Your clients want EO + AI
Your teams are stuck in data pipelines
Dataionics removes the data engineering bottleneck so you can deliver faster. You build the applications. We industrialize the data layer.
3→6 months → days
Delivery cycle compressed
×3
Faster project delivery
2,500+
EO collections indexed
∞
Reusable pipelines
The bottleneck is not AI. It is not analytics.
Every EO project goes through the same hidden grind before any real work can start.
Sourcing data
Across fragmented providers, with no unified API or catalog.
Building ingestion
Pipelines built from scratch for every new engagement.
Harmonizing formats
Incompatible formats, projections and resolutions across sources.
Preprocessing per client
The same grunt work repeated for every new dataset and geography.
Most EO projects fail to scale
because the data layer
is too complex to industrialize
The industry is hitting a structural wall
The data engineering bottleneck is not a project risk. It's a structural drag on every engagement.
From months to weeks
What this looks like on real projects.
Before
8–12 weeks
data prep
Custom per-project pipelines built from scratch.
After
Day one
ready-to-use
Harmonized datasets delivered. Teams focus on analytics.
Before
Manual
sourcing
Heavy preprocessing at every step of the project.
After
Unified
multi-source
Standardized, repeatable delivery across engagements.
Before
New pipeline
every use case
Non-reproducible outputs across projects and clients.
After
Reusable
data layer
Audit-ready architecture across the full portfolio.
We act as your data infrastructure layer
Clear separation of roles. Zero overlap.
You - Integrator / Consulting Firm
Application & client ownership
- Define the use case
- Build the application
- Manage the client relationship
- Deliver business value
Dataionics
Data layer ownership
- Source EO data across providers
- Orchestrate ingestion pipelines
- Prepare and harmonize datasets
- Deliver reliable inputs in your cloud
This is not a replacement model. It's a reinforcement model.
You keep the client. You keep the application. We handle the data layer.
Free exploration of the EO catalog. No account required.
Designed for integrators, not end users
If your engagements depend on EO data, the data layer is your structural constraint, not your team's skill.
From first conversation to live data layer
No long sales cycle. No upfront commitment.
Discussion
Review current EO projects and data constraints. Direct conversation on model fit.
Use case
Select a relevant scenario from your portfolio where the data layer is a pain point.
Pilot
Define and deploy an adapted data layer. Your team evaluates integration.
Scale
Extend across projects. Reusable pipelines, better margins, faster delivery.
Ludovic Auge
CEO, Dataionics. Ex-Airbus Defence & Space (OneAtlas, Copernicus DIAS). 15+ years in EO data infrastructure.
Why Earth Observation projects stall in the data layer
“The conversation in our industry still orbits around models and detection algorithms. But the structural failure mode for EO projects is upstream. Integrators spend 3 to 6 months fighting fragmented sources, incompatible formats and missing time series before a single analytic runs. That's not a capability gap. It's an industrialization gap, and it's exactly what a dedicated data layer solves.”
Read the full insight6 min read