Production AI does not live in a notebook. It runs on Kubernetes, talks to ERPs, lands in compliance-grade systems, and rides on data pipelines that hold up under load. Arc10 ships the AI and the infrastructure underneath it — one team across the stack.
Production AI — agent platforms with traced, bounded runs, RAG over real document sets, multi-provider routing with failover, AI features built into your product. The work that takes AI from a notebook demo to a system on call at 3am. Same team that ships auramind is the team that ships yours.
Production Kubernetes — autoscaling against the metrics that actually predict load, not the ones the cloud provider ships by default. Custom HPA on queue depth and request latency, Prometheus + Grafana for observability, infrastructure-as-code so the cluster is reproducible. The work that lets a small team run a system a much larger team would otherwise need to staff for.
ETL pipelines, Airflow DAGs, the rebuild work that turns a brittle batch system into something you can extend without breaking last week. Idempotency, backfills, schema evolution — the parts of data engineering most teams discover the hard way the second time they ship a regression.
Microsoft Business Central, Dynamics 365, SFTP-based EDI, payment and shipping APIs. Integration logic that sits in version-controlled middleware — not in the AL surface — so the ERP stays clean and the business logic evolves on its own cadence.
PCI-DSS-aligned design, append-only audit logs, government-reporting interfaces, real-time pipelines that survive an audit. The architectural posture (service isolation, tokenization, scoped access) is enforced by the system, not by convention.
React, React Native, Next.js, Python and Java backends, Kubernetes deployments — full-stack from one team. The kind of build where the API, the mobile client, the admin console, and the deployment pipeline are designed together rather than handed off across vendors.
Most growing software companies end up coordinating multiple vendors — one for the AI, one for infrastructure, one for integrations, one for the mobile app. Each handoff is a place the system can drift, and the coordination cost is paid by the in-house engineering lead who least has time for it.
Arc10 is one team across all of it. The senior engineer wiring the Kubernetes autoscaler is the same one writing the integration tests against Business Central, on the same Slack as the engineer shipping the AI feature into the product. The system stays coherent because the people building it stay in one room.
Honest list, not a logo wall. The tools we run in every day, with the work that proves it linked above.
Need infrastructure-grade engineering without the enterprise overhead? The senior engineers who ship the AI and the systems underneath it will take the call.