The Predictive AI Company
Predict anything.
The Foundation Model for Prediction. No training. No pipelines.
The prediction layer for large scale structured data.
Pretrained on billions of tabular datasets to understand the patterns in rows and columns. Send your data, get predictions back in seconds. No training. No pipelines. No infrastructure.
Purchase probability
Fraud detection
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Predictive maintenance
One API call. Instant predictions.
Classification
Regression
Up to 50M rows
Mixed types
Advised by Mehdi Ghissassi, Google DeepMind and backed by
French Tech 2030
Microsoft for Startups
Station F
Inria
Scaleway
Nvidia Inception Program
Neuralk vs. Competition
The Predictive AI Company
Purpose-built for enterprise-scale structured data. Unmatched accuracy and deployment speed.
Built for
Tables
Training
No
Infrastructure
Api call
Performance
Best
First prediction
Seconds
Why not XGBoost?
Great algorithm. Six months of pipeline work. Neuralk beats it on benchmarks with one API call.
Built for
Tables
Training
per dataset
Infrastructure
ml pipeline
Performance
strong
First prediction
months
Why not AutoML?
Still trains a model every time. Still needs infrastructure. Neuralk comes pretrained out of the box.
Built for
Tables
Training
per dataset
Infrastructure
Per dataset
Performance
strong
First prediction
Weeks
Break your performance ceiling.
If it has rows and columns, we can predict it.
Proof, not promises.
All benchmarks public and reproducible. No asterisks.
Predict
Revenue
curl -X POST <https://api.neuralk.com/v1/predict> \\
Enterprise-grade from day one.
Secure by default
Any scale
Zero infrastructure
Your data is processed and never stored.
No training on your data.
Enterprise compliance documentation
available.
From prototype to production.
Datasets of 50M+ rows
Mixed types, numbers, text, categories.
One API, any volume.
No ML pipeline.
No model management.
No retraining when data drifts.
One API call replaces months of engineering.
Enterprise-grade from day one.
Secure by default
Your data is processed and never stored.
No training on your data.
Enterprise compliance documentation available.
curl -X POST <https://api.neuralk.com/v1/predict> \\
Any scale
From prototype to production.
Datasets of 50M+ rows
Mixed types, numbers, text, categories.
One API, any volume.
Zero infrastructure
No ML pipeline.
No model management.
No retraining when data drifts.
One API call replaces months of engineering.
I see immense potential in Neuralk-AI's scientific team to empower enterprises in building AI solutions that seamlessly integrate and interact with structured databases.
We are thrilled to collaborate with Neuralk-AI in developing next-generation AI solutions for our clients.
Neuralk's API could help us save our clients numerous hours of unnecessary manual data cleaning.
Frequently asked questions
Yes. In the same way ChatGPT answers questions about topics it never trained on directly, your data is a variation of patterns the model has seen millions of times.
AutoML trains a new model every time. Neuralk is already trained. There is no additional training step required.
Yes. You can check the results on our public benchmark, or try it on your dataset via our API.
They can, but very poorly. They typically perform 2x worse than Tabular Foundation Models. They’re built for language, not rows and columns.
Our models ingest all the main tabular data models: CSV, JSON, database exports,… We are constantly developing connectors to new data sources such as Databricks or Snowflake. Reach out for requests.
Customer data is processed via API and not stored. Data exists only in memory for the duration of the inferencecall and is discarded immediately after. We retain only operational metadata (request IDs, timestamps, billing) required for service operation. API keys are hashed at rest. All traffic is encrypted in transit. Check our Security page for more information.
You can test our API for free using pre-loaded credits, no credit card required. Paid plans scale with usage. Enterprise pricing on request.
The only limit is the kind of data and volume you have. Structured data + prediction target = it works. The model understands structure, not industry.