Product
Shipping software, not a slide deck
OctOpus is already live. Users can upload real data, transform it, run research loops, edit code and plans, and move from exploration to deployable outputs in one product.
For investors
OctOpus automates recurring data science for enterprise teams.
Enterprise teams give OctOpus data and a business objective, and it builds, validates, and delivers the model and report — without requiring a data scientist to operate it. The same product (an autonomous AI data scientist under the hood) supports self-serve users, team adoption, and private enterprise deployment, with investor materials available on request.
Founder-led · pre-seed
Live product, public distribution, paying users, and active enterprise demand. Customer detail, revenue
metrics, pipeline, and round terms are shared in the private deck.
Commercial signal
Bottom-up adoption with enterprise expansion
Self-serve pricing captures individuals and small teams, while larger organizations pull the product toward governance, collaboration, and private deployment.
Why now
Data science workflows are becoming agentic
The category is moving beyond dashboards and static AutoML. OctOpus frames the task, runs experiments, recovers from failures, and returns a model plus decision-ready output.
Doudou BA
Founder · applied ML, optimisation, and production systems
OctOpus comes out of real machine-learning work in production settings: forecasting, optimisation, automation, deployment, and the messy loop between them. The company is being built by someone close to the problem, shipping quickly, and designing for both individual adoption and enterprise expansion.
Customer references, revenue detail, pipeline numbers, and round specifics are shared privately.