AI and data engineering for regulated industries: production LLM systems, data pipelines, and high-availability SaaS from Daniel Joye in Montgomery, Alabama.
Daniel helps teams in insurance, healthcare, and labor-union technology that need AI and data systems to work reliably in production, not just in a demo.
Typical problems: an LLM or analytics prototype that cannot be trusted in front of customers, a platform that has to stay up during peak demand, or a roadmap with no one senior enough to own delivery.
Every engagement starts with a free 30-minute evaluation call. You get a written plan within 48 hours, then work ships weekly. Book a free 30-minute evaluation
Services
Fractional Product Owner + Technical Program Leadership
Who it is for: Teams that need a senior product owner or technical program lead without a full-time hire.
The problem: Delivery is slipping, priorities change weekly, and executives cannot see risks until they become outages or missed dates.
- Clear roadmap tied to measurable outcomes
- Stable execution cadence for cross-functional delivery
- Executive-ready status, risks, and decision points
Deliverables
- Outcome map and KPI definitions
- Delivery plan with milestones, dependencies, and risks
- Operating cadence (planning, standups, reviews)
- Release readiness checklist and go-live plan
Project evidence
Boundaries: Daniel works alongside your team and leadership; he does not replace your executive decision-makers or take on people management of your staff.
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High-Availability Delivery and Release Governance
Who it is for: SaaS operators in regulated markets where downtime during peak periods is expensive.
The problem: Traffic spikes, such as insurance enrollment season, expose fragile releases and unclear operational readiness.
- Predictable releases with strong operational readiness
- Incident response and change management playbooks
- Capacity planning for peak load
Deliverables
- Release governance and change control
- Operational readiness gates
- On-call and incident response structure
- Post-release learning loop
Project evidence
Boundaries: Scope covers architecture review, release governance, and readiness practices. It is not a 24/7 managed-hosting or on-call service.
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AI and Analytics Program-to-Production
Who it is for: Insurance, healthcare, and union organizations with an LLM or machine-learning prototype they want to rely on.
The problem: The prototype impresses in a demo but has no tests, monitoring, or compliance review, so nobody will put it in front of customers.
- AI features aligned to workflow and adoption
- Model outputs tied to decisions and actions
- Measurement plan for lift and retention
Deliverables
- Use case definition with success metrics
- Data readiness plan
- Deployment and monitoring plan
- Safe rollout plan
Project evidence
Boundaries: Daniel builds and hardens the system with your team. He does not provide legal or regulatory sign-off; your compliance owners stay responsible for approvals.
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Union Local Analytics Value Pack
Who it is for: Union locals that want near-term analytics value from the member data they already hold.
The problem: Member churn, dues remittance, and contractor performance are tracked in spreadsheets with no early warning.
- Reduced remittance errors and faster financial review
- Proactive engagement driven by churn risk signals
- Contractor reliability insights for planning and partnerships
Deliverables
- Prototype roadmap for three ML use cases
- Feature definitions and data requirements
- Dashboard and workflow integration plan
Project evidence
Boundaries: Delivered as prototypes on your data. Production rollout is a separate, scoped follow-on.
Book a free evaluation