We don't just build it. We run it.
Your AI systems run in production around the clock - deployed on infrastructure you own, monitored, cost-capped, and yours to operate or hand to your team.
The capability, defined.
An AI system that only runs on someone's laptop isn't a system. This is the part most firms skip: real deployment, real uptime, real observability. We put your agents and workflows on infrastructure you own, with the monitoring and guardrails to run them like production software - because that's what they are.
Not a prototype handed off with a 'good luck.' Not a rented agent platform that owns your logic and your data. It's real deployment on standard infrastructure in your accounts - Railway, Vercel, Cloudflare, Modal, or your own cloud - operated like the production software it is.
What this costs you today.
The build works on someone's laptop, and that's exactly where most agencies leave it - which means it isn't a system yet.
The anatomy of the system.
Deployment is the part most firms skip, and it's where production reliability actually lives. We put your systems on standard infra in your accounts, with the operational layers that keep them up and keep them affordable.
Engineered, not prompted.
We deploy and operate on Claude Code, the Claude Agent SDK, n8n, Railway, Vercel, Cloudflare, and Supabase - the same infrastructure we'd run for ourselves.
What this looks like in the wild.
The reliability that ships.
Cold start for a Cloudflare Workers V8 isolate - roughly 100x faster than a traditional container - so edge-deployed agent code spins up instantly instead of stalling on first request.
Share of GenAI deployments that instrument observability today (Gartner) - the operational gap that separates a system you can run from one that breaks blind. We close it.
Where everything runs - Railway, Vercel, Cloudflare, Modal, or your own cloud - so there's no proprietary platform to escape and you already hold the keys.
↳ Industry benchmarks and engineering standards, not Anfloy client metrics - we report your real numbers once you're live.
Named tools, and why.
The model is fungible - the system is the moat. Here's what we build it on, and the reason each earns its place.
Why not just a rented agent platform?
Rented agent platforms get you a demo fast, then own the result. Your logic lives in their UI, your data flows through their cloud, and the bill scales with usage on terms you don't set. We deploy the same systems on standard infrastructure in your accounts - so when you want to leave, there's nothing to leave. You already hold the keys.
The honest fit check.
Teams that have an AI system - built by us or already in hand - and need it deployed, monitored, and operated like production software, on infrastructure they own and can take over anytime.
If you have a mature platform team and standardized internal infra, you likely just need the code and the runbook - we'll hand those over and step back. And if it's a one-off script run by hand a few times, full production hosting is overkill we won't sell you.
The honest answers.
Why does hosting matter - can't we just run it ourselves?
You can, and we set you up to. The point is that a real AI system needs deployment, secrets management, monitoring, autoscaling, and cost controls to run reliably - and that's engineering, not a checkbox. We do it so the system is production-grade on infrastructure you own, then you can operate it yourself, have us run it, or hand it to your team. The choice stays yours because nothing is locked to us.
Do you lock us into your platform?
No - the exact opposite is the point. Everything runs on standard infrastructure - Railway, Vercel, Cloudflare, Modal, or your own cloud - in your accounts, under your keys. There's no Anfloy platform to be trapped in and nothing proprietary to escape. When you want to leave, there's nothing to leave: you already hold the keys and the code is already in your repo. That's the whole differentiator from a rented agent platform.
What happens when something breaks at 2am?
It pages a human instead of rotting silently - that's the entire reason this layer exists. We wire health checks, retries, and alerting into every system, with dead-letter queues for work that keeps failing and traces that show exactly what broke and why. We can operate it on call for you, or set your team up with the dashboards and runbooks to respond. Either way the failure is loud and diagnosable, not a quiet outage you discover from a customer.
Can you keep our data fully private?
Yes. For sensitive or regulated workloads we self-host the entire system on your own infrastructure, so data never leaves your perimeter and nothing trains a public model. Secrets live in your vault, traffic stays in your network, and access is least-privilege. We'll architect to your compliance requirements - and tell you honestly where a managed service is fine versus where self-hosting is worth the operational cost.
How do you stop an autonomous agent from running up a huge bill?
Cost guardrails are part of the deploy, not an afterthought. We set per-run token and spend budgets, cap loop iterations, add rate limits, and alert on spend before it becomes an invoice - so a runaway loop hits a ceiling instead of your credit card. You get cost attribution per run through the traces, so you can see what each system actually costs and tune it. Autoscaling also means you're not paying for idle capacity between spikes.
How long does it take to get our system into production?
Deploying an existing build to real infrastructure with monitoring and guardrails is typically days, not weeks - the longer pole is hardening, load-testing, and tuning autoscale and budgets to your real traffic. We ship a production deploy first, then layer in the observability and cost controls, so you're live quickly and getting steadily more robust rather than waiting for a perfect setup before anything runs.