Full-Stack AI builds

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.

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Build pipeline9deploys this week

Scope

Spec · architecture

running

Build

Full-stack + AI core

queued

Ship

Tested · deployed

queued

Complete

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09:41:00tests.pass 142/142

09:40:59schema.migrate ✓

09:40:58auth.wired · sessions ok

09:40:57evals.pass · core loop

↳ Built on the stack that ships

Claude CodeAgent SDKn8nRailwayVercelSupabase

[ 000 ]Trusted by operators

[ 01 ]What it is

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 that · ✓ this

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.

[ 02 ]The status quo

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 demo looks magic and collapses the moment real users, real auth, and real data hit it.

An agency hands you a prototype you can't extend or maintain - the dependency outlives the deliverable.

AI got bolted on as a gimmick instead of wired into the core where it earns its place.

The build takes a quarter and ships to a market that already moved - you bet big before you learned anything.

[ 03 ]What we build

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.

Scoping + architecture

We cut to the smallest version that proves the thing - the core loop, the real users, the one feature that has to work - and write the architecture before a line of code. Most builds fail by building the wrong thing well.

Full-stack foundation

Next.js front end, typed APIs, real auth and sessions, Stripe payments, and a database with migrations - the boring, reliable software a demo skips and a product can't.

The AI core

Agents, a RAG layer, or direct model calls wired in where they create genuine product value - and kept out of the paths that should stay deterministic and cheap.

Evals

A test suite on the AI core - golden datasets and LLM-as-judge scoring on the loops that matter - so the smart parts are proven, not vibes-checked.

Deploy + observability

Shipped to production on your infra with CI, traces, error tracking, and cost visibility - it's live and watched, not a sandbox that quietly drifts.

[ 04 ]How it works

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.

Scope

Build

Full-stack and for real: Next.js, typed APIs, auth, payments, a database with migrations, and the AI core wired in properly - agents, retrieval, or model calls where they earn product value, boring and reliable everywhere else.

Ship

Tested, observable, and deployed to production on your infra - landed in your repo, not a Figma prototype or a sandbox that rots. You own the codebase the moment it goes live.

How we engineer it

Scope ruthlessly

Perfection is the enemy of profit. We cut to the version that proves the thing, then build that to a real standard - not a throwaway demo.

Engineer for production

Auth, data, error handling, and observability from day one. The difference between a junior with Claude Code and a senior firm is what happens when real users hit it.

AI where it earns it

We add agents, RAG, or model calls where they create real product value - and keep the rest boring and reliable.

Ship to your repo

Everything lands in your repository and runs on your infrastructure. You own the codebase the moment it ships.

[ 05 ]Example builds

What this looks like in the wild.

SaaS MVP

A real first version of your product - users, billing, the core AI loop - launched in weeks so you can put it in front of customers and learn from real usage.

AI feature in your app

An agent, copilot, or RAG search dropped into your existing product and engineered to your codebase's standards, not duct-taped beside it.

Internal tool

A custom tool your team actually uses - built around your workflow instead of bending you to a SaaS you'll outgrow and keep paying for.

AI-native API or service

A backend service that wraps your AI core behind a clean, typed, rate-limited API - so other teams or customers can build on it reliably.

[ 06 ]By the numbers

The reliability that ships.

Weeks, not quarters

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.

Production-grade

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.

100% in your repo

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.

[ 07 ]The stack

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.

Next.js + TypeScript

Type-safe full-stack React - the production default for AI products, with server actions and streaming UI for responsive model output.

Claude (Anthropic API)

The AI core - reasoning, agents, and grounded generation with native Citations - frontier capability wired in where it earns product value.

Claude Agent SDK + MCP

Production agent loops and standard tool connectors when the product needs to act, not just generate - the same harness Claude Code runs on.

Supabase / Postgres

Auth, Postgres with migrations, and pgvector for the RAG layer in one system - so the data foundation is real from day one, not a toy.

Stripe

Real billing, subscriptions, and metered usage with verified webhooks - because a product without payments is a prototype.

Vercel

Push-to-deploy hosting with edge functions and previews on every PR - the product ships continuously, in your account.

Braintrust / Langfuse

Evals and tracing on the AI core, so the smart parts are measured and observable in production - not shipped on faith.

[ 08 ]The architectural difference

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.'

· Dimension

· Off-the-shelf SaaS

· Anfloy custom

Fit

You bend your workflow to their product.

Built around your actual workflow and edge.

The AI

A generic feature, same for every customer.

Your AI core - agents, RAG, your data, your moat.

Cost shape

Per-seat rent forever, rising with headcount.

A fixed build. An asset on your books, not a tax.

Roadmap

You wait in line for features they prioritize.

You set the roadmap. We ship to it.

Data

Lives in their cloud, on their terms.

Lives in your repo, on your infra.

Ownership

You're a tenant. They can change the deal.

Built once, yours forever. Deployable anywhere.

Fit

Off-the-shelf SaaSYou bend your workflow to their product.

Anfloy customBuilt around your actual workflow and edge.

The AI

Off-the-shelf SaaSA generic feature, same for every customer.

Anfloy customYour AI core - agents, RAG, your data, your moat.

Cost shape

Off-the-shelf SaaSPer-seat rent forever, rising with headcount.

Anfloy customA fixed build. An asset on your books, not a tax.

Roadmap

Off-the-shelf SaaSYou wait in line for features they prioritize.

Anfloy customYou set the roadmap. We ship to it.

Data

Off-the-shelf SaaSLives in their cloud, on their terms.

Anfloy customLives in your repo, on your infra.

Ownership

Off-the-shelf SaaSYou're a tenant. They can change the deal.

Anfloy customBuilt once, yours forever. Deployable anywhere.

[ 09 ]Who it's for

The honest fit check.

Build this if

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.

Skip it if

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.

[ 10 ]Questions

The honest answers.

Q01

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.

Q02

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.

Q03

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.

Q04

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.

Q05

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.

Q06

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.

[ 11 ]Keep going up the ladder

RunInfrastructure & HostingWe don't just build it - we host and operate it on real infra.RunMaintenance & EvolutionSystems that get sharper every month, not stale.AutomateWorkflow AutomationDeterministic workflows with LLM steps - wired into your stack.

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