Trade analytics platforms for businesses that need clarity across tariffs, codes and borders.
International trade data is scattered across tariff schedules, HS codes, customs data and market statistics that were never designed to be queried together. We build the analytics platforms that unify it — from UN Comtrade data exploration to HS code classification — so trade and market teams can see opportunity and risk clearly.
The problems that bring
trade businesses to us.
If any of these sound familiar, the data is the constraint, not the market.
Trade data locked in disconnected sources
Tariff schedules, HS codes, customs records and market statistics typically sit in separate systems and formats that do not talk to each other.
Market opportunity is hard to see
Without a queryable platform, spotting genuine cross-border opportunity means manually cross-referencing spreadsheets and government datasets.
Classification done by hand
HS code classification is often still a manual, error-prone process rather than a structured part of the platform.
Sustainability layered on top of trade
International trade businesses increasingly need sustainability and compliance metrics alongside pure trade data, not as a separate exercise.
Built on data made
queryable, not just collected.
Most trade analytics problems are not a data-availability problem — the data exists. It is a query problem. Here is how we approach it differently.
Built on real cross-border data platforms.
We built and continue to scale Hanse for Hanse Analytics, giving businesses monthly UN Comtrade data and BI tools to explore international market opportunities — and rebuilt Core’s platform around its HS code classification and supply chain products.
That is the foundation we bring to every international trade build: the data made queryable, not just collected.
Built on real trade data platforms
We built and continue to scale Hanse for Hanse Analytics — a platform giving businesses monthly UN Comtrade data and BI tools to explore international market opportunities.
HS code classification, done properly
Our rebuild for Core gave the business a platform reflecting its HS code classification and supply chain products across international markets.
Senior engineers, start to scale
The same engineers who design the data model build and support it in production.
Production in weeks
A first working version typically ships in 8–14 weeks.
“An HS code is a small thing to get wrong and an expensive thing to get wrong at scale. The interesting engineering problem in trade data is never the dashboard — it is making sure the classification underneath it is actually correct.”
Not sure your trade data could support this? Ask the engineer who’d build it.
Book a 30-minute working session with a senior engineer — a real conversation about your data, not a sales call.
Other sectors we know well.
Eight years of building means we’ve learned the dialect of a few specific domains. Here is where else we’ve put in the hours.
The stack behind the platforms we build.
Pragmatic, mostly boring, and chosen because it works in production for real client data — not because it's on the front page of Hacker News.
The company adjusted swiftly to emerging obstacles and proactively identified needed solutions promptly.Read more client stories
Data engineering, AI and BI —
built for international trade teams.
Data engineering
We design and build the data infrastructure that powers intelligent decision-making — from ingestion to insight.
Learn moreData products
Standalone data products your team can actually use.
Learn more 02Data solutions
Bespoke solutions to your most complex data challenges.
Learn more 03BI & analytics
Dashboards and reporting tools that turn raw data into clear decisions.
Learn more 04Machine learning & MLOps
Predictive models and ML pipelines designed for production.
Learn more 05Data consultancy
Strategic advice on how to structure, govern, and get more value from your data.
Learn moreQuestions, answered straight.
Have an outcome in mind?
We'll help you
ship it.
- You're building a data product and need a team that can deliver.
- You want to get AI-ready — pragmatically, not theoretically.
- Your reporting is a mess and you need a real platform underneath it.