Core Signals
+90The profile of the area where the person lives, plus essential email and phone attributes such as validity and operator.
55 address · 25 email · 10 phoneTHE AI PLATFORM DATA SCIENCE TEAMS WISH THEY HAD
Better data + AI: better models, faster time to market and higher productivity that frees your team for broader analytics. No extra spend.
+45%
Gini uplift with 3,000+ data points from the Data Enrichment Network.
Multiple
NORA, MJÖ, TERRA… prepare data and build models in natural language.
Minutes, not months!
Launch the model while the opportunity is still there.
1000x Productivity
Ample time to explore new opportunities in credit, fraud, collections, pricing, retention, etc.
⇒ More EBITDA no money left on the table
A real credit admission case, step by step. Click each stage to see it in the product.
Full process takes minutes, not months!




1Upload any data file — CSV, Parquet, XLS — and NORA works on it for you.
2Tell NORA your goals: clean, merge, replace, build the target and more. NORA does it all.
3NORA proposes a plan. You approve it.
4NORA executes and validates every step. The dataset is ready.
Step 01 Data Workspace
Three messy files in different formats and one prompt. NORA plans the work, you approve it, and NORA delivers a clean dataset ready for a new project.


1Pick the model family: Origination & Underwriting.
2Then the model type: credit risk at origination.
Step 02 Project setup
Pick the business problem you want to solve and Thorcast sets up the project to match it.

Step 03 Data enrichment
Enrich your dataset with alternative data available only on the Thorcast platform.



1MJÖ proposes a mission plan. One approval runs it.
2MJÖ prepares the data and trains several models.
3The best model, ready to review.
Step 04 MJÖ · AutoML
MJÖ plans the preprocessing, recommends new variables, trains and compares several models, and presents the best one.


1Model performance: Gini and ROC curve.
2The HTML technical report, with MJÖ's synthesis.
Step 05 Results & audit
Industry-standard metrics and a technical report ready for audit, including MJÖ's written synthesis.


1Enter the economics of your credit policy.
2Profit, risk and approval for every cutoff.
Step 06 Decision layer
Turn the score into a credit policy: approval rate, risk and economic impact for every cutoff.

Step 07 Deployment
Put the model to work in Thorcast Cloud, on your own premises, or right in the app.
More than 3,000 data points, condensed into 261 model-ready signals. To maximize performance and keep your models simple to build, explain and run.
What you already have
The profile of the area where the person lives, plus essential email and phone attributes such as validity and operator.
55 address · 25 email · 10 phoneThe digital footprint behind an email or a phone: which kinds of platforms it is linked to.
127 email · 44 phoneArea profiles, points of interest and drive-time zones around any location, on a private map.
Tradelines, delinquency history and score bands from the bureau.
Thorcast builds high-performance predictive models powered by AI, alternative data and a unique analytics and decision platform.
Use case · video Every answer comes with a real use case: watch how Thorcast solved it, step by step.
Credit risk
Credit risk
Churn & retention
Field operations
Service quality
Cross-sell & upsell
Collections & recovery
Lead scoring
Credit risk
Fraud detection
Customer lifetime value
Demand forecasting
Device & plan demand
Use case video coming soonGeographic intelligence
Fraud detection
Fraud detection
Fraud detection
Fraud detection
Collections & recovery
Collections & recovery
Income estimation
Income for limits & affordability
Use case video coming soonIncome estimation
Income for credit limits
Use case video coming soonChurn & retention
Churn & retention
Churn & retention
Churn & retention
Churn & retention
Lead scoring
Lead scoring
Lead scoring
Lead scoring
Lead scoring
Cross-sell & upsell
Cross-sell & upsell
Cross-sell & upsell
Cross-sell & upsell
Customer lifetime value
Customer lifetime value
Customer lifetime value
Demand forecasting
Demand by product & store
Use case video coming soonPricing & underwriting
Pricing & underwriting
Geographic intelligence
Geographic intelligence
Geographic intelligence
Explore Thorcast step by step: prepare your datasets, train and compare models, run predictions, and monitor real-world performance.
7 tutorials · Videos in English
Normalizes, organizes, repairs and merges your datasets.
Builds your models autonomously, following your rules, with auditability, maximum performance and record speed.
Step by step, in manual mode.
Add hundreds of new signals in minutes, without writing code.
Compare models and run your first batch scoring.
Track your model’s real-world performance.
Challenge your best model to improve performance and measure the economic impact.
NORA and MJÖ work like a world-class data engineer and data scientist: they follow your rules and run on your premises.
Weeks of coding in Python or R to get an uncertain output
I have 3 datasets in this session. Tell me how you want to merge, clean or transform them.
Merge these three files by customer ID, fix the dates and build the default target.
Here is my plan:
customer_idapplication_datedefault_90dShall I go ahead and execute this plan?
ApproveEdit plan
Write the exact instruction for NORA…
Minutes of natural language prompting to get a flawless output

NORA
Trained to be the best data engineer in the world.
Specialties
How it works with you
Delivers

MJÖ
Trained to be the best data scientist in the world.
Specialties
How it works with you
Delivers
Data preparation, model training and scoring run in your environment, on your infrastructure.
AI plans; validated code executes. Same input, same result, every time.
Every plan waits for your approval. Pause or stop at any moment.
Every step is logged, reproducible and documented in the technical report.
Last updated: 31 December 2025
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