Help your clients say yes to AI.
Aeroxis is a senior AI engineering delivery partner for agencies, consultancies, MSPs, and contractors. We help you scope, build, and productionize AI agents, workflow automation, digital twins, and custom AI-enabled software behind your existing client relationships.
Your clients are asking about AI. Delivery is the hard part.
Many service firms can sell strategy, transformation, cloud, CRM, ERP, or operations work. But when a client asks for an AI agent, document automation workflow, internal copilot, or AI-enabled operating system, the delivery risk changes.
Aeroxis plugs in as the senior technical partner: scoping the right workflow, building the first useful pilot, integrating with real systems, and hardening it into production when the economics are proven.
- For partners: white-label or named subcontracting AI engineering capacity.
- For direct clients: focused workflow automation projects with measurable business value.
- For regulated/technical teams: post-quantum and security-aware automation where evidence and controls matter.
- For complex data work: digital twin, DevOps, CI/CD, and cloud delivery experience from real client engagements.
Resellable offers
We keep the offer ladder simple so partners can explain it to clients without turning every conversation into a science project.
- AI Workflow Opportunity Audit: 1–2 weeks to turn vague AI interest into a prioritized workflow map, ROI/risk scorecard, and pilot scope.
- AI Agent Pilot: 3–6 weeks to build one working AI-enabled workflow with integrations, human review, and a production path.
- Production AI System: harden a proven pilot with auth, monitoring, evaluations, guardrails, training, and support.
- PQC Snapshot / Readiness Assessment: identify quantum-vulnerable cryptography across code, TLS, certs, and infrastructure for teams with long-lived sensitive data.
Proof from actual delivery
Aeroxis is not a demo shop. Past work includes DevOps and GitLab CI delivery for STCNET-style enterprise environments, NOAA digital twin and satellite data work, and cloud/software engineering across production systems.
The pattern is the same: understand the operational system, build the right automation layer, and leave behind something the client can run.