health and AI workloads
- Evaluation boundary
- Cloud platforms that support enterprise and product teams together
- Production decision
- Release and observability practices suited to services at scale
What that gives your team
- Training data with provenance
- Evaluation that can regress
- Feedback loops that improve
Training data pipelines
Prepare and transform text, image, audio and multimodal data for controlled use.
Annotation workflows
Design labeling, review, calibration and adjudication around quality criteria.
LLM and agent evaluation
Create rubrics, datasets and regression workflows for application behavior.
Human feedback
Capture comparisons, demonstrations and preference signals in structured form.
Data quality
Make disagreement, review outcomes and quality thresholds visible.
How it works
How AI data work moves
The workflow connects quality criteria to the people, data and engineering decisions that use the result.
01
Define
Translate the quality question into criteria, rubric and decision boundaries.
02
Build
Create datasets, annotation paths or evaluation runs with traceable versions.
03
Review
Calibrate reviewers, handle disagreement and inspect quality signals.
Engagement models
Choose how we work together
Engagements can be shaped around a bounded modernization goal, an embedded capability gap or a longer operating transition across Canadian stakeholders. For AI Data & Evaluation, the initial scope should stay anchored to cloud platforms that support enterprise and product teams together.
ConsultingADVISORY
Opinions, reviews, and focused direction.
ExploreStaff AugmentationEMBEDDED
Ongoing capacity in your engineering team.
On-Call SupportON-CALL
Incidents, rotations, and production response.
OutsourcingDELIVERY
Roadmaps with clear delivery ownership.
SolutionsPROJECT
Plan and deliver a defined technical outcome.
ExploreSupportCARE
Ongoing engineering care and improvement.
Questions answered
AI Data & Evaluation questions, answered
A short set of practical questions to clarify the first conversation.
How should research and experimentation workflows shape AI Data & Evaluation work in Toronto?
AI evaluation needs reproducible datasets, criteria and experiment records so promising research can become a system with defensible production behaviour. Scope should begin with the systems, owners and evidence connected to cloud platforms that support enterprise and product teams together.
Is this model training or data engineering?
The category focuses on the data, feedback and evaluation infrastructure around AI systems; model training is only in scope where it is part of that evidenced workflow.
Can you evaluate an existing LLM or agent?
The existing LLM and Agent Evaluation Systems service supports application-specific criteria, datasets, regression runs and actionable findings.
How do you handle reviewer disagreement?
Calibration, review and adjudication are explicit workflow concerns rather than hidden quality assumptions.
Is AI Data On-Call Support available?
No. The model is drafted for architectural completeness but remains nonpublic and nonroutable.
Next step
Frame the next AI data and evaluation step in Toronto.
Start with cloud platforms that support enterprise and product teams together and the technical or organizational boundary that makes it difficult today.