Northhaven Analytics

Northhaven Analytics — Synthetic Data & Custom ML

Data Infrastructure · Est. 2025 Warsaw · Remote Global

Synthetic Data.
Custom ML Models.
Any Industry.

We build the data and AI models your team needs — without touching real records. FinTech, MedTech, enterprise. Production-ready in 3–6 weeks.

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Zero real data accessed NDA from day one Full audit trail

Live Demo

3–6 wks

To production

10+

Industries served

Zero

Real data touched

NDA

From day one

Synthetic Datasets

Statistically faithful, at any scale. No real records ever used.

FinTech MedTech SaaS Insurance Energy

Custom ML Models

Trained on synthetic data, deployed to your infrastructure. Fraud detection, churn, risk scoring, diagnostics.

XGBoost Deep Learning Supervised

Validation & Advisory

Model performance testing, dataset integrity, regulatory alignment. Safe integration into your pipelines.

GDPR HIPAA SR 11-7 EU AI Act

Production-Ready

Live Systems.
Already Deployed.

Two live modules. No waiting list.

MODULE 01 — CREDIT RISK

Credit Risk Scoring &
Explainable AI Engine

Upload a bank statement CSV. Get a full credit assessment in under 3 minutes — no code required. XGBoost scoring, transparent PDF reports, configurable risk policy.

Credit Scoring Explainable AI PDF Reports SME / FinTech

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EYO HUB — SCORING ENGINE

LIVE

782

SCORE

Revenue Stability

Debt Service Ratio

Market Exposure

Cash Flow Volatility

Payment History

APPROVE — LOW RISK

PDF generated

MODULE 02 — PRIVATE DEBT

Private Debt Exit Risk
Simulator

Simulates exit and refinancing probability for illiquid corporate debt — 3–5 years forward, under macro stress. Built for mid-cap and institutional complexity.

Exit Risk Modeling Private Debt Refinancing Simulation Institutional

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EXIT PROBABILITY SIMULATOR

RUNNING

Y1Y2Y3Y4Y5

Exit Prob. Y3

61.4%

Refi Prob. Y5

82.1%

Stress Case

34.8%

REFINANCEABLE — MODERATE CONFIDENCE

Knowledge Base

Frequently
Asked
Questions

Straight answers on synthetic data, security, and how we work — for any team building with AI.

How realistic is your synthetic data compared to real data?

Built using correlation matrices, behavioural logic, and domain-specific constraints. Predictive accuracy typically matches 90–95% of real data performance.

Can synthetic data replace real data for ML training?

In many cases — yes. Ideal for model development, stress testing, and edge case simulation. Faster iteration, zero regulatory friction.

How is privacy guaranteed?

We never access or transform real client databases. Every dataset is generated from statistical patterns — no record can be traced to a real person, patient, or user. Full GDPR and HIPAA compatibility by design.

Which industries do you work with?

FinTech, MedTech, energy, AI startups, enterprise SaaS, insurance, logistics, and beyond. Our core capability applies to any domain that needs to train AI without exposing sensitive records.

Do you operate under NDAs?

Always. Every engagement starts with a mutual NDA from day one. We adapt to each organisation’s internal compliance and data handling policies.

Get in Touch

Ready to Build
Something Real?

Whether you’re a FinTech scaling risk models, a MedTech team blocked by data privacy, or an AI startup that needs clean training data — let’s talk.