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AI consulting. Human perspective.

Intelligence,
put to work.

Turn AI possibility into business progress. We help you find the right opportunities, build practical solutions, and bring your people along.

Toronto roots. North American reach. A personal approach.

Built on experience.
Focused on what comes next.

Over 20 Yearsof enterprise experience

Strategy → deliveryone connected partnership

People first.technology with a purpose

Possibility, made tangible

Less friction.
More forward.

Start with the work that slows you down. Here are three ways an AI engagement could make it better.

Illustrative workflows, designed to start a conversation.

IN PRACTICE01 / 03

From another attachment to a clear next step.

The familiar friction

Orders arrive as PDFs and emails. Someone retypes the details, checks for missing information, and chases an approval.

A better way forward

AI prepares a structured draft, flags missing fields, and routes exceptions to the right person before a record reaches your business systems.

  1. 01PDFs & emails
  2. 02Extract & check
  3. 03Review & approve

Your team approves what gets posted. Exceptions stay visible.

Explore process automation
The human advantage
Wayne HolmesFounder, Holmes Computer Consultants
Meet your consultant

Technology changes.
The value of good
judgment doesn’t.

Behind every useful AI solution is someone who understands the business. We bring over 20 years of enterprise technology experience to your next chapter, with honest advice and the technical depth to deliver.

A different kind of AI partner
How we work

Start with a question.
Build toward an outcome.

Clear steps. Shared decisions. Progress you can see. Our approach keeps your business goals at the centre.

01

Understand the opportunity.

We start with your people, processes, and priorities. Together, we find where AI can solve a meaningful business problem.

02

Prove the value.

Test a focused use case with a working prototype. Measure results against an agreed baseline before committing to a broader rollout.

03

Build for the long run.

Integrate what works, train the people who use it, and establish the governance and support to keep improving.

Your world. Understood.

Different industries.
Distinct possibilities.

Your challenges aren’t generic. Your AI strategy shouldn’t be either. Explore what’s possible in your industry.

Your next move

A clearer picture.
A more confident decision.

All guides & resources
Free self-assessment

How ready is your business?

Explore your organisation’s AI readiness and get a tailored starting point. No technical expertise required.

Find your starting point
Interactive ROI calculator

What could AI mean for you?

Explore potential savings, costs, and payback periods with transparent estimates tailored to your business.

Explore your potential ROI
A little more clarity

Good questions.
Straight answers.

AI is a big decision. You deserve to understand it.

Ask us something else
How do I get started with AI consulting?

Start with a free AI readiness assessment. Contact us to schedule an initial consultation where we evaluate your current technology stack, data maturity, and business objectives. From there, we deliver a prioritized AI transformation roadmap specific to your industry and organizational context. No commitment required.

How much does AI consulting cost and what is the typical ROI?

AI consulting costs vary by scope: rapid cloud AI integrations can start in weeks for modest budgets, while custom private LLM deployments for enterprise are larger investments. Use our free AI ROI Calculator to project cost savings, payback period, and 3-year cumulative impact specific to your industry and organization size.

How long does AI implementation typically take?

Timeline depends on scope. A targeted AI pilot — such as automating a single workflow with a cloud API integration — can be deployed in 2 to 4 weeks. A comprehensive enterprise AI transformation covering multiple departments, and workforce training typically runs 8 to 16 weeks. Our phased approach starts with a focused use case and agreed success measures, then expands when the evidence supports it. Timelines and returns depend on your data, integrations, and scope.

Do you work with small businesses or only enterprises?

We work with organizations of all sizes. Small and medium businesses benefit from rapid cloud AI integrations that deliver immediate productivity gains with 2 to 5 week deployment timelines. Enterprises benefit from comprehensive transformation across multiple departments with custom infrastructure and organization-wide training.

How does your agency ensure our company's proprietary data remains secure during AI implementation?

We architect our custom AI solutions and LLM integrations specifically for enterprise data governance. We implement strict, localized AI models or enterprise-grade enclosed systems that guarantee your proprietary B2B data is never used to train public models.

What is included in the Corporate AI Workforce Training?

Our AI adoption training transforms your entire organizational stack, from C-suite executives to execution staff. We focus on advanced prompt engineering, automated workflow creation, and practical AI application to turn every employee into a massive force multiplier.

New to the language of AI? Start with the essentials.

Explore the AI glossary
Quick reference: AI terminology
AI (Artificial Intelligence)
Technology enabling computers to simulate human decision-making, pattern recognition, and autonomous execution.
LLM (Large Language Model)
The specific foundational AI engine — like ChatGPT, Claude, or Llama — customized and secured for your business data.
API (Application Programming Interface)
The secure software bridge linking your company's existing data (like Salesforce or SAP) directly into advanced AI models.
PIPEDA (Personal Information Protection and Electronic Documents Act)
A Canadian federal privacy law that protects personal information collected, used, or disclosed in the course of commercial activities and supports electronic commerce.
Prompt Engineering
The specialized discipline of giving an AI highly specific, structured instructions to guarantee accurate, business-ready outputs.
Generative AI
A branch of artificial intelligence that produces new content — text, code, images, audio, and structured data — rather than only classifying or predicting from existing inputs. Enterprise generative AI spans commercial cloud models (GPT, Claude, Gemini), open-weight models (Llama, Mistral), and custom fine-tuned deployments.
RAG (Retrieval-Augmented Generation)
An AI architecture pattern that connects large language models to proprietary data — internal documents, databases, and knowledge bases — so AI answers are grounded in actual business context rather than generic public training data.
ROI (Return on Investment)
The financial return generated from an investment, calculated as net profit divided by cost. In AI consulting, ROI measures time savings, error reduction, revenue impact, and cost avoidance from AI deployments.
GPT (Generative Pre-Trained Transformer)
A family of large language models developed by OpenAI, including GPT-4 and GPT-4o, widely used for text generation, analysis, and automation. One of several commercial LLM options alongside Claude and Gemini.
MVP (Minimum Viable Product)
The simplest functional version of a product built to validate an idea, test architecture, and secure stakeholder buy-in before committing to full-scale development.
ERP (Enterprise Resource Planning)
Integrated business management software — such as SAP, Oracle, or Microsoft Dynamics — that manages core processes including finance, HR, manufacturing, and supply chain. AI integration with ERP systems unlocks predictive analytics and process automation.
CRM (Customer Relationship Management)
Software platforms — such as Salesforce, HubSpot, or Dynamics 365 — used to manage customer interactions, sales pipelines, and marketing campaigns. AI-enhanced CRMs enable predictive lead scoring and automated customer communications.
HIPAA (Health Insurance Portability and Accountability Act)
A United States federal law establishing data privacy and security requirements for safeguarding medical information. Relevant for Canadian organizations handling US patient data or deploying AI in cross-border healthcare contexts.
SOC 2 (Service Organization Control 2)
A compliance framework developed by the AICPA that defines criteria for managing customer data across five trust principles: security, availability, processing integrity, confidentiality, and privacy. Critical for evaluating AI platform vendors.
POC (Proof of Concept)
A small-scale implementation designed to demonstrate that an AI approach is feasible and can deliver expected results before committing to full deployment.
BIM (Building Information Modeling)
A digital representation of the physical and functional characteristics of a building or infrastructure. AI-enhanced BIM enables predictive scheduling, automated clash detection, and intelligent resource allocation.
RFI (Request for Information)
A formal document used in construction and procurement to clarify project specifications, resolve ambiguities, and request additional details. AI can automate RFI processing, routing, and response drafting.
OEE (Overall Equipment Effectiveness)
A manufacturing KPI measuring the percentage of planned production time that is truly productive, combining availability, performance, and quality metrics. AI-driven OEE optimization identifies bottlenecks and predicts equipment failures.
MES (Manufacturing Execution System)
Software that monitors, tracks, and controls manufacturing processes on the factory floor in real time. AI integration with MES enables predictive quality control, automated scheduling, and real-time production optimization.
HACCP (Hazard Analysis and Critical Control Points)
A systematic approach to food safety that identifies, evaluates, and controls hazards throughout the food production process. AI-powered HACCP systems automate critical control point monitoring, deviation alerting, and compliance documentation.
CFIA (Canadian Food Inspection Agency)
Canada's federal agency responsible for food safety, animal health, and plant protection. CFIA enforces the Safe Food for Canadians Act and sets food safety standards that AI compliance systems help automate.
SFCA (Safe Food for Canadians Act)
Federal legislation strengthening food safety oversight in Canada by consolidating food inspection authorities, requiring traceability and licensing for food businesses, and enabling faster response to food safety incidents. AI traceability systems help meet SFCA requirements.
NLP (Natural Language Processing)
A branch of artificial intelligence focused on enabling computers to understand, interpret, and generate human language. NLP powers chatbots, document analysis, sentiment analysis, and voice-activated AI assistants.
TCO (Total Cost of Ownership)
The complete cost of an AI solution across its lifecycle, including licensing, infrastructure, integration, training, maintenance, and ongoing optimization — not just the initial purchase price.
SLA (Service Level Agreement)
A formal contract defining the expected performance standards, uptime guarantees, response times, and remedies between an AI platform provider and the client organization.
AIDA (Artificial Intelligence and Data Act)
The AI-specific portion of Canada's Bill C-27, targeting "high-impact" AI systems with obligations around risk assessment, mitigation, monitoring, transparency, and record-keeping. Substantive obligations closely parallel the EU AI Act's high-risk tier, meaning a single well-built control matrix can satisfy both regimes. Status and final scope have evolved with parliamentary amendments — check current ISED guidance.
RPA (Robotic Process Automation)
Hard-coded software bots that follow scripted steps — click, type, read fields — to automate repetitive desktop or web workflows. RPA is brittle against UI change and exception handling, which is the primary reason AI computer-use agents are increasingly displacing it for enterprise back-office automation.
OCR (Optical Character Recognition)
Technology that extracts text from images, scans, or screenshots. Modern AI vision models read documents semantically, often replacing classical OCR pipelines for invoice extraction, contract review, and form processing.
LoRA (Low-Rank Adaptation)
A parameter-efficient fine-tuning technique that adapts a base LLM by training small "adapter" matrices instead of full model weights. LoRA dramatically reduces fine-tuning cost, compute, and storage while preserving base-model capabilities — the standard recipe for customizing open-weight SLMs in enterprise deployments.
Llama (Meta open-weight LLM family)
Meta's family of open-weight large language models (Llama 3.1, Llama 3.2, and successors). Released under a community license with a 700M monthly active user threshold, widely used as a base model for self-hosted and fine-tuned enterprise deployments — particularly the smaller 8B class for SLM workloads.
vLLM (high-throughput LLM inference engine)
An open-source LLM inference and serving engine optimized for throughput on GPU infrastructure. Commonly used to self-host open-weight models (Llama, Mistral, Qwen, Phi) in private-cloud or on-premise deployments where data residency, cost at scale, or vendor independence matters.
Frontier LLM
The most capable large language models available at a given time — typically the flagship offerings from OpenAI, Anthropic, and Google. Used as the baseline against which cheaper or specialized alternatives (SLMs, fine-tuned models, open-weight models) are benchmarked before production deployment.
Let’s move forward

Your next chapter
starts here.

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