Data-driven, production-ready solutions, with MLOps as it should look like.
Typical starting point
Ad hoc files and duplicate extracts
Spreadsheets, one-off exports, and copies in shared drives. Access rules are unclear, and ops and analytics rarely see the same numbers.
With Arraxis
Structured, defensible storage
Schemas you can explain, access paths you can audit, and pipelines that refresh analytical and operational views on a schedule someone owns.
Typical starting point
Fast output, thin accountability
AI-assisted workflows can ship large patches or notebooks before the team has traced the path to production. Prompts and merges accumulate; what actually runs becomes hard to explain or sign off.
With Arraxis
Sagas and stories with explicit scope
Large work is split into sagas and stories. Each slice is reviewed on its own before it ships, so you can follow what changed and why.
Typical starting point
Dashboards that lag reality
Charts that look healthy until a customer complains. Drift, data-quality breaks, and model regressions surface late, if at all.
With Arraxis
Signals tied to production behavior
Evaluation hooks on live traffic: drift, quality breaks, and failure modes you can act on, with owners who know what to change when the world moves.
Typical starting point
Templates that stop at accounts and integrations
Wix, Squarespace, and similar builders ship marketing pages quickly, then cap out on customer accounts, custom workflows, and data you control.
With Arraxis
Migration with a maintainable surface
We move you off the ceiling without a big-bang rewrite: a product UI your team can extend, plus a handoff path so non-developers can run routine updates through guided steps.
Write-up previews
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 data setup and identify where data science methods could be applied. No cost, no commitment.
We look at what data you have, how it flows, and what you are not using yet
If you already have data pipelines or models, we review them for performance and cost
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.