Put information in its place.
Categorize support requests, route documents, or organize product catalogs using the labels your business needs.
Nouvre Inference · In development
Your workload. Open models. Measurable results.
We're building open-weight inference for classification, extraction, and data labeling. Help us shape it around the work your business already does.
The service is not live yet. We're looking for early partners to help define the benchmarks, workflow, and production requirements.
Our first focus
Repetitive work. A clear quality bar.
We're starting with workloads where quality can be tested against known answers and cost matters at volume.
Turn documents into useful data.
Pull fields from invoices, reports, and business documents into structured records your systems can use.
Make large datasets workable.
Apply consistent annotations to text, flag uncertain results for review, and measure agreement against your reference labels.
The design partner process
Start with one workload. Let the results guide us.
We're seeking companies already using AI for these tasks, or preparing to process a substantial volume of documents or text.
01 · Define
Set the quality bar
We'll discuss your current workflow, volume, budget, and data requirements. Together, we'll scope a representative evaluation set and agree on what success means.
02 · Benchmark
Compare on your terms
The proposed benchmark will compare selected open-weight models with your current baseline on quality, latency, and estimated cost. We'll agree on scope and terms before work begins.
03 · Pilot
Shape the first release
If the results meet your needs, we'll plan a limited pilot as the platform becomes ready. Your feedback will help define structured outputs, batch processing, monitoring, and model changes.
More choice in your AI stack.
Our goal is a consistent API with evaluated open-weight models behind it, so your application can evolve as better models arrive.
We're starting with classification, extraction, and labeling. Over time, we aim to support more workloads and dedicated infrastructure.
Clear about where data goes.
Our initial plan is to use third-party inference providers through Hugging Face. We do not operate our own GPU infrastructure today.
Before any evaluation or pilot, we'll agree on the provider, data handling requirements, and applicable retention and training policies. Open weights alone do not make hosted inference private.
Build with us
Become a design partner.
Tell us about one workload you'd like to run on open models. Include your approximate volume, current approach, and what a useful result would look like.
We'll follow up to discuss fit and next steps. This is an expression of interest; pricing, timing, and pilot scope will be agreed together.
Please start with a description of the workload. We'll arrange how to share any sample data after discussing your requirements.
Prefer email? tim@nouvre.com