Product Data Intelligence
30,000 products · 1.5M attribute records
An AI-assisted product-data workflow combining model recommendations, deterministic validation, confidence thresholds, and human review.
View the Product Data Intelligence projectAtlantic Canadian Applied AI
Enterprise AI that improves real work.
The AI Guys designs and deploys practical AI systems for organizations that need to modernize documents, data, decisions, and workflows without building a large internal AI team.
Founder-led delivery across enterprise AI, data science, data engineering, automation, optimization, and decision intelligence.
- 01 AI agents
- 02 Document intelligence
- 03 Data engineering
- 04 Workflow automation
- 05 Decision systems
- 06 AI governance
From opportunity to production
The model is only one part of the system.
Useful enterprise AI depends on trusted data, reliable integrations, explicit business rules, appropriate human oversight, and a workflow people can actually adopt.
We design the full operating system around the AI component, from opportunity assessment and architecture through implementation, evaluation, and operational handoff.
Capabilities
What we build
Practical systems designed around the way information, decisions, and work move through an organization.
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01
AI Strategy and Opportunity Assessment
Identify high-value use cases, assess data and organizational readiness, estimate expected value, and define a practical implementation roadmap.
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02
Enterprise Agents and Knowledge Systems
Build governed assistants that retrieve trusted organizational knowledge, explain procedures, coordinate tools, and help employees determine the appropriate next action.
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03
Intelligent Document Processing
Extract, classify, validate, and route information from invoices, forms, shipping records, contracts, and other document-heavy workflows.
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04
Data and AI Engineering
Connect models with APIs, cloud platforms, data pipelines, semantic models, databases, and the systems where operational work already happens.
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05
Decision Intelligence
Apply forecasting, optimization, simulation, economic modelling, and machine learning to resource allocation and complex operational decisions.
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06
Governance and Operationalization
Establish evaluation, controls, human review, security boundaries, monitoring, and support practices required for responsible production use.
How we work
Start with the business decision, not the model.
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Define the operational problem
Clarify the workflow, users, constraints, desired decision, and measurable outcome.
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Assess feasibility and value
Evaluate data, integrations, risk, implementation effort, expected return, and whether AI is the appropriate method.
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Build and evaluate
Develop a focused solution with explicit quality criteria, deterministic controls, and human review where consequences matter.
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Operationalize
Integrate the system, support adoption, monitor performance, and improve it using real operational evidence.
Technology in context
Built for real enterprise environments
We select technology based on the operational problem, existing architecture, cost, control requirements, and long-term supportability.
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Microsoft ecosystem
Substantial experience across Microsoft Fabric, Azure AI services, Copilot Studio, Power Platform, and Power BI.
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Data and integration
APIs, Python, SQL, semantic models, databases, pipelines, and cloud data platforms connected to operational systems.
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Controls and evaluation
Permissions, deterministic validation, quality thresholds, human review, monitoring, and source-of-truth checks.
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Supportable architecture
Technology choices grounded in maintainability, cost, security boundaries, adoption, and the client’s existing environment.
The team
Founder-led by practitioners who build.
The AI Guys combines enterprise AI strategy, applied data science, economics, data engineering, system integration, and quantitative decision modelling. Senior practitioners remain directly involved from discovery through delivery.
Michael Simpson
Enterprise AI and Data Science
Applied AI, data science, economics, optimization, simulation, and decision-support leadership.
Read Michael’s profileLogan Stackhouse
Data Engineering and AI Systems
Data pipelines, cloud architecture, APIs, databases, integration, automation, and production foundations.
Read Logan’s profileHave an operational problem that may be suitable for AI?
Tell us about the process, document workload, data challenge, or decision you are trying to improve. We will help determine whether AI is appropriate and what a practical first step could look like.