Speed at every layer of your stack
We compress the distance between data and decision: AI systems, automated pipelines, and performance-critical software. Speed is the constraint we design around, and every claim below points to something we have shipped.
Numba JIT compilation, CPU/GPU routing, model quantization and sparsity, caching, and parallelized containers. Systems built to run at full throughput.
Faster convergence in training, causal A/B frameworks that shorten the decision of which model to ship, and monitoring that catches drift after go-live.
Parallel calls, minimal round-trips, and orchestration tuned for latency and cost, with people set up to lead the agents instead of babysitting them.
Quantum-safe data encoding in production today, plus a research track aimed at the performance ceiling classical compute has not reached yet.
Live systems we ship, with write-ups that explain how they work
Human-in-the-lead learning for teams that use AI agents
When agents draft and execute well, the bottleneck is rarely another approval click. Someone still needs domain knowledge to set direction, judge outcomes, and step in when judgment matters. Academy builds that skill through structured courses, practice cards, live sessions, and study groups on studdyco.study.
Privacy-preserving encoding for machine learning
Encode-then-mix pipelines that keep models useful while blunting reconstruction of sensitive fields.
Swiss travel planning with SBB data
Trip planning assistant with SBB real-time schedule integration. The next public release is in preparation.
Write-ups covering the full path from problem to working system
From simple rules to ML-powered fraud prevention that saved $2M annually.
Vertical shorts for a few documented builds. Long-form write-ups stay on this site.
We look at your current AI setup and identify where speed is being left on the table, in inference, training, agents, or monitoring. No cost, no commitment.
We look at your inference stack, training loop, and agent pipelines to find where time and cost are being lost
If you already have models in production, we review them for latency, throughput, and cost, then show where optimization gives the biggest return
Based on what we find, we suggest concrete next steps with estimated effort
Book a 30-minute call
A short call to see whether your data or ML question is a fit. No commitment: if we are not the right team, we will say so plainly.
Scheduling is not configured here yet. Email info@arraxis.com and we will suggest times.
Fill out the form and we will get back to you within 1-2 business days.