AI & Automation.
Microsoft Copilot, ChatGPT, Claude, and other AI rollouts. On-prem AI for sensitive data.
01Overview
C:\HOWL\SOLUTIONS\SERVICES\AI-AUTOMATION\AI is the highest-leverage technology investment most small and mid-size businesses can make right now, and the easiest to spend money on without getting anything back. We work with both cloud and on-prem AI. Cloud AI (Microsoft Copilot, OpenAI, Anthropic) is the right answer for most workflows, with higher capability and lower cost. For clients with data sovereignty, regulatory, or IP concerns, we deploy AI stacks on hardware they own, where the data stays in the building.
TWO WAYS TO RUN AI ┌───────────────┐ ┌───────────────┐ │ CLOUD │ │ ON-PREM │ │ copilot, gpt, │ │ your hardware │ │ claude │ │ your data │ └───────┬───────┘ └───────┬───────┘ │ │ most workflows sensitive data │ │ └─────────┬──────────┘ ▼ the right tool per job // capability where it pays. // sovereignty where it matters.
02Copilot adoption & training
C:\HOWL\SOLUTIONS\SERVICES\AI-AUTOMATION\COPILOT\Most businesses with Microsoft 365 Copilot licenses don’t actually use them. The license-to-utilization gap is the biggest source of wasted AI spend in Canadian SMB. We close it.
What’s included
- Readiness assessment (data permissions audit, SharePoint sharing review, licensing alignment)
- Pilot group selection and use case mapping
- Role-based prompt training for sales, finance, operations, and admin functions
- Governance and data oversight (preventing data oversharing, audit trails, conditional access)
- Adoption measurement and follow-up training
LICENSED vs USED seats paid ████████████████████ seats used ████████████████████ │ └── the gap is the wasted spend readiness ──▶ pilot ──▶ training ──▶ governance // adoption is the product, not seats.
03AI workflow automation
C:\HOWL\SOLUTIONS\SERVICES\AI-AUTOMATION\WORKFLOW-AUTOMATION\Power Automate, Copilot Studio, and the orchestration work that turns AI from a chat tool into an actual workflow engine.
What’s included
- Power Automate flows tied to business processes
- Copilot Studio custom agents (purpose-built assistants for specific workflows)
- Integration with Microsoft 365, Dynamics, and third-party tools via connectors
- Approval chains, audit logging, and human-in-the-loop controls
- Documentation of every flow: trigger, transform, destination, error path
FROM CHAT TOOL TO WORKFLOW ENGINE ┌───────────┐ ┌─────────────────┐ │ CHAT TOOL │ ──▶ │ WORKFLOW ENGINE │ └───────────┘ └────────┬────────┘ │ intake ──▶ approve ──▶ file ──▶ notify people handle exceptions, not routing // the same request handled the same // way, every time.
04Custom AI workflows
C:\HOWL\SOLUTIONS\SERVICES\AI-AUTOMATION\CUSTOM-AI-WORKFLOWS\When the workflow you need doesn’t fit Power Automate or Copilot Studio, we build it. Document processing, classification, summarization, routing, using cloud LLMs, on-prem models, or both.
What’s included
- Document processing and structured data extraction
- Classification, summarization, and routing pipelines
- Hybrid cloud-and-local workflows where sensitivity rules dictate the mix
- LLM integration into existing business tools, with attention to cost, latency, and data governance
- Source code, deployment scripts, and operational documentation handed over at the end
DOCUMENTS IN, DECISIONS OUT docs ──▶ [ EXTRACT ] ──▶ [ CLASSIFY ] │ ┌────────┬────────┘ ▼ ▼ SYSTEM A SYSTEM B cloud, on-prem, or both, per sensitivity // built when the off-the-shelf // flow does not fit.
05On-prem AI
C:\HOWL\SOLUTIONS\SERVICES\AI-AUTOMATION\ON-PREM-AI\Some businesses can’t or shouldn’t send their data to a cloud AI service. Legal firms with privileged client matter. Medical and dental practices under PIPEDA. Engineering firms with proprietary designs. Anyone whose IP, regulations, or risk profile says the data stays in the building. We deploy open-weight large language models (Llama, Qwen, Mistral) on hardware you own, sized to the models you actually need to run. The right platform depends on the deployment. We build on NVIDIA DGX Spark, AMD Strix Halo, Apple Silicon, or a dedicated GPU server, and the shape stays the same. A chat interface for your team, optional document search over your own files, and the same productivity gains as cloud AI, without the data leaving your network. Most run quietly on a wall outlet, with unified memory sized to the model (up to 512GB on the largest), and none of the cost or infrastructure of a server room.
What’s included
- Hardware specification and procurement, sized to your workload (DGX Spark, Strix Halo, Apple Silicon, or GPU server)
- Model selection based on workload (general chat, document analysis, summarization, coding)
- Inference platform setup (Ollama, vLLM, or MLX depending on hardware) with multi-user access
- Chat interface deployment (Open WebUI) tied to your identity system where supported
- Optional document search over your own files
- Operational documentation, model update procedures, and runbooks handed over at the end
THE BUILDING BOUNDARY ┌──────────────────────────────┐ │ YOUR HARDWARE │ │ ┌────────┐ ┌──────────┐ │ │ │ MODEL │◀──▶│ YOUR │ │ │ │ local │ │ DATA │ │ │ └────────┘ └──────────┘ │ └──────────────┬───────────────┘ │ nothing crosses the wall // chat + document search for your // team. runs on a wall outlet.