About

Most companies don't have an AI problem. They have a deployment problem.

The models are there. The APIs are there. What's missing is someone who can take a promising prototype and turn it into a system that runs reliably in production, handles edge cases, doesn't cost a fortune at scale, and that your team can maintain after we leave.

What we actually do

Cartesian Trees is an AI solution partner based in Paris. We work alongside your engineering team to design, build, and deploy AI-powered systems.

Where we come from

We started with a research question: what if your database could reason under uncertainty? That led us to build Bayesian inference algorithms directly into PostgreSQL (MCMC sampling, Gibbs sampling, variational inference), running where the data lives instead of in a separate Python process. That research earned us Jeune Entreprise Innovante (JEI) status from the French government, their recognition that a company is doing genuine R&D, not just applying existing tools.

That background shapes how we approach everything. We understand the math underneath, which means we can debug problems that purely-integration-focused teams can’t, and we know when a fancy approach is overkill and a simple one will do.

As AI moved from research to product, from Bayesian models to LLMs, from batch inference to real-time agents, we moved with it. Today we build LLM-powered applications, agentic systems, RAG pipelines, and the full production stack around them. The foundation is the same: understanding the technology deeply enough to make it work reliably, not just impressively.

How we work

Instead, we start by understanding what business decision you’re trying to improve or what workflow you’re trying to automate. Then we build a working prototype against your actual data, usually within the first couple of weeks, so we can see what works and what falls apart early, before you’ve invested months. Once the approach is validated, we do the harder, less glamorous work of making it production-ready: evaluation pipelines, error handling, cost optimization, monitoring, and proper testing.

We work embedded with your team throughout. When the project is done, your engineers own the system and understand it well enough to extend it. We’re not interested in creating dependency. We’d rather you call us for the next problem because the last engagement went well, not because you can’t maintain what we built.

Why JEI matters

The Jeune Entreprise Innovante certification is awarded by the French government to companies where R&D is a core part of the business, not companies that just use AI, but companies that advance it. It means our expertise is built on original research, not just OpenAI API wrappers. It also means we approach problems with a researcher’s rigor: we measure, we evaluate, we don’t ship things we can’t prove work.

Who we are

The person you'll actually work with

Ayush Tiwari Founder, Cartesian Trees · Paris, France

Engineering degree from IIT Roorkee (B.Tech, 2012–2016). Before starting Cartesian Trees, Ayush worked with American and French companies across a wide range of engineering challenges: contributing to Stripe's payment infrastructure, building Facebook Conversions API integrations, contributing to Metabase (open-source BI), and working on Pyodide (Python in the browser).

What we work with

Depth where it matters

Whatever fits the problem. The areas below are where our depth lives.

AI and data

  • LLMs (OpenAI, Anthropic, Mistral, OSS)
  • Agentic frameworks
  • RAG systems
  • Bayesian inference (PyMC)
  • NLP with spaCy
  • Embeddings and vector search
  • Evaluation frameworks

Backend

  • Python
  • Django
  • FastAPI
  • PostgreSQL (deep)
  • asyncpg
  • SQL

Frontend

  • React
  • Next.js
  • TypeScript

Infrastructure

  • GCP
  • Azure API Management
  • Kubernetes
  • Docker
  • CI/CD (GitHub Actions)

Data

  • NumPy
  • SciPy
  • Pandas
  • Metabase
  • Pipeline engineering
  • Scraping and extraction

Open source

  • Stripe API
  • Facebook CAPI
  • Metabase
  • Pyodide
  • postgres-bayes (ours)

Trying to figure out where AI fits, or hit the wall between demo and production?

Let's talk