Production-ready Java backends, generated from your SQL schema. Hours, not months.
A production-ready, 95–100% tested backend in hours. Hexagonal, vertical-sliced, monolith or microservices. Your team builds the business logic on top.
You see it running and test every endpoint before any payment.
Your schema stays confidential. NDA on request.
- Java 25
- Spring Boot 4.1
- Hexagonal
- Vertical slicing
- 95–100% tests
See it in action
From a SQL schema to a running, tested backend
A short walkthrough: schema in, hexagonal backend out. Generated and verified.
The wrong assumption
“We’ll just hire devs + AI and build it faster and cheaper.”
Reasonable, and wrong. Before a single business rule, weeks-to-months go into setup, architecture decisions, scaffolding every entity across every layer, and the test foundation. That is exactly the part that silently rots into hidden coupling.
That part is what AlveoForge delivers, correct and tested, in hours. We don’t compare against your whole build. Your business logic stays yours; we compare against the production-ready structural layer any team pays for either way.
Why it’s different
Why a dev team + AI can’t replicate this
AI drifts to legacy patterns
AI learned mostly from public code — classic layered MVC. Ask it for real hexagonal and, unsupervised, it drifts toward what it knows. It looks hexagonal; it isn’t.
Deterministic, not probabilistic
Same schema, same output, every time. Audit it once and trust every build. A human + AI team produces something different each run.
Fixed at the source
A bug found once is fixed for every future generation. Conventions and structure are enforced by construction, not by hoping code review catches the drift.
How it works
Guided, end-to-end
- 1
Kick-off call
You share your SQL schema; we review it together and spot edge cases.
- 2
Configuration session
We complete the enrichment file together — modules, boundaries, options. You bring the domain; we handle the technical translation.
- 3
Generation & verification
AlveoForge generates the complete backend; the output is verified before anything is shared.
- 4
Try before you buy
A live instance and a full interactive API preview (Swagger). Every endpoint, testable, before any payment.
- 5
Delivery
Once you’re satisfied, the codebase lands in a private GitHub repo, ready to clone.
By the numbers
The difference, measured
Validated on a real 300+ table schema.
Under the hood
Inside every generated entity
Most tools generate empty shells: hexagonal folders, stub CRUD, no real behaviour and no tests. This is what AlveoForge generates, and verifies, for every entity.
Two migration engines — and your database adopted
Flyway or Liquibase, with full parity. Or point it at your existing production database and it adapts (brownfield). Your DDL is the single source of truth: no schema drift.
Single and bulk, everywhere
Every entity ships individual and batch operations: create-many, fetch-by-ids, bulk updates, each one validated and tested.
Relationship expansion on read
GET endpoints hydrate related entities on demand, across vertical slices, with no N+1 queries and no JPA object graph leaking across your layers.
The full HTTP contract, tested
Every endpoint is tested for success and for failure, error paths included. Across all 3 layers plus e2e, asserting methods, lines and branches.
Login, hashing, JWT and refresh tokens
A real login entity, password hashing at the persistence edge, JWT (in-process or service-to-service HTTP), refresh tokens inline or in a dedicated table.
Architectural rulings, applied consistently
Scalar foreign keys (no ORM graph bleeding across layers), N-N as first-class join-table entities, soft-delete from audit columns, composite, natural and identity keys. The rulings that separate “it compiles” from “it’s correct”.
Scope
What you get
You receive
- ✓Complete, running backend in a private repository
- ✓Real hexagonal architecture + vertical slicing
- ✓Full CRUD, single & bulk operations, and relationship expansion on read (?expand=)
- ✓Full standard SQL type coverage: arrays, enums, JSONB, UUID, timestamptz, numeric precision, binary (bytea), composite & foreign keys, inheritance & partitions
- ✓PostgreSQL · MongoDB · Redis cache · transactional outbox
- ✓Database migrations (Flyway or Liquibase) + brownfield adoption of your existing database
- ✓JWT auth, refresh tokens & email verification + dev / pre / prod profiles
- ✓Test suite across all 3 layers + e2e: success & error paths (95–100%)
- ✓Monolith or microservices, your choice, no lock-in
- ✓Code fully yours + 30-day post-delivery support
You don’t
- ✕The generation factory (stays proprietary)
- ✕Row-level authorization (who may access which row): your policies on top
- ✕Your business logic and domain-specific rules (e.g. regex)
- ✕Third-party service integrations
As your project grows, each feature is generated as an independent vertical slice, added alongside your code without touching what your team already built. Hexagonal isolates the domain from database, framework and delivery; vertical slicing keeps each feature cohesive, so everything that changes together lives together. That’s what lets the backend scale in order and stay fast to work on, instead of decaying into the tangled coupling that freezes teams years in.
How buying works
You see your backend before you pay for it.
The first payment is a down-payment, not the whole project. A consultancy can’t match that, because it’s generated, not hand-written.
- 1
A call
You tell us your system and hand us your SQL schema.
- 2
You get a quote
Fifteen days to decide. No rush, no pressure.
- 3
We generate your backend
From your schema: hexagonal, tested, runnable.
- 4
You see it running, free
A private link: your API live, its Swagger, its test report and JaCoCo coverage. You haven’t paid anything yet.
- 5
First tranche
A first set of tables. You pick which, and we suggest your hardest. You read the real code before committing to the rest.
- 6
The repo is yours
Second payment, and the full repository is yours to keep.
Why we can do this and a consultancy can’t
A consultancy can’t build your backend for free just to show you. We can: the marginal cost of generating it is near zero. It’s not “we’re faster”; it’s a structural asymmetry.
Roadmap
Delivered, and what comes next
Delivered
- Real hexagonal architecture + vertical slicing
- Deterministic generation: same schema, same output
- Monolith or microservices from the same schema
- Full standard SQL type coverage
- Composite, natural & generated primary keys · N-N as first-class resources
- PostgreSQL inheritance & declarative partitioning
- Multi-store: PostgreSQL · MongoDB · Redis cache · outbox
- Migrations (Flyway / Liquibase) + brownfield adoption
- Full CRUD + bulk operations
- Relationship expansion on read (?expand=)
- JWT auth, refresh tokens & email verification
- Reference-data seeding & required defaults
- Tests across 3 layers + e2e: success & error · 95–100% coverage
- Validated on 300+ table schemas
- GitLab scale, 1,000+ tables — stress-testing the pipeline on very large schemas
In progress
- Public launch — getting AlveoForge ready to go public: site, demos and first founding clients
Long term
- Self-service platform — upload schema → preview → repo, no call required
- More languages & frameworks — NestJS, Python and C# (hexagonal), beyond Java, plus any language on client demand
- Optional reactive API — Mono/Flux, opt-in for streaming / high-I/O domains
- GraphQL / BFF read layer — alternative to REST expand for complex client graphs
- Hierarchical tree expand — parent/child trees (self-referential) expanded on read
Delivered items ship today in the guided Java / Spring Boot service. In-progress and long-term items are on the roadmap, not available yet.
About the founder
Built by someone who lived the problem

I’m Álvaro. AlveoForge started from a deadlock I watched up close: a backend that worked but had no real architecture. Rewriting it properly was the right engineering call, but it meant freezing the team for the better part of a year while clients wanted new features now. No business can afford to stop that long, and that tradeoff is exactly what AlveoForge removes. I spent 11+ months turning real hexagonal architecture at scale into a deterministic factory that delivers a production-ready backend in hours, not months. I’m its first user, validated at scale on a real system. If it convinces you, let’s talk.
LinkedIn →FAQ
Answers by role
General
Is my schema confidential?+
Yes. It’s used only to build your backend, treated as confidential, and an NDA is available on request.
What do I need to provide?+
Your SQL schema to start: the structure (tables, relationships, constraints), not your data or business logic. From there we run a short guided session to capture the domain choices that shape the output: microservice boundaries and their names, whether you want MongoDB / Redis / the outbox pattern, environment profiles, and so on. You bring the domain; we handle the technical translation.
What exactly do I receive?+
A production-ready backend for every entity: hexagonal, tested, runnable. Your business logic and integrations stay with your team. The code is fully yours, no lock-in.
Monolith or microservices?+
Your choice, from the same domain: a modular monolith or one runnable Spring Boot app per module. No rewrite when you switch.
Do I get the generation tool itself?+
No. The factory stays proprietary. You receive the output: professional, tested code you own entirely and can maintain without AlveoForge.
How is pricing set?+
Per-project, priced on your schema (size and complexity), not per table. The first payment is a down-payment: you see your backend running on your schema, with its Swagger and test reports, before you pay anything. As a yardstick, the same structural layer built in-house is four to eight months of a team.
Who owns the generated code?+
You do, completely. Modify it, deploy it, extend it, reuse it in other projects. No licence fee, no royalties, no runtime dependency on us. The only thing that stays ours is the generator itself.
What do you do with our schema?+
It’s used only to build your backend, kept confidential, and deleted after delivery. Never shared with third parties or used to train anything. Prefer to send an anonymized/renamed schema (structure without real names)? That works too.
You’re a solo founder — what if AlveoForge goes away?+
The code is yours with zero dependency on us: standard Maven, standard Spring Boot. It keeps building, running and staying maintainable whether we’re around or not. That independence is the continuity guarantee.
For tech leads
Real hexagonal, or MVC with hexagonal folder names?+
Real hexagonal with vertical slicing: business logic isolated from database, framework and delivery. It’s the whole reason the factory exists: AI-assisted teams drift toward layered MVC; this doesn’t.
How is the test suite structured?+
As a four-layer test pyramid: the industry-standard taxonomy (Fowler / Clemson), each layer its own Maven profile. Unit tests every class in isolation with its collaborators mocked, fast and deterministic, the default run. Integration exercises the persistence adapters against a real database (PostgreSQL, MongoDB, Redis) spun up with Testcontainers, not an in-memory stand-in. Component boots the whole service on a random port and drives it through its HTTP boundary: JSON contract, status codes, validation, live schema. End-to-end runs a full user journey with every collaborator real, including outbox propagation and email verification. There’s deliberately no consumer-contract layer: the OpenAPI/Swagger spec is the contract, and the component layer verifies it.
Is the 95–100% coverage real, or empty tests?+
Methods, lines and branches, measured with JaCoCo. Every path is executed and asserted, not just run. Mutation testing available on request.
Do the tests cover error paths, or just the happy path?+
Both. Every endpoint is tested for success and for failure: 400 validation, 401 auth, 404, 415, error paths included. Across all 3 layers plus e2e, asserting methods, lines and branches. Empty happy-path tests are exactly what we don’t ship.
What operations does each entity get?+
Full CRUD plus batch: create, read (single, paged, by-ids), update, delete, create-many and bulk updates, with read-time relationship expansion (?expand=…) across slices, no N+1 queries and no JPA object graph leaking across layers.
How does it fit our existing database and migrations?+
Your DDL is the single source of truth, emitted as a versioned migration. Pick Flyway or Liquibase, both wired with parity, or point it at an existing production database and it adapts (brownfield) without rebuilding your schema. Hibernate runs in validate mode, so entities are checked against the real schema at boot.
Can my team maintain code it didn’t write?+
Yes. One pattern repeated consistently, standard readable code any Java dev extends from day one, plus a 30-day support window. And you own it: no dependency on AlveoForge.
Do I need a specific IDE or build tool?+
No. The output is a standard Maven project: open it in any IDE (IntelliJ, VS Code, Eclipse, NetBeans…) or in none at all. Build, test and run it straight from the terminal with the Maven wrapper (./mvnw). No editor, plugin or vendor lock-in.
What’s the tech stack?+
Java 25, Spring Boot 4.1, hexagonal + vertical slicing, PostgreSQL · MongoDB · Redis with outbox, JWT auth, dev / pre / prod profiles.
What SQL types do you support?+
The full breadth of standard PostgreSQL types: arrays, enums, JSONB, UUID, the date/time family (date, time, timestamptz), numeric and decimal precision, binary (bytea), varchar/char length validation, composite and foreign keys, plus table inheritance and declarative partitioning. And generation never fails on a type it doesn’t recognize: it falls back to a safe default (string) that your team can refine later, so you always get a running backend. We flag any such column together in the kick-off, no surprises. That’s why the process is guided.
For agencies
The schema belongs to my client.+
We can work from an anonymized/renamed schema (structure without real names) or under a tripartite NDA. On-premise isn’t offered: the factory never leaves.
Does it grow with the project?+
Yes. New modules are generated as independent vertical slices and added alongside your code, without touching what your team already built.
Same architecture across projects?+
Yes. Deterministic output means the same structure every time, so developers rotate between projects with no ramp-up.
Ready to see it on your schema?
Book a call →Your schema stays confidential. NDA on request.
Per-project, priced on your schema. You see it running before you pay, and the first payment is a down-payment, not the whole project. The same structural layer, built in-house, is months of a team.