Data engineering & AI readiness for startups
We build the data foundations growing startups need: pipelines, a single source of truth, investor-ready metrics and AI-ready data. Without hiring a full data team.
Senior engineers from day one · Based in Switzerland · Working with startups in Europe and the US
Sound familiar?
Your product database, Stripe, HubSpot, spreadsheets and lab or ops tools each hold part of the truth.
Every update means exporting CSVs and rebuilding the same numbers by hand.
The model works in a demo, but production data is messy, incomplete or hard to access.
Your developers build the product. Nobody owns pipelines, data quality or the warehouse.
Customers, partners and regulators start asking where data lives and who can access it.
Scripts and dashboards that worked at 10 customers fall over at 1,000.
What we do
Start with the audit, or go straight to what you need. Every service has a clear deliverable and duration.
Find out what data you have, where it breaks, and what to build first.
A clean, scalable data stack, sized for a startup budget.
Make your data usable by AI features, copilots and models.
What to expect
A typical engagement for a startup with a few data sources and a small team. Your timeline depends on your scope.
A clear map of your data, the gaps, and a costed plan for the next 90 days.
Your key data flows into one platform, with dashboards your whole team trusts.
Clean, documented, governed data your product and AI features can rely on.
Our approach
We agree on scope and milestones before we start, and you see progress every week.
We map your sources, tools and goals, and agree on what matters most for the next 90 days.
Week 1–2We set up pipelines, models and dashboards in short sprints, with something working every week.
Weeks 3–8We document everything and train your team, so you are never locked in.
Final weekOptional: we keep the platform running and growing as you scale.
Monthly, if neededHow we work with you
Every proposal is tailored after a first call. Pick what fits your stage and budget.
A short, fixed-scope audit with a clear deliverable. The easiest way to start.
A defined project to set up your data platform or AI-ready data, with agreed milestones.
A senior data engineer and architect on demand, part-time, without hiring.
We monitor, maintain and improve your data stack as you grow.
Who we work with
Different industries, same problem: data spread across tools, and not enough time to fix it. We work with seed to Series B startups in any sector, including:
Who you work with
You work directly with the partners, backed by a dedicated engineering team that scales with your project.
[Role] · [X] years in data engineering
[Role] · [X] years in data platforms and AI
[Role] · [X] years in delivery and client advisory
FAQ
If you have real users, customers or lab data, it's the right time. Fixing data foundations early costs a fraction of fixing them after a Series B.
Yes. We work across sectors, from biotech and healthtech to fintech, climate, logistics and B2B SaaS. The data problems are surprisingly similar.
We pick what fits your stage and budget: often Postgres, BigQuery, Snowflake or Databricks, dbt and your existing BI tool. No over-engineering.
Least-privilege access, encrypted environments and masked data in development. We work in line with GDPR, Swiss nLPD and, where relevant, HIPAA.
No. Every project ends with documentation and a hand-over. Ongoing support is optional.
Scope and price are agreed before we start. The audit is fixed scope; projects are priced by milestone; the fractional team is monthly.
Let's talk
Book a free 30-minute call with a partner. You'll leave with a first view of your biggest data gaps and quickest wins, whether or not we work together.
Book a free 30-minute callPrefer email? Write to hello@clearpipedata.com