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Nouvre Inference · In development
Your workload. Open models. Measurable results.
We're building open-weight inference infrastructure for repeatable, high-volume classification, information extraction, and data labeling. Bring us a workload and a quality bar. We'll benchmark selected open models against it.
Nouvre Inference is in design-partner development. The service is not live yet. Workload evaluations will help determine which workflows deserve production infrastructure.
Working with financial data? See the workloads we're focused on →
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.
Financial data
Financial data workloads
We're building and testing around financial-data processing, with real production workloads as the basis for benchmarks. These are the tasks we want to measure with design partners to determine where current models meet the quality bar and which production requirements need better tooling.
Financial information extraction
Evaluate turning filings, reports, transcripts, and other financial content into structured records, with source references retained for validation.
Classification and enrichment
Test repeatable model-driven pipelines to classify, tag, and enrich financial content at scale against your own categories and quality requirements.
Entity identification & resolution
Measure how well models identify companies, securities, people, products, and other financial entities, and resolve them against your reference data or identifiers, flagging ambiguous matches for review.
Data labeling
Explore AI-assisted classification and labeling for downstream analytics and model development, measuring agreement with reference labels and identifying where human review is needed.
Structured output
Test transforming unstructured financial information into consistent schemas for databases, APIs, analytics systems, and AI applications, including your field normalization requirements.
Validation & provenance
Test outputs against known answers and reference data, retain source evidence for auditability, and route uncertain results for human review. Assess confidence measures and point-in-time correctness where applicable.
Discuss your workload →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, quality requirements, and data-handling constraints. Together, we'll define a representative evaluation set, reference answers, your current baseline, and success criteria.
02 · Benchmark
Compare on your terms
We'll benchmark selected open-weight models against the evaluation set and compare 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.
For repeatable, high-volume workloads, open weights can offer more choice in unit economics, model selection, deployment, and data handling, reducing dependence on one model vendor. We measure whether those options meet your quality bar and make economic sense for your workload.
Clear about where data goes.
Initial evaluations may use third-party inference infrastructure. We do not currently offer Nouvre-operated production GPU infrastructure.
Before customer data is processed, 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.
Have a financial-data workload? Bring a representative task in extraction, enrichment, entity resolution, labeling, validation, or structured output. We'll scope a benchmark together to determine how current open models perform and whether a pilot makes sense.
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