Custom models and practical team education
AI Training Toronto
Adapt AI systems to your business data and help your team use AI tools with informed judgment, practical skills, and clear operating boundaries.
Built around your environment
Two Complementary Paths to AI Training
Custom model training adapts models to specific tasks, terminology, and examples. WEBSITETOON assesses fine-tuning, retrieval-augmented generation, embeddings, classification, computer vision, and speech applications according to your data and requirements.Team AI training helps people understand AI capabilities and limitations, practice prompting, evaluate outputs, and apply tools to their roles. Programs can cover strategic planning, agent supervision, and AI security awareness.Model adaptation and team education can be pursued separately or together. Evaluation criteria, exercises, and program scope are tailored to the use case and participants rather than promising a particular quality improvement or productivity gain.Our services
Choose the right starting point.
Scope the services around your systems, data, business priorities, and review requirements.01
Language Model Fine-Tuning
Adapt suitable language models using domain-specific examples, documents, or conversations. Evaluate task performance against a baseline to determine whether fine-tuning is useful for the intended workflow.02
Retrieval-Augmented Generation
Connect AI applications to approved documents and knowledge bases through retrieval. RAG supplies relevant context at query time rather than necessarily retraining the model; retrieval quality, permissions, and answer grounding require evaluation.03
Embedding Models and Semantic Search
Select or adapt embeddings for semantic search over industry terminology and document structures. Evaluate retrieval relevance using representative queries and approved data sources.04
Classification Models
Train and evaluate models to categorize emails, support tickets, documents, or images using labeled examples and task-specific criteria.05
Computer Vision Training
Develop and evaluate object detection and image recognition models for defined business use cases using suitable image data and annotations.06
Voice and Speech Models
Assess speech recognition and text-to-speech adaptation for domain terminology and voice requirements. Use authorized recordings and evaluate performance for the intended speakers and conditions.07
Corporate AI Education
Provide practical instruction for executives, employees, power users, and technical teams. Select content according to roles, experience, approved tools, and organizational policies.- AI fundamentals: capabilities, limitations, and opportunities.
- Prompt engineering: clear instructions, useful context, examples, and output evaluation.
- AI tool practice: role-specific tasks and industry workflows.
- Agentic workflows: designing, supervising, and collaborating with agents.
- AI security awareness: sensitive data handling, prompt injection, access controls, and escalation.
- AI strategy workshops: identify opportunities and build an implementation roadmap.
A considered approach
Understand the details before you begin.
Review the capabilities, requirements, and limitations relevant to your engagement.Choosing Fine-Tuning, RAG, or Another Approach
Fine-tuning can help adapt task behavior or domain patterns, while RAG can make approved reference material available at query time. Prompting, embeddings, or a combination may be more appropriate. Compare approaches using representative tasks, data quality, cost, and operational constraints; no approach is automatically better for every use case.Executive AI Strategy
For leaders assessing the AI landscape, strategic opportunities, risks, and implementation priorities. Combine discussion of an AI roadmap with practical exploration of relevant tools.AI Fundamentals for Teams
Introduce capabilities and limitations, prompting basics, and hands-on exercises relevant to participants' roles. Include review of outputs and boundaries for using organizational data.Advanced Prompt Engineering
For power users and developers working on complex tasks. Cover structured instructions, few-shot examples, system prompts, task decomposition, and evaluation of prompt patterns.Agentic AI Workshop
Hands-on technical education in agent design, multi-agent coordination, tool use, memory, and operational considerations. Include permissions, supervision, escalation, and human approval before consequential agent actions.AI Security Training
Examine data privacy risks, prompt injection, sensitive information handling, usage policies, and relevant compliance considerations. Training supports informed decisions but does not itself guarantee security or compliance.Custom AI Training Programs
Tailor learning objectives and exercises to your industry, team skill levels, and AI goals. Discuss in-person or virtual delivery, multi-session learning, follow-up coaching, and support as part of the agreed program scope.Practical Applications
Use representative, authorized data and role-specific exercises to connect model development and team learning to business tasks.- Document and knowledge-base search with retrieval-grounded answers.
- Email, ticket, and document classification.
- Domain-specific drafting and response assistance.
- Object detection and image recognition.
- Speech recognition and text-to-speech adaptation.
- Supervised agent workflows and AI-assisted research or analysis.
Our process
A clear path from planning to review.
Agree on priorities, responsibilities, and measures before implementation.- 01
Data Assessment
Review available data, intended tasks, usage rights, quality, and gaps. Define requirements and a data preparation plan. - 02
Data Preparation
Clean, format, and structure data. Address sensitive information according to the agreed handling policy, consider edge cases, and create training and validation splits where appropriate. - 03
Model Selection
Compare suitable base models and approaches against performance, cost, speed, hardware requirements, and licensing constraints. - 04
Training and Evaluation
Fine-tune or configure the selected approach and evaluate it using agreed criteria and held-out examples. Compare results with the baseline and review limitations rather than assuming improvement. - 05
Deployment Planning and Configuration
Assess model compatibility with your own infrastructure or managed hosting. Define access controls, monitoring, scaling, and readiness checks before deployment. - 06
Continuous Improvement
Collect authorized feedback and additional examples, review performance, and evaluate proposed updates before release. Improvement depends on evidence and is not automatic.
Frequently asked questions