Deep-Tech Engineering
Algorithms, data pipelines, and hard technical problems solved. When the problem is the code, not the UI.
What's included
Algorithm Development
Signal processing, optimization, numerical methods. We write the math your product depends on.
Data Pipelines
ETL, streaming, batch processing. Reliable data infrastructure that scales.
ML & Inference
Model integration, edge inference, performance tuning. Production ML, not prototypes.
Hardware Integration
IoT protocols, sensor data, embedded systems communication. Software meets physical.
Technical Documentation
Algorithm specs, validation reports, integration guides. Your team understands every decision.
Validation & Testing
Unit tests, integration tests, statistical validation. Provably correct, not just working.
Proven track record
ProxControl
Industrial velocity drift, solved. Stabilized spray-paint velocity and painting-state detection for layer-thickness analytics across 2 algorithm phases.
Frequently asked questions
What counts as deep-tech engineering?
Problems where the core challenge is algorithmic or scientific, not just connecting APIs. Computer vision, signal processing, ML model optimization, industrial IoT, data pipelines, and custom algorithms. If the hardest part is the math, that's deep-tech.
Can you work with our existing codebase?
Yes. We frequently integrate with existing systems. We audit your current architecture, identify the technical bottleneck, and build the solution to plug in cleanly.
How do you validate technical solutions?
We start with a proof-of-concept sprint (typically 1 to 2 weeks) to validate the approach before committing to a full build. You see working results before the bulk of the budget is spent.
What industries do you work with?
We've built deep-tech solutions for industrial automation (ProxControl), real estate (Morta), fintech, and consumer AI products (Automaticall). The technical patterns transfer across industries.