From idea to a product that ships.
A real, customer-facing AI product - auth, billing, data, the AI core wired in properly - in front of users in weeks, landed in your repo, not a prototype that rots.
The capability, defined.
Sometimes the answer isn't an internal agent - it's a product. An AI feature inside your app, a standalone tool, a SaaS MVP. We build production software with AI at its core: real auth, real data, real reliability - the engineering a demo skips.
Not a Figma prototype. Not a demo that falls over with real users. Not a dev body-shop bolting AI on as a gimmick. It's production software with AI at its core, engineered for reliability and shipped into your repository, that you own the moment it goes live.
What this costs you today.
You have an AI product idea - a feature, a tool, an MVP - and the options are a slick demo that can't take real traffic or a quarter-long agency build that misses the market.
The anatomy of the system.
The gap between a junior with Claude Code and a senior firm is everything that happens after the demo - auth, data, error handling, evals, observability. That engineering is the product.
Engineered, not prompted.
We ship in tight sprints on Claude Code, the Claude Agent SDK, n8n, Railway, Vercel, Cloudflare, and Supabase - the same stack we operate in production for clients.
What this looks like in the wild.
The reliability that ships.
The build window for a focused MVP when you scope to the core loop first - the difference between learning from real users now and betting big before you've learned anything.
The standard a real product needs and a demo skips - auth, migrations, error handling, evals, observability - which is exactly where most AI prototypes fall over.
Where the codebase lives the moment it ships - no platform tax, no per-seat rent on your own product, no roadmap you don't control.
↳ 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 buy an off-the-shelf SaaS?
Off-the-shelf software is the fastest path - until you hit the wall where the tool ends and your actual workflow begins. A custom build costs more up front and pays you back as an asset you own, shaped to your edge, with no per-seat tax and no roadmap you don't control. The 2026 question isn't 'what tool do we rent' - it's 'what work do we want to own outright.'
The honest fit check.
Founders and product teams who need a real AI product - a SaaS MVP, a customer-facing feature, or an internal tool - built to a production standard and owned outright, fast enough to learn from real users.
If an off-the-shelf SaaS already fits your workflow cleanly, buy it - a custom build only pays off where the tool ends and your real edge begins. And if you need a 20-person platform team scaling a mature product, you need to hire in-house, not contract a build.
The honest answers.
How is this different from a dev agency?
We're AI engineers, not a body shop. We build the AI core properly - agents, retrieval, evals - and the production software around it, and we hand you a system you own and can extend. A typical agency ships a prototype and a dependency; we ship production code into your repo and a clean handoff. The work compounds as your asset; the reliance on us doesn't.
Do we own the code?
Completely. Everything ships into your repository and runs on your accounts, your keys, your infrastructure - the front end, the APIs, the AI core, the evals, all of it. Built once, yours forever: no platform tax, no per-seat rent on your own product, no lock-in. The day it launches, the codebase is yours to run, extend, or hand to another team.
What happens when it breaks?
It's engineered so breaks are caught, not catastrophic. We build in error handling, tracing, and error tracking from day one, evals on the AI core so model regressions get flagged, and CI so a bad change doesn't reach production. You get observability into what's failing and why, and we can operate and maintain it on a loop or hand you the runbook so your team can. The difference from a demo is precisely what happens when real users hit an edge case at 2am.
How long to ship?
A focused MVP typically ships in weeks, not quarters. We scope ruthlessly to the smallest version that proves the value, build that to a real production standard, and launch - then iterate from actual usage instead of guessing. Larger products take longer, but we ship in working increments behind feature flags, so you're seeing and testing real software early, not waiting for one big reveal at the end.
Does it run on our infrastructure?
Yes - it deploys to your accounts: Vercel for the app, Supabase or your own cloud for data and auth, your keys for the model APIs. Nothing routes through an Anfloy server, and your data and your users live in your perimeter. For sensitive or regulated workloads we self-host the whole stack on your infrastructure so nothing leaves your network.
Can you build on our existing codebase, or only greenfield?
Both. We regularly drop an AI feature - an agent, a copilot, RAG search - into an existing product, matching your stack, conventions, and review standards so it reads like your team wrote it, not like a foreign module bolted on. We start by reading your codebase and architecture, scope the integration to your reality, and ship via PRs your team reviews - so you keep control of your own product the whole way.