AI operationalization consultancy
AI that ships: in industries where it has to be right.
Vaiyu Solutions takes AI from architecture to production (data, training, deployment, monitoring) for organizations where a wrong answer costs more than a headline. When a pilot stalls before launch, we’re the ones who get it shipped.
What we do
Six ways in: one standard of delivery.
01
Discovery & AI Strategy
A 2–4 week sprint that turns a fuzzy mandate into a costed, de-risked plan.
Detail →
02
Data Engineering for AI
Ingestion, curation, harmonization: data your models and your auditors can trust.
Detail →
03
Model Development & Training
Custom pipelines, LLM adaptation, federated learning: built for your domain.
Detail →
04
Deployment, MLOps & Optimization
Secure, observable, affordable AI in production: new builds and stalled pilots alike.
Detail →
05
AI Governance & Compliance Readiness
Reproducibility, validation, and documentation that stand up to scrutiny.
Detail →
06
Fractional AI Leadership & Enablement
Senior AI leadership (strategy, hiring, board reporting) without the full-time hire.
Detail →
Why teams trust us
Published, cited, covered, then hired.
Our team’s research has appeared in Nature Communications,Nature Machine Intelligence,The Lancet Oncology, andRadiology, and been covered byThe Wall Street Journal. We hold the Vice Chair for Algorithmic Development of theMLCommons Medical Working Group, helping set the standards medical AI is measured against.
Announced July 2026 · read the release →- Editor’s ChoiceCommunications Engineering (Nature)
- Top 25, 2022Nature Communications, Health Sciences
- 1st place, 2015Brain Tumor Segmentation, MICCAI
- PressThe Wall Street Journal
Built in the open
Our frameworks run in research hospitals worldwide.
- GaNDLFLow-code, reproducible deep learning for clinical workflows.Communications Engineering (Nature) Editor’s Choice · an MLCommons projectGitHub ↗
- MedPerfFederated benchmarking of medical AI at global scale.Nature Machine Intelligence · an MLCommons projectGitHub ↗
- FeTSReal-world federated tumor segmentation across 71 sites on 6 continents.Nature Communications · covered by The Wall Street JournalGitHub ↗
- OpenFLAn open framework for federated learning, hardened in healthcare.Physics in Medicine & BiologyGitHub ↗
- CaPTkQuantitative cancer-imaging platform for radiomics and ML phenotyping.Journal of Medical ImagingGitHub ↗
- GaNDLF-SynthDemocratizing generative AI for medical imaging: autoencoders to diffusion.MLCommons ecosystemGitHub ↗
Plus 40+ conda-forge packages maintained for reproducible scientific computing.
How we engage
Four ways to bring us in.
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. No black-box handoffs.
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.
We typically start with a 2–4 week discovery sprint: framing, feasibility, and a costed plan.
Sources & attribution
- 1. Led by our founder across NIH/NCI-funded programs at the University of Pennsylvania and Indiana University.
- 2. Vaiyu client engagements: pre-training optimization with model accuracy maintained or improved.
- 3. Pati, S. et al. “Federated learning enables big data for rare cancer boundary detection.” Nature Communications 13 (2022).doi:10.1038/s41467-022-33407-5; 71 sites across 6 continents, the largest real-world federated learning study to date.
- 4. Founder track record at Indiana University: inference latency reduced by up to 70%, compute requirements by 10–50%, in clinical research environments.