For teams without EO AI in-house

We turn EO SOTA into production systems your team can run.

Hyperalis Labs helps startups and companies adopt state-of-the-art Earth observation AI without building a research group from scratch. We implement modern models, engineer reliable pipelines, and transfer the technical know-how your team needs to operate them.

SOTA model implementation
Production-ready EO pipelines
Technical enablement and training
Infrastructure and utilities monitoring from satellite data Infrastructure and Utilities
Agriculture and forestry monitoring from earth observation imagery Agriculture and Forestry
Climate and land-use change monitoring from EO data Climate and Land
Synthetic data generation for EO model training Synthetic Data

What we deliver

You bring the problem and business context. We deliver the research translation, engineering execution, and technical handover required to run EO AI in production.

Research Translation and System Design

Map your use case to current literature, select viable model families, and define architecture, data contracts, and evaluation protocol.

Model, Pipeline, and MLOps Engineering

Build training and inference pipelines for optical and SAR data with reproducible experiments, versioned artifacts, and deployment-ready interfaces.

Team Enablement

Ship documentation, training sessions, and runbooks so your engineers can maintain, monitor, and extend the solution after launch.

How we engage

Each engagement follows a technical path that keeps method choices explicit, engineering quality high, and ownership transferable to your team.

1
Scoping and technical baseline Define target decisions, data availability, and measurable success criteria before model work starts.
2
Build and hardening Implement and benchmark candidate methods, then harden the best approach for reliability, latency, and operational cost.
3
Handover and iteration Transfer code, infrastructure knowledge, and monitoring practices, then continue with structured improvement cycles.
Spec-driven
Clear architecture and interface decisions before implementation
Evaluation-gated
Promotion based on agreed technical metrics, not intuition
Operator-ready
Handover materials designed for internal engineering teams

What success looks like

From day one, we structure work around measurable outcomes: faster decision cycles, reliable model behavior, and clear operational ownership.

Faster Time-to-Value

Shorten the path from new imagery to usable outputs for product and operations teams.

Reliable Technical Foundation

Use reproducible pipelines and explicit evaluation criteria to reduce model and deployment risk.

Internal Capability Growth

Build internal confidence through technical handover, training, and operational runbooks.