StarApple AICanada

StarApple AI, Canada

AI capability, built to a Canadian standard.

AI native, People First

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engage@starapple.ai

Seven questions

If one of these is yours, we have a practice for it.

CA·01  AI audits

Not seeing the ROI from your AI investment?

95% of enterprise generative AI pilots return nothing measurable to the P&L. MIT NANDA, State of AI in Business, 2025

What the audit unlocks

  • Every model and AI feature running in your organisation, including the ones nobody approved.
  • A risk rating per system, written against the Canadian instrument it answers to.
  • A costed 90-day plan with each item assigned to a named person.
4 to 6 weeks. See the full scope
Three colleagues reviewing a growth chart on a large monitor in an office
CA·02  Team AI training

Paying for licences your team has stopped opening?

70% of AI programme success comes down to people and process, against 20 percent for technology and 10 for the algorithm. BCG, the 10-20-70 rule

What the training unlocks

  • Every participant leaves with a working assistant built for their own role.
  • A prompt library the team owns and keeps adding to.
  • A usage policy your legal group can adopt as written.
Two 90-minute sessions. See the full scope
A facilitator presenting to a seated group working on laptops around a boardroom table
CA·03  Machine learning training

Hiring for skills your own analysts are one programme away from?

1 model per analyst, built, validated and deployed on your own data before the programme ends. StarApple AI Canada

What the programme unlocks

  • Analysts who already know SQL and Python finish able to ship without supervision.
  • A written capability assessment for each participant, so you can staff from evidence.
  • Work done on your data, which means the first project is already underway.
8 or 12 weeks. See the full scope
A software engineer reading code across two monitors at an office desk
CA·04  AI model development

Running a generic tool on a problem only you have?

40% of agentic AI projects are forecast to be cancelled by the end of 2027, most of them for unclear business value. Gartner, 2025

What the build unlocks

  • A model trained on your own history rather than somebody else's average.
  • Integration into the systems your staff already have open.
  • Documentation written to the standard internal audit and OSFI E-23 expect.
3 to 9 months. See the full scope
Two developers working at adjacent monitors, one screen showing application code
CA·05  AI safety and alignment

Could you prove your system refuses what it should?

#1 Prompt injection sits first in the OWASP Top 10 for LLM Applications, and ordinary QA does not catch it. OWASP

What the practice unlocks

  • Adversarial testing against your deployed system, with a written attack log.
  • An evaluation suite your team runs on every release, in your own CI.
  • A stated pass mark, so a failure blocks the build instead of prompting a debate.
6 to 10 weeks, then retained. See the full scope
A woman in glasses reading closely from a screen, lit by the display
CA·06  AI strategy

Funding a dozen pilots and shipping none of them?

30% of generative AI projects are abandoned after proof of concept. Gartner, 2024

What the roadmap unlocks

  • A costed two to three year plan naming which investments get funded.
  • The ones that get closed, which is usually the more valuable half of the document.
  • The conditions under which each judgement changes, so the plan survives contact with next year.
6 to 8 weeks. See the full scope
Two people mapping a plan across a whiteboard covered in diagrams and notes
CA·07  AI policy and governance

Could you show a regulator how a decision was made?

42001 ISO/IEC 42001 is the AI management system standard an auditor can certify you against, and the spine of what we build. ISO/IEC

What the framework unlocks

  • An intake gate wired into the release process you already run.
  • A named approver per system, and a form that takes twenty minutes.
  • Evidence you can hand to a regulator without assembling it first.
8 to 12 weeks. See the full scope
A large team seated around a boardroom table watching a colleague work through a whiteboard

The Canadian surface

Every report names the instrument it answers to.

Findings are written clause by clause, in your general counsel's own vocabulary, so they can go to a regulator without translation.

Federal AI legislation is still in motion. We design to the risk-tiering logic it shares with the EU AI Act and ISO/IEC 42001, which holds whichever bill passes.

What handover means

Every engagement ends with a named internal owner.

That owner receives the documentation, the monitoring setup, and enough training to retrain or retire the system without calling us. We stay on retainer where you want a second reader on model changes, and we say so in the statement of work rather than discovering it later.

The Toronto skyline and CN Tower seen across Lake Ontario at golden hour

How an engagement runs

Four stages, in this order.

The order is load-bearing. Building before the evidence stage is the most common reason an AI programme is defunded at the following budget cycle.

STAGE 01

Scope

We agree the business metric before the statement of work: a single number, the person who owns it, and the date it gets measured.

STAGE 02

Evidence

Data landscape, model inventory, how the work actually flows, and the regulatory surface you sit on. Findings are written for your board to read directly.

STAGE 03

Build

Training, models, guardrails or policy, built in your stack with your people in the room, against the evidence gathered in stage two.

STAGE 04

Handover

Documentation, monitoring, and the named owner from the band above, who runs it without us.

Background

Where the practice comes from.

Built in a thinner market

StarApple AI is the Caribbean's first AI company. It learned to deliver where data infrastructure is patchy, specialists are scarce, and a failed pilot is not something a client can absorb. Canadian engagements inherit that discipline.

Our own diagnostic models

Cognitive Debt, the Delegation Illusion and Automation Fragility are frameworks we developed for specific ways AI programmes fail. We use them in diagnosis and we publish them, so you can check the reasoning before you buy it.

Safety arrives as code

Red-teaming, evaluation harnesses and refusal testing are delivered as test suites your team keeps and runs in CI, with a written attack log and a stated acceptance threshold.

Start here

Tell us the decision you are trying to make.

A scoping call runs thirty minutes at no charge, and ends with us naming the practice that fits, or telling you that none of them does yet.

AI Solutions Manager, North America engage@starapple.ai

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