THE AI PLATFORM DATA SCIENCE TEAMS WISH THEY HAD

Smarter Analytics. EBITDA follows.

Better data + AI: better models, faster time to market and higher productivity that frees your team for broader analytics. No extra spend.

+45%

More predictive models

Gini uplift with 3,000+ data points from the Data Enrichment Network.

Multiple

AI agents on your team

NORA, MJÖ, TERRA… prepare data and build models in natural language.

Minutes, not months!

Record time to market

Launch the model while the opportunity is still there.

1000x Productivity

Spare capacity for broader analytics

Ample time to explore new opportunities in credit, fraud, collections, pricing, retention, etc.

⇒ More EBITDA no money left on the table

THORCAST IN ACTION

From messy data to a deployed model. Seven steps, one platform.

A real credit admission case, step by step. Click each stage to see it in the product.

Raw data Deployed model

Full process takes minutes, not months!

  1. Upload any data file — CSV, Parquet, XLS — and NORA works on it for you.Tell NORA your goals: clean, merge, replace, build the target and more. NORA does it all.NORA proposes a plan. You approve it.NORA executes and validates every step. The dataset is ready.

    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.

    Step01Data Workspace

    Step 01 Data Workspace

    NORA gets your data ready

    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.

    NormalizeOrganizeRepairAssemble
    • No code. Just natural language.
    • Not weeks, just minutes.
    • NORA is your best data scientist for the job.
DATA ENRICHMENT NETWORK (DEN)

Your data tells half the story. Thorcast brings the other half. The best data on the market, one click away.

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

Address
Email
Phone
ID

Core Signals

+90

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 phone
AddressEmailPhone

Deep Signals

+171

The digital footprint behind an email or a phone: which kinds of platforms it is linked to.

127 email · 44 phone
EmailPhone

Geo Intelligence

Block level

Area profiles, points of interest and drive-time zones around any location, on a private map.

AddressLocation

Credit Bureau

Tradelines, delinquency history and score bands from the bureau.

ID
SOLUTIONS

From banking to retail, what would you want to predict? Thorcast predicts it better.

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

Who will pay back?

Credit risk

Who will pay back?

Churn & retention

Who is about to leave?

Field operations

Will the technician find someone home?

Service quality

Which failure will trigger a complaint?

Cross-sell & upsell

What will they buy next?

Collections & recovery

Who can we still recover?

Lead scoring

Which lead will convert?

Credit risk

Who will pay back?

Fraud detection

Is this a fraud?

Customer lifetime value

What is this customer worth?

Demand forecasting

How much will we sell?

Device & plan demand

Use case video coming soon

Geographic intelligence

Where should we grow?

Fraud detection

Is this a fraud?

Fraud detection

Is this a fraud?

Fraud detection

Is this a fraud?

Fraud detection

Is this a fraud?

Collections & recovery

Who can we still recover?

Collections & recovery

Who can we still recover?

Income estimation

How much does this customer earn?

Income for limits & affordability

Use case video coming soon

Income estimation

How much does this customer earn?

Income for credit limits

Use case video coming soon

Churn & retention

Who is about to leave?

Churn & retention

Who is about to leave?

Churn & retention

Who is about to leave?

Churn & retention

Who is about to leave?

Churn & retention

Who is about to leave?

Lead scoring

Which lead will convert?

Lead scoring

Which lead will convert?

Lead scoring

Which lead will convert?

Lead scoring

Which lead will convert?

Lead scoring

Which lead will convert?

Cross-sell & upsell

What will they buy next?

Cross-sell & upsell

What will they buy next?

Cross-sell & upsell

What will they buy next?

Cross-sell & upsell

What will they buy next?

Customer lifetime value

What is this customer worth?

Customer lifetime value

What is this customer worth?

Customer lifetime value

What is this customer worth?

Demand forecasting

How much will we sell?

Demand by product & store

Use case video coming soon

Pricing & underwriting

What will this risk cost?

Pricing & underwriting

What will this risk cost?

Geographic intelligence

Where should we grow?

Geographic intelligence

Where should we grow?

Geographic intelligence

Where should we grow?

Don't see your question? Thorcast models any prediction your data can support.
HOW IT WORKS

Enrich your data. Power your models with AI. Improve predictions in minutes, not months.

Explore Thorcast step by step: prepare your datasets, train and compare models, run predictions, and monitor real-world performance.

7 tutorials · Videos in English

TUTORIAL 013:163:183:14 · English

NORA AI: your data preparation agent

Normalizes, organizes, repairs and merges your datasets.

TUTORIAL 023:443:403:40 · English

MJÖ AI Agent: your most efficient team member

Builds your models autonomously, following your rules, with auditability, maximum performance and record speed.

TUTORIAL 051:351:361:37 · English

Model comparison and batch scoring

Compare models and run your first batch scoring.

TUTORIAL 071:481:421:48 · English

Champion–Challenger

Challenge your best model to improve performance and measure the economic impact.

AI AGENTS

Your AI data team. Trained to be the world's best. Working on your premises.

NORA and MJÖ work like a world-class data engineer and data scientist: they follow your rules and run on your premises.

The usual way is the old way
1import pandas as pd2import numpy as np3from sklearn.model_selection import train_test_split, GridSearchCV4from sklearn.metrics import roc_auc_score, roc_curve5from xgboost import XGBClassifier6 7# 1. Load the three sources (each one in a different format)8loans = pd.read_csv("loans_2024.csv", sep=";", decimal=",")9bureau = pd.read_excel("credit_bureau.xlsx", sheet_name="data")10customers = pd.read_parquet("customers.parquet")11 12# 2. Fix types, dates and duplicates13loans["application_date"] = pd.to_datetime(loans["application_date"], dayfirst=True, errors="coerce")14loans["requested_amount"] = loans["requested_amount"].str.replace(",", "").astype(float)15loans = loans.drop_duplicates(subset="application_id")16bureau["customer_id"] = bureau["customer_id"].str.strip().str.upper()17bureau = bureau.replace({-999: np.nan, np.inf: np.nan})18bureau = bureau.sort_values("inquiry_date").groupby("customer_id").tail(1)19customers["age"] = customers["age"].where(customers["age"].between(18, 99))20 21# 3. Join everything on customer_id22df = loans.merge(bureau, on="customer_id", how="left")23df = df.merge(customers, on="customer_id", how="left")24assert df["application_id"].is_unique, "duplicated applications"25 26# 4. Target and features27df["default_90d"] = (df["max_days_past_due_12m"] >= 90).astype(int)28df["debt_to_income"] = df["total_debt"] / df["monthly_income"].replace(0, np.nan)29df["utilization"] = df["balance"] / df["credit_limit"].clip(lower=1)30for col in ["bureau_score", "inquiries_6m", "n_credit_cards"]:31 df[col] = df[col].fillna(df[col].median())32df = pd.get_dummies(df, columns=["product", "region"], drop_first=True)33 34def woe_bins(x, y, bins=10):35 t = pd.qcut(x, bins, duplicates="drop")36 g = pd.DataFrame({"bin": t, "y": y}).groupby("bin")["y"].agg(["sum", "count"])37 g["good"] = g["count"] - g["sum"]38 return np.log((g["good"] / g["good"].sum()) / (g["sum"] / g["sum"].sum()))39 40# 5. Split, tune and train41X = df.drop(columns=["default_90d", "application_id", "customer_id"])42y = df["default_90d"]43X_tr, X_te, y_tr, y_te = train_test_split(X, y, test_size=0.3, stratify=y, random_state=42)44grid = {"max_depth": [3, 4, 6], "learning_rate": [0.03, 0.1], "n_estimators": [300, 600]}45search = GridSearchCV(XGBClassifier(eval_metric="auc"), grid, scoring="roc_auc", cv=5)46search.fit(X_tr, y_tr)47model = search.best_estimator_48 49# 6. Validate50p = model.predict_proba(X_te)[:, 1]51auc = roc_auc_score(y_te, p); gini = 2 * auc - 152fpr, tpr, _ = roc_curve(y_te, p); ks = max(tpr - fpr)53print(f"AUC={auc:.3f} Gini={gini:.3f} KS={ks:.3f}")54# TODO: out-of-time check, calibration, scorecard, cutoffs, report...

Weeks of coding in Python or R to get an uncertain output

With Thorcast You don't write code. NORA and MJÖ do it for you.
NORA Session ChatUpload
NORAAssistant

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.

NORAAssistant

Here is my plan:

  1. Join loans, bureau and customers on customer_id
  2. Fix the date format in application_date
  3. Build the target default_90d

Shall 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

Code-free. All in natural language.
YOUR PREMISES · YOUR ENVIRONMENT · DATA DOES NOT LEAVE YOUR PREMISES
MJÖ

MJÖ

AI Data Scientist

Trained to be the best data scientist in the world.

Specialties

Out-of-time validationMissing values & outliersAutoMLHyperparameter tuningExplainabilityDecision layers

How it works with you

  1. 1You set the goalone mission
  2. 2MJÖ plansevery step justified
  3. 3You approve oncepause or stop anytime
  4. 4MJÖ deliversthe champion, explained

Delivers

Champion modelPlain-language explanationAudit-ready report

On your premises

Data preparation, model training and scoring run in your environment, on your infrastructure.

Deterministic execution

AI plans; validated code executes. Same input, same result, every time.

You stay in control

Every plan waits for your approval. Pause or stop at any moment.

Fully auditable

Every step is logged, reproducible and documented in the technical report.

BLOG

Intelligence, shared. Ideas from the Thorcast team.

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