AI & Marketing · Consulting
AI Implementation & Automation: Practical AI for Companies That Actually Have to Ship
Self-hosted models, real workflows and measurable hours saved. No hype, no per-token surprise bills.
The problem
What this usually looks like
Everyone says you should be using AI. Nobody says what for. Meanwhile your team is pasting confidential customer data into a public chatbot, you are paying for tools nobody uses, and the actual repetitive work, quotes, follow-ups, reports, documentation, is still being done by hand.
How I approach it
Start from the work, not the technology. I find the repetitive, high-volume, low-judgement tasks in your business, automate those specifically, and where the data is sensitive I run the models locally on your own hardware so nothing leaves the building. Then I train your team to use it so the tools do not sit idle.
Scope
What I actually do
Audit your workflows to find where AI actually saves hours, and where it would just add risk and noise.
Deploy local, self-hosted LLMs using Llama.cpp, Ollama and quantized open models so confidential data never goes to a third-party API.
Build retrieval-augmented (RAG) knowledge bases over your own documents, procedures, product specs, pricing, contracts.
Automate quoting, proposal drafting, follow-up sequences, meeting notes, reporting and content production.
Integrate AI into CRM and marketing automation so lead enrichment, routing and follow-up happen without human effort.
Write the AI usage policy, what staff may and may not put into which tools.
Train the team, in plain language, so adoption actually happens.
Deliverables
What you get
- AI opportunity audit with estimated hours saved per workflow
- Deployed local model stack on your hardware
- RAG knowledge base over your documents
- Automated workflows in your existing systems
- Written AI usage and data-handling policy
- Staff training sessions and quick-reference guides
Outcomes
What changes
- Hours returned to the team every week
- Confidential data stays in-house
- No runaway per-token API costs
- Staff who actually use the tools
Engagement
How we work together
AI opportunity audit
Build-and-deploy implementation project
Ongoing AI operations retainer
AI adoption training and workshops
Questions
Straight answers
Why run AI locally instead of just using ChatGPT?
Three reasons: confidentiality, cost and control. Local models on your own hardware mean customer data, pricing and contracts never leave your building, there is no per-token bill that scales with usage, and the tool cannot change or disappear under you.
We are not a tech company. Is this relevant to us?
Especially then. The highest-return AI work is unglamorous, quotes, follow-ups, documentation, reports, dispatch notes. Trades, logistics, manufacturing and distribution businesses have more of that work than tech companies do.
Will this replace our staff?
It removes the repetitive work they already resent. Every engagement I have run has meant the same people handling more volume, not fewer people.
Related
Services that usually go with this
Logistics & Supply Chain Consulting
Twenty years on the board, not twenty slides about best practice.
Marketing, SEO & Demand Generation
Marketing built by someone who has to live with the leads it produces.
Custom AI Agents & Assistants
An agent earns its place when it completes a task end to end and hands a person something they can check.
Private & On-Premise AI Deployment
If the data cannot leave the building, the model has to come to it.
Hiring instead of contracting? See the full-time roles, or the ATS-formatted resume.
Contact
Tell me what you are hiring for
- Call: +1 519 278 5085
- Email: tim@timarmstrong.ca
- Based: London & Southwestern Ontario, Ontario, America/Toronto (Eastern Time)
- Availability: Available nationally across Canada, on-site, hybrid or remote
- Serving: London · Stratford · Woodstock · Kitchener: Waterloo · Cambridge · Guelph · Brantford · Sarnia · Chatham-Kent · Windsor · Toronto · Mississauga and Canada-wide