Invest in Northhaven.
We build autonomous systems for healthcare operations, starting with mintHealth for ambulatory surgical centers — on a synthetic-data engine proven first in quantitative finance.
The thesis
Healthcare operations are under-automated
Surgical centers still run procurement on spreadsheets, preference cards and phone calls. The work is repetitive, rules-based and expensive when it fails — a good fit for autonomous agents.
Privacy is the bottleneck, and our engine removes it
Models that touch patient data are slow to build and approve. Training and testing on synthetic data first shortens that path — the same approach we built for banks.
One engine, several products
mintHealth is live; Brevio and our ostomy-monitoring research with Coloplast follow on the same foundation.
Investor materials
Some documents below predate our healthcare focus and describe the synthetic-data engine for finance that the company grew from.
Northhaven Analytics is developing a specialised synthetic data engine. Specifically, it is designed exclusively for the financial sector. We create high-fidelity, correlation-aware datasets for banks and hedge funds. Moreover, we serve fintechs and quantitative research teams. Ultimately, this enables AI development, model training, and risk analysis without relying on real customer data.
Our Mission: To provide financial institutions with the safest, most realistic alternative to sensitive datasets. Indeed, it is built on a foundation of deep domain logic, transparency, and reproducibility.
Pitch Deck
Investment Thesis
The financial world is moving toward privacy-first AI. For instance, regulations like GDPR, banking secrecy, and the upcoming EU AI Act create barriers. Furthermore, internal data-access barriers and strict model validation standards make it difficult to use real data. However, synthetic financial data solves this problem. Yet, general-purpose generators fail to capture the complexity unique to finance.
Therefore, Northhaven is building a specialised engine. Specifically, it reflects the true structure of financial systems: correlations, lifecycle behaviour, and risk dynamics. In short, financial teams want realistic, compliant data. Consequently, we provide exactly that.
Market Opportunity
The global synthetic data market is expanding rapidly. This is driven by increased regulatory pressure on real-data usage. In addition, there is rising demand for AI-ready datasets. Moreover, the need for faster model development is growing.
Consequently, finance is the single largest segment of the synthetic data industry. However, it is the one with the highest technical barriers. Thus, this is where Northhaven operates.
Problem We Solve
Financial institutions face critical challenges. For example, they have limited access to real customer data for training models. Furthermore, they face long internal approval cycles. In addition, there is a high risk of compliance violations. Consequently, these barriers slow down innovation and increase costs.
Therefore, Northhaven provides domain-accurate datasets that model true financial behaviour. Ultimately, this enables safe development of AI and quantitative systems.
Our Solution
Northhaven Analytics delivers synthetic datasets that mirror real-world financial patterns. Specifically, we provide multi-entity structures (clients, accounts, transactions). Moreover, we offer behavioural modelling based on income and spending patterns. In addition, we enable scenario and lifecycle simulation. Crucially, we recreate financial logic, not just raw data.
Learn more about our approach: How We Turn Financial Complexity into Synthetic Intelligence
Technology Overview
Our engine combines advanced dependency modelling with probabilistic generation. Furthermore, it uses correlation-preserving algorithms and domain-specific constraints. In fact, the system is designed by a dual-founder team blending quantitative finance and data engineering. Therefore, it is built to scale into an enterprise-grade platform.
Traction
Despite being early-stage, Northhaven has:
- a working MVP generator
- complete dataset structure & metadata architecture
- internal validation and consistency checks
- early interest from financial professionals and quant teams
- a clear roadmap to enterprise deployment
A free demo dataset is provided to qualified institutions after a technical consultation.
Business Model
Northhaven operates on:
- one-off project fees for custom datasets
- enterprise simulation packages
- annual or quarterly subscriptions
- advisory and validation services
This structure aligns directly with the purchasing habits of financial institutions and research teams.
Roadmap
Founders
Oleg Fyłypczuk — Financial Modeling & Product Vision Oleg brings deep experience in quantitative finance. Specifically, he defines the behavioural architecture behind Northhaven’s datasets. Thus, he ensures alignment with real institutional needs.
Gabriel Wiśniewski — Data Engineering & Algorithmic Architecture Gabriel designs the technical foundation of Northhaven. In short, he ensures that our datasets remain structurally consistent, reproducible, and enterprise-ready.
Read full bios: About Northhaven Analytics
Investment Opportunity
Northhaven Analytics is preparing a pre-seed round. This is to accelerate development and expand core modules. Funds will support advanced modelling R&D. In addition, we will expand security infrastructure. Therefore, we welcome conversations with investors specialising in fintech, AI/ML, and deep tech.