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Model + knowledge training · Train your AI

Your people are trained. Is your AI?

Give AI the organizational knowledge, context, tools and rules it needs to do useful work.

Train your AIWhat training AI means

The core idea

The goal is not to make a model know everything. It is to give the right system reliable access to the right organizational truth.

General-purpose AI models know a great deal about the world and almost nothing about your organization: your clients, products, policies, history, language or rules.

Closing that gap is rarely about training a new model. It is about structuring what your organization knows, connecting AI to it safely, and proving the result is reliable.

What “training AI” actually means

Use the lightest approach that works.

There are several ways to teach AI about an organization. They differ in cost, effort, flexibility and risk. Most useful systems combine the first few; heavier methods are used only when the evidence supports them.

  1. Instructions + context

    Clear instructions, examples and organizational context supplied to a capable model.

    Almost always

  2. Retrieval (RAG)

    The system looks up trusted, permissioned sources at the moment it answers, and can cite them.

    Most knowledge work

  3. Structured knowledge

    Entities, relationships and definitions modelled explicitly so answers stay consistent.

    Complex domains

  4. Tools + agents

    Controlled access to systems so AI can look things up and take bounded actions.

    Multi-step work

  5. Fine-tuning

    Adjusting a model on curated examples for consistent format, classification or domain language.

    When justified

  6. Pre-training a model

    Building a foundation model from scratch. Rarely the right answer for a single organization.

    Rarely

What we do

Everything AI needs to understand your organization.

Depending on the use case, model and knowledge training can include any of the following.

  • Organizational knowledge + data preparation

    Find, clean and curate the sources AI should rely on. Resolve conflicts, retire outdated material and decide which source is authoritative for which question.

  • Structured knowledge

    Model the entities, relationships and definitions your organization runs on, so AI answers consistently instead of guessing from loose documents.

  • Retrieval (RAG)

    Connect AI to trusted, permissioned sources at the moment it answers, with citations back to the source so people can check the work.

  • Model selection + customization

    Choose models that fit the task, data sensitivity, cost and hosting requirements. Customize with instructions, examples and configuration before reaching for heavier methods.

  • Fine-tuning, when justified

    Adjust a model on curated examples when a task needs consistent format, classification or domain language at scale, and when evaluation shows it beats simpler approaches.

  • Agents + tool access

    Give AI controlled access to the systems it needs to look things up and take bounded actions, with clear limits on what it can do without a person.

  • Permissions + source authority

    Make sure AI only sees what the person using it is allowed to see, and that sensitive information stays where it belongs.

  • Evaluation + feedback loops

    Build test sets from real questions and tasks, measure quality before and after changes, and capture corrections so the system improves over time.

Better AI needs better information

The model is only one layer of the system.

Reliable AI starts with structured information, explicit relationships, provenance, permissions and clear source authority. When those are missing, even the best model produces confident, inconsistent answers.

Read the guide
  1. 05AI that can be trusted

    Answers and actions grounded in the right organizational truth.

  2. 04Provenance + freshness

    Where an answer came from, and whether it is still current.

  3. 03Permissions

    Who and what can see each piece of information, carried through to AI.

  4. 02Entities + relationships

    Customers, products, policies and projects, and how they connect.

  5. 01Sources

    Documents, systems and data, with a clear view of which source is authoritative.

Where it applies

Common starting points.

  • Internal knowledge assistants

    Answer staff questions from policies, procedures and past work, with citations and the right permissions.

  • Client and service support

    Help teams respond faster and more consistently, grounded in current product, service and account information.

  • Document-heavy work

    Drafting, review, extraction and comparison across contracts, reports, submissions and correspondence.

  • Classification + routing

    Consistently categorize requests, cases or records so they reach the right person or process.

  • Agents that take bounded actions

    Look up records, prepare updates and trigger steps in systems, with people approving what matters.

  • Evaluation of existing AI

    Test an assistant or pilot you already have against real questions, and fix what is making it unreliable.

Built for Canada

Privacy, residency and control, decided deliberately.

Training AI on organizational information raises real questions about personal information, access, and where data is stored and processed.

We help you decide which information should be used at all, how permissions carry through to AI, and whether a workload needs Canadian-hosted or Canadian-controlled infrastructure. Many do not. Some clearly do.

Sovereign AIResponsible AI

Questions

AI model training FAQ

What does it mean to train AI on our organization?

It means giving an AI system reliable access to your organization's knowledge, context and rules. In most cases that involves structured knowledge, retrieval from trusted sources, careful instructions, tool access and evaluation. Fine-tuning a model is one option, used when it is justified, not the default.

Do we need to fine-tune a model?

Usually not at the start. Many organizational use cases are better served by retrieval and structured knowledge, because facts change and need to be cited. Fine-tuning is useful for consistent formats, classification or specialized language at scale. We recommend it only when evaluation shows it is worth the cost and maintenance.

What is RAG?

Retrieval-augmented generation (RAG) is an approach where an AI system retrieves relevant information from approved sources before it answers, and uses that information to ground its response. It keeps answers current and makes it possible to cite where information came from.

What is AI training data, in an organizational context?

For most organizations, 'training data' means the curated documents, records, examples and definitions an AI system relies on, whether retrieved at runtime or used to customize a model. Its quality, permissions and provenance matter more than its volume.

Can our data stay in Canada?

Often, yes. Many models and platforms can be deployed in Canadian regions or on Canadian-controlled infrastructure. Whether a workload needs Canadian residency or control depends on the data, obligations and risk. We help you decide and design accordingly.

Which models and platforms do you work with?

Trained is independent and does not resell a single platform. We recommend models and infrastructure based on the task, data sensitivity, hosting requirements, cost and your existing technology environment.

What should your AI know about your organization?

Tell us what you want AI to help with and where your knowledge lives today. We will help you find the lightest approach that works.

Train your AI