Tuvaris

In-house AI researcher

Every enterprise should own its models.

Tuvaris is the AI researcher that builds them.
On your data. Inside your infrastructure.

Request early access →How it works ↓

Input

Tuvaris

Output

Your data + your targets

A specialized model, deployed and maintained

01 / What it does

What it does

Tuvaris is your in-house AI researcher for building and continuously improving specialized models on proprietary enterprise data.

It designs experiments, trains and evaluates candidates, manages deployment, and maintains performance in your environment against your quality, latency, and cost targets.

  1. 01 DataConnects to your data sources.
  2. 02 ExperimentsDesigns the experiments.
  3. 03 TrainingTrains candidate models.
  4. 04 EvaluationScores every candidate against your quality, latency and cost targets.
  5. 05 DeploymentDeploys the best candidate.
  6. 06 MaintenanceMonitors it in production. Retrains when performance drops.

You set the targets. Tuvaris runs the work.

02 / Why

Why

AI teams take weeks to experiment, evaluate and deploy one custom model.

Tuvaris automates all of it.

WithoutWith Tuvaris
Who runs experimentsA scarce ML teamTuvaris, continuously
IterationWeeks per modelAutomated end to end
Custom modelsFew, each built by handOne for every task, built automatically
Cost to runOversized models to cover every caseEach model sized to its task
When your data changesThe model goes staleTuvaris retrains it
What you ownAPI accessThe weights and the process

What you buy

The continuing ability to build your own models.

  • Models trained on your data. You own the weights.
  • A system that keeps improving them.
  • Model development without building a research team.

03 / How it works

How it works

You define
Quality, latency, and cost targets.
Tuvaris decides
Which strategies are best suited for the task. Which experiments to run. Presents the best candidate for your approval.
  1. 01

    Connect

    Reads your data sources and training platforms.

    You get: Data inventory

  2. 02

    Design

    Chooses the data, the base model and the method.

    You get: Experiment plan

  3. 03

    Train

    Runs the experiments on your compute.

    You get: Trained candidates

  4. 04

    Evaluate

    Tests every candidate against your targets.

    You get: Eval report

  5. 05

    Deploy

    Ships the best candidate to your serving stack.

    You get: Versioned deployment

  6. 06

    Monitor

    Tracks quality, latency and cost in production.

    You get: Drift report

The loop repeats when your data changes or performance drops.

04 / Deployment and security

Deployment and security

Deployment
On-prem. Your infrastructure.
Data egress
Private by default. Nothing leaves without your explicit approval.
Audit trail
Every action, dataset, experiment and deployment.
Model weights
Yours.
Integrations
Your data sources, training platforms and serving stack.

External computeUsed only when you allow it.

05 / Request early access

Request early access

We are opening Tuvaris to a small number of companies first.

Request early access →info@tuvaris.ai

Address

Via la Santa 1
6962 Viganello, Lugano
Switzerland

Supported by

USI Startup Center

Team

Tanish Shinde, Co-Founder
Greta Brahimaj, Co-Founder

Tuvaris