Services
From mandate to production.
All six exist to get one thing done: a system that survives contact with auditors and with real users.
01
Discovery & AI Strategy
A 2–4 week sprint that turns a fuzzy mandate into a costed, de-risked plan.
A plan your board and your engineers both believe.
What we do
- Problem framing and use-case triage against business value
- Data-readiness and infrastructure assessment
- Technical due diligence on vendors, models, and architectures
- Build-vs-buy analysis with a costed roadmap
02
Data Engineering for AI
Ingestion, curation, harmonization: data your models and your auditors can trust.
Data that holds up when someone audits how it was built.
What we do
- Pipelines for ingestion, curation, and quality control
- Harmonization across sites, formats, and legacy systems
- Annotation workflows and dataset versioning
- “Data as IP” strategy for licensing and collaboration, the approach that contributed to a $3.5M NIH/NCI awarda
03
Model Development & Training
Custom pipelines, LLM adaptation, federated learning: built for your domain.
A model that still holds up on next quarter’s data.
What we do
- Architecture selection and custom training pipelines: vision, language, multimodal
- Fine-tuning open and frontier models on proprietary data
- Federated and privacy-preserving training when data cannot move
- Pre-training cost optimization: up to 50% lower training spend with accuracy maintained or improvedb
04
Deployment, MLOps & Optimization
Secure, observable, affordable AI in production: new builds and stalled pilots alike.
AI your own ops team can run.
What we do
- API-driven integration with your stack: REST, MCP, A2A
- CI/CD for models, monitoring, and drift detection
- Inference optimization for constrained and clinical hardware: up to 70% latency reduction, 10–50% fewer resourcesc
- Cloud, VPC, on-premises, and edge deployment
05
AI Governance & Compliance Readiness
Reproducibility, validation, and documentation that stand up to scrutiny.
When a regulator asks how it works, the answer is already written down.
What we do
- Reproducible pipelines and experiment lineage
- Validation protocols and audit-ready documentation
- Privacy-preserving design: de-identification, federation, access control
- Responsible-AI reviews calibrated to regulated environments, built on a decade of healthcare-grade rigor
06
Fractional AI Leadership & Enablement
Senior AI leadership (strategy, hiring, board reporting) without the full-time hire.
Your team runs it without us afterward.
What we do
- Fractional Chief AI Officer engagements
- Embedded technical advisory alongside your team
- Hiring support and vendor selection
- Workshops and upskilling, from instructors who have taught federated learning at MICCAI, AAAI, ISBI, and RSNA
How we engage
Start small, extend on evidence.
Every engagement is structured so you can judge the work before you extend it.
01
Discovery Sprint
2–4 weeks. Framing, feasibility, and a costed plan. The default way to start.
02
Build & Handover
Scoped delivery with documentation, training, and knowledge transfer, so nothing you inherit is a black box.
03
Embedded Advisory
Recurring senior engineering and product leadership inside your team.
04
Fractional CAIO
Strategy, hiring, vendor selection, and board reporting, on a fractional basis.
Tell us what you’re trying to ship.
Most of this starts with a 30-minute call. If there’s something there, the usual next step is a 2–4 week discovery sprint: framing, feasibility, and a costed plan you keep either way.
Sources & attribution
- a. The “data as IP” approach our founder led at Indiana University contributed to a $3.5M NIH/NCI award.
- b. Vaiyu client engagements: pre-training optimization with model accuracy maintained or improved.
- c. Founder track record at Indiana University, optimizing inference for clinical research environments.