AI strategy becomes
production software.
aiwillnet.com architects and builds, hands-on, the data platforms and multi-agent AI systems that regulated enterprises need to move past experimentation, then hands off working software, not slideware, to the teams who scale it.
Where we do the work ourselves.
Every engagement below is something we architect and build ourselves, not just advise on.
AI strategy & governed deployment
Roadmaps that end in a running system, built for HIPAA/PCI-grade governance from day one, not bolted on after a pilot succeeds.
Multi-agent AI systems & RAG
Custom agent frameworks and retrieval-augmented generation (RAG), plus AI-augmented application development on platforms like Azure AI Foundry, specified, built, and deployed into production, not left as a prototype.
Enterprise data architecture
Data platforms across Azure and AWS, from data lakes and Delta Lake/Databricks lakehouses to dbt-modeled warehouses, master data management, and system integration, designed to hold up under real regulatory and audit scrutiny.
Fractional technical leadership
Player-coach engagement: we sit with your team, write code alongside them, and back it with training, documentation, and coaching, not slide decks in a separate room.
Healthcare & regulated data
HL7 FHIR interoperability, compliance-aware pipelines, and AI systems built for organizations that cannot afford to get this wrong.
Multi-cloud & DevOps modernization
Azure and AWS migration, CI/CD, and platform hardening that turns a fragile legacy estate into something a small team can actually run, on whichever cloud the org already stands on.
Strategy to execution, specified
Agile delivery, hands-on project management, and DevOps/CI-CD best practices, with work broken into executable specifications so vision turns into shipped software on a schedule, not a roadmap slide that never closes.
Business intelligence, multi-industry
Dashboards, KPI frameworks, and reporting layers built on the same production data platform, proven across healthcare and construction, not a healthcare-only specialty. See industries served →
One production data management application suite, grown module by module: patient and entity identity (EMPI/MDM), reference data (MRM), integration, and security, plus the modules a suite like that is always still missing until someone builds them, metadata-driven architecture and AI-assisted data-quality auto-correction among them. Governance sits at the center of all of it, with AI governance built into the framework from the start, not bolted on after the fact.
See the full integration & AI-governance architecture, diagrammed →
Five stages, one person accountable throughout.
The same sequence every engagement runs, in order, start to handoff.
Discover
Audit the current data estate, workflows, and where AI actually earns its keep.
Architect
Design the platform and agent architecture against your real governance constraints.
Build
Write the production code ourselves, alongside your engineers, in your environment.
Deploy
Ship into production with monitoring, guardrails, and documentation in place.
Hand off
Transfer ownership through training, documentation, and hands-on coaching, so your team can run and extend it without us.
"At a healthcare enterprise, we identified that the data organization had grown overly dependent on a large, consultant-heavy team of roughly twenty-five, and led a three-year phased transformation down to a lean, high-leverage core team of six, staying ahead of real budget pressure the entire way. To absorb the resulting workload without losing delivery capacity, we built a production multi-agent AI framework, eleven specialized agentic personas working with retrieval-augmented generation, that took over the work the departing consultants had done."
- Name
- Thierry Willner
- Base
- Omaha, NE
- Focus
- Data architecture, multi-agent AI, regulated industries
- Mode
- Hands-on, player-coach
- Sectors
- Healthcare, construction, public sector, manufacturing
- Platforms
- Azure & AWS (multi-cloud)
A pull toward technology started early, hands-on with computers well before it became a career, building a first database application at eighteen. That path moved through accounting and finance in before landing in IT as a hands-on developer and consultant, drawn to the concrete problem of building systems that actually work rather than just advising on them.
Over twenty-plus years we've built and led data architecture, business intelligence, and now AI engineering functions across healthcare, construction, and financial services, consistently as a player-coach who codes alongside the team rather than directing from a distance. aiwillnet.com is where we bring that same hands-on, production-first discipline to organizations ready to move past AI experimentation into real, deployed, regulated-environment scale.
Most consultants can design the strategy and hand you a deck. We stay in the code, from the agent framework to the full-stack application, because we only ask a team to run what we've already proven we can build.
Let's build something real.
If your AI initiative needs to become a running system inside a regulated environment, we'd like to hear about it.