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.
- 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.