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.
Verify a provenance credential by link or code
Hosted lookup for Witness Mark certificates. Open a shared verify link, or enter a certificate id. No public research demos on this site.
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
Rules versus classical ML on imbalanced payments: precision, recall, and euro cost at the operating threshold. Numbers ship with a dated script.
Vertical shorts for a few documented builds. Long-form write-ups stay on this site.
We review how data moves today, where ML fits, and what it takes to keep models honest as new data arrives. No cost, no commitment.
What you collect, where it lands, and gaps that block reliable analytics or models
When automation tools produced large patches or notebooks nobody wants to own in production, we read them against operational reality
Concrete next steps for training refresh, deployment, monitoring, and rollback, scaled to your team
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.
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