AI Use-Case Intake and Governance
Structured intake, duplication detection, value assessment, risk classification, and routing of AI opportunities to the appropriate delivery pathway.
Selected work
Applied AI and decision systems built around real operations.
The examples below reflect real projects led or delivered by our founders in enterprise and consulting environments. Names, screenshots, and proprietary implementation details have been generalized where appropriate.
These examples demonstrate founder delivery experience. They do not imply that the organizations behind the work are customers of or endorse The AI Guys.
Retail and product data
AI-assisted enrichment and validation across approximately 1.5 million product-attribute records.
Context
A large product catalogue contained inconsistent and incomplete attribute data across approximately 30,000 products. Improving the data manually would have required extensive repetitive effort and would not have produced a scalable quality-control process.
What was built
A founder-led team designed and delivered an AI-assisted workflow that generated attribute recommendations, applied deterministic validation rules, assigned confidence thresholds, preserved the latest approved version, and routed uncertain cases for human review.
Technical pattern
- Generative AI for proposed values
- Deterministic schema and business-rule validation
- Confidence scoring and review gates
- Workflow automation
- Structured writeback and export
- Cost-aware model selection
- Human oversight for material exceptions
Outcome
The solution reached production in fewer than 200 delivery hours and supported more than $700,000 in realized and expected business value. The design converted a large one-time cleanup effort into a repeatable product-data operating process.
~30,000
products
~1.5M
product-attribute records
<200
delivery hours to production
>$700K
realized and expected value
Finance and enterprise data
Natural-language access to high-volume invoice and operational information.
Context
Operational leaders needed faster access to invoice and property-related information spread across enterprise data, documents, and systems. Traditional reporting could answer known questions but was less effective for varied, conversational follow-up.
What was built
The team developed a governed pattern combining enterprise resource planning data, document extraction, APIs, cloud notebooks and pipelines, semantic modelling, and an AI-facing interaction layer.
Technical pattern
- ERP and API integration
- Lakehouse and pipeline architecture
- Semantic models
- Natural-language query
- Governed AI-generated summaries
- Role-aware access patterns
- Validation against source-of-truth fields
- Cost and latency optimization
Outcome
The work established reusable architecture for AI-assisted access to hundreds of thousands of financial and operational records, while retaining structured enterprise data as the authoritative source.
Documents and workflow
Extracting and validating operational data from thousands of document pages.
Context
High-volume operational documents required employees to locate and re-enter multiple fields before downstream processing could begin.
What was built
A document-intelligence proof of concept combined OCR, custom extraction models, deterministic validation, exception handling, and integration planning for enterprise systems.
Technical pattern
- OCR and document intelligence
- Custom field extraction
- 20+ required operational fields
- Confidence-aware validation
- Deterministic joins to enterprise records
- Exception routing
- Human review
- Comparative assessment against incumbent tools
Outcome
The solution pattern demonstrated how document extraction could reduce repetitive review while preserving human oversight for low-confidence and material exceptions.
Public-sector decision support
Simulation and forecasting for workforce capacity and service allocation.
Context
Decision-makers needed to understand how staffing levels, caseloads, demand, wait times, and service allocation interacted under different operational scenarios.
What was built
Michael assumed technical ownership of a workforce-capacity decision platform that combined agent-based simulation, forecasting, scenario planning, and an interactive web interface.
Technical pattern
- Agent-based simulation
- Staffing and caseload forecasting
- Wait-time analysis
- Service-demand modelling
- Scenario comparison
- Interactive decision support
- Ongoing model maintenance and assumption refinement
Outcome
The platform enabled decision-makers to compare alternative staffing and service-allocation strategies using a structured analytical framework rather than relying solely on static estimates.
Economics and advanced analytics
An automated platform for measuring employment, GDP, output, and impact per invested dollar.
Context
Economic impact reporting across multiple creative sectors involved complex data transformation, repeated analysis, and a material risk of inconsistency or double counting.
What was built
Michael led the design and implementation of an automated economic impact assessment framework integrating Python, analytical workflows, visual reporting, forecasting methods, and economic logic.
Technical pattern
- Python
- Alteryx and structured workflow automation
- Tableau and interactive outputs
- Forecasting
- Economic impact modelling
- Sector and regional logic
- Double-counting controls
- Repeatable reporting
Outcome
The platform improved the efficiency, consistency, and scalability of impact reporting and supported publicly released analysis of employment, gross domestic product, and economic output.
Related experience
Additional solution patterns
These are delivery patterns our founders can apply to new client contexts, not a catalogue of pre-packaged startup products.
Enterprise Knowledge and Process Agents
Governed assistants that retrieve approved procedures, explain next steps, and coordinate tools without bypassing permissions or source-of-truth controls.
Optimization and Simulation
Quantitative systems for pricing, resource allocation, operational planning, forecasting, and scenario evaluation.
AI Delivery Operating Models
Practical delivery frameworks covering intake, value framing, solution design, validation, approval, launch, monitoring, and continuous improvement.
The next project should begin with your operating context.
We can help define the decision, assess the available evidence, identify constraints, and determine whether a focused AI pilot is justified.