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Vaiyu Solutions

Work & expertise

Proof, not promises.

Client work in regulated industries is mostly confidential. Our research, frameworks, and standards work are public — and citable.

Industries

Regulated is our comfort zone.

  • Healthcare & PharmaOur anchor. A decade building AI that runs in hospitals: imaging pipelines, multi-site collaboration without data sharing, validation that satisfies clinical review.
  • Academia & ResearchUniversities, research hospitals, and consortia. We turn grant-funded prototypes into sustainable, validated software — and carry research models the last mile into clinical deployment.
  • Financial ServicesDocument intelligence, risk models, and LLM workflows — deployed with the lineage and audit trail your regulators expect.
  • EnergyForecasting, monitoring, and optimization for infrastructure that cannot go down.
  • ManufacturingDefect detection and predictive maintenance that hold up at line speed, on the hardware you already run.
  • AutomotivePerception and analytics built under hard latency, safety, and certification constraints.

Representative engagements

The shapes of work we deliver.

Representative, end-to-end engagement patterns — each one we are built to deliver from architecture through monitoring.

The pilot that stalled before production

The model that wowed in a demo and then stalled — or the prototype you inherited and can’t trust. We re-engineer it into something your ops team can run and your auditors can sign off: hardened pipelines, lineage, monitoring, drift detection, and the tests that got skipped.

Research model → clinical deployment

Take a promising model from the lab to validated deployment on hospital infrastructure: data harmonization, retraining, and inference optimization for the hardware you actually have.

Learning across sites — without moving data

Stand up federated training across hospitals, partners, or geographies, so consortia can build shared models while every record stays home.

Private LLMs on your data

Adapt open or frontier language models to your domain inside your own environment — fine-tuning, evaluation harnesses, and guardrails included.

Document intelligence for regulated back offices

Extraction, classification, and review workflows for finance and compliance teams, with every model decision logged and reviewable.

AI at the edge of the plant

Defect detection and predictive maintenance under real latency, bandwidth, and hardware budgets — production lines, vehicles, substations.

The AI cost-down audit

A focused pass over your training and inference spend. Our track record: training costs cut by up to 50%, inference latency by up to 70% — without giving up accuracy.2

Built in the open

Open-source infrastructure, used in production.

Plus 40+ conda-forge packages maintained for reproducible scientific computing.

Selected publications

Peer-reviewed, not press-released.

  1. Federated learning enables big data for rare cancer boundary detection

    Nature Communications · 2022 · 71 sites, 6 continents — the largest real-world federated learning study to date

  2. GaNDLF: a generally nuanced deep learning framework for scalable end-to-end clinical workflows

    Communications Engineering (Nature) · 2023 · Editor’s Choice

  3. Federated benchmarking of medical artificial intelligence with MedPerf

    Nature Machine Intelligence · 2023

  4. OpenFL: the open federated learning library

    Physics in Medicine & Biology · 2022

  5. Towards fair decentralized benchmarking of healthcare AI with the FeTS Challenge

    Nature Communications · 2025

  6. Privacy preservation for federated learning in health care

    Patterns (Cell Press) · 2024

Full list on Google Scholar ↗

Standards & community

We help write the rules we build by.

Tell us what you’re trying to ship.

We typically start with a 2–4 week discovery sprint: framing, feasibility, and a costed plan.

Write to ussupport [at] vaiyu [dot] solutions

Sources & attribution

  1. 2 — Training-cost reductions delivered in Vaiyu client engagements; latency and resource reductions delivered by our founder at Indiana University.