SOFT-HOUSE.AI · AI ENGINEERING TEAM

Your AI engineering team,
from idea to shipped.

Software House takes an idea and ships it to production. It runs requirements, design, build, security review, QA, and release through the full gated delivery process inside the GitLab + Jira, GitLab-native, Azure DevOps, or GitHub pipeline you already use. A human approves every gate. This is delivery, not autocomplete.

Start a free evaluation Book a demo

Try it on a real feature in your repo before you commit to anything.

AI ROLES

PHASES

sequential

HUMAN GATES

you decide

UPTIME

00:00:00

● live run

engineering-manager · live rungated

you ›/software-house "Add a customer feedback widget"

EM ·Preflight ✓ Phase 1 · requirements → discovery

EM ·🚦 GATE 1 · brief approved

EM ·Phase 3 · backlog created in your tracker

EM ·Phase 4 · design + task split 🚦 GATE 3

EM ·Phase 6 · parallel build → tasks complete ✓

EM ·Phase 7 · QA accept → MR !42 🚦 GATE 4

EM ·Phase 8 · staging ✓ → release v1.3.0 → production ✓

you ›approve

// the problem

Copilots autocomplete. That’s the easy 20%. We took the other 80% and finished it.

AUTOCOMPLETE · 20%DELIVERY · 80%

A single AI agent can write a function. It can’t run requirements with your stakeholder, reconcile feasibility with an architect, size the work, build it test-first, get it reviewed twice, pass QA against acceptance criteria, and cut a traceable release, all without making a mess in your repo.

So engineering leaders are stuck choosing between velocity (AI tools that bypass your process) and control (your process, but no AI leverage).

Software House refuses the trade-off.

// the solution · a team, not a bot

An orchestrated delivery team, not a lone bot

Software House runs your feature through a coordinated team of specialists and stops at human gates, the same shape as a real delivery org. An orchestration layer plans the work and dispatches each role; standing roles run on every feature, while specialist roles activate only when the work calls for them. The exact composition and methods stay under the hood.

// how it works

One command. Eight phases. Five gates where you decide.

At each 🚦 the orchestrator stops and shows you the work: the brief, the backlog, the design, the merge request, the staging results, and waits. Approve, edit, or reject. Between gates it does the work and runs your backend commands on your behalf.

Discovery

→ requirements brief

Feasibility reconcile

→ refined brief + constraints

🚦GATE 1 · approve the brief

Planning

→ backlog in your tracker

🚦GATE 2 · approve the backlog

Architecture

→ design + task split

🚦GATE 3 · approve the design

Estimation

→ estimates + risk flags

Parallel build

→ test-first, in isolation

Review + QA

→ review → QA → merge

🚦GATE 4 · approve the merge

Delivery

→ staging → verify → release

🚦GATE 5 · approve / hold

See it run on your own code

The evaluation isn’t a sandbox.

You connect your real repo (GitLab + Jira, GitLab-native, Azure DevOps, or GitHub), pick a real feature, and watch the team run it through all five gates. You approve every step. Nothing merges without you.

phases

human gates

AI roles

Start a 5-day evaluation Book a demo first, on-site support

// why it's different

Six things a single agent can’t give you.

Humans hold the wheel

Five approval gates. Nothing is written to your tracker, merged, or deployed without passing its gate. Rehearse the entire flow with --dry-run. It prints every intended action and makes none.

It ships through your pipeline

Real Epics, Stories, and Tasks. Real merge / pull requests. Real ADRs. Real tagged releases on GitLab + Jira, GitLab-native, Azure DevOps, or GitHub. Backend-agnostic by design; switch with one config key.

Senior-grade quality and security

TDD by default. A per-stack coding-standards brief enforced before anything is “done.” Every merge gets two reviews: developer cross-review plus an independent second model. A standing Security Engineer reviews every feature and threat-models the design.

Governance is a feature

ADRs, a wiki a standing Technical Writer keeps current, traceable tickets, gated releases, resumable audited state. Secrets and PII are stored as references, never raw values, and every third-party egress is disclosed at preflight.

Your people, amplified or AI end-to-end

Run it all-AI (two developers in parallel), or flip to human-developer mode (--devs 0): AI plans, designs, estimates, and reviews; your engineers write the code. It augments people instead of replacing them.

From one feature to a whole portfolio

For multi-team, multi-repo orgs, a Program Director layer ranks and sequences initiatives by value, effort, and risk into dependency-aware waves, with portfolio gates, running unattended overnight with a morning rollup. (Enterprise.)

// category

We deliver software, not just code.

Head-to-head: a coordinated ecosystem versus a code assistant or a single agent.

Dimension	Software House ecosystem	AI code assistants	Single agent
Unit of work	A delivered, gated feature: built, reviewed, and released	A code suggestion you accept inside a file	A raw code dump you integrate yourself
Number of roles	11 specialized roles, dispatched per phase	None. It autocompletes for one person	1: a single generalist agent
Pipeline phases	8 phases, discovery through delivery	None	None
Human approval gates	5 gates: nothing proceeds without your approval	None	None
Runs inside your real tracker / CI	Yes: GitHub, GitLab + Jira, GitLab-native, or Azure DevOps	No	No
Produces governed artifacts (ADRs + audit trail)	Yes: ADRs, tickets, and a resumable audit trail	No	No
Standing security review on every feature	Yes: a standing Security Engineer reviews every feature	No	No
Two-reviewer quality gate	Yes: developer cross-review plus an independent QA accept/reject	No	No
Portfolio / program orchestration	Program-office: value/effort/risk scoring + dependency DAG + wave plan	None	None
Cost model	€10,000 / project / month flat, no per-token billing	Per-seat / per-token	Per-token

An Epic / Feature / Story / Task ticket hierarchy in your tracker

Pull requests, each linked to its task ticket

An ADR added to the architecture decision index

An updated docs / wiki entry kept current by the Technical Writer

A QA verdict recorded against the feature's acceptance criteria

A CI-verified, tagged release

A resumable run-state record for a full audit trail

See it ship a feature

// the field · named, not hypothetical

The best agentic tools write code. We deliver software.

These are the strongest agentic coding tools, multi-agent frameworks, and AI app builders shipping right now. We run on the same class of model, so here is the honest version of where Software House is genuinely different, and where it isn't.

Devin Cognition

Autonomous cloud engineer

Strong at: Bounded, well-specified tasks and legacy-code migration, run unattended in its own sandboxed cloud: it plans, codes, tests, and opens a PR on its own.

Where we differ: Devin is one agent in its own cloud. Software House is eleven roles working inside your GitHub, GitLab, or Azure DevOps, with a Business Analyst and Architect resolving exactly the ambiguous requirements and novel architecture Devin is documented to struggle with, before a line of code is written.

GitHub Copilot agent mode

Issue-to-PR agent

Strong at: GitHub-native teams: assign an issue and it works in an Actions sandbox, pushes commits to a draft PR, and now reviews code agentically, with the broadest IDE reach of any tool.

Where we differ: Copilot gates once, at the pull request, and only inside GitHub. Software House gates the brief, the backlog, and the design before code exists, then the merge and the release, and runs the same way on GitLab + Jira, GitLab-native, Azure DevOps, or GitHub.

Cursor Anysphere

AI-native IDE

Strong at: A single developer moving fast: Agent Mode plus up to eight parallel background agents and subagents working across one codebase, right in the editor.

Where we differ: Cursor parallelizes coding. Software House parallelizes delivery: discovery, design, build, two independent reviews, security, QA, release, each behind a human gate and recorded in an audit trail. For inline editing, honestly, reach for Cursor; for a delivered feature, reach for us.

GitLab Duo Agent Platform

SDLC agentic flows

Strong at: GitLab-standardized orgs: native agentic flows (issue → MR, fix-CI, code review) across the lifecycle, inside the org's own context and guardrails. It even runs Claude Code and Codex as external agents.

Where we differ: Duo chains agents into automated flows inside GitLab. Software House is a role-based delivery org with an explicit human sign-off at every phase, and it is backend-agnostic, not tied to one platform. The closest in spirit; the furthest apart on per-phase human control and choice of backend.

Claude Code Anthropic

The engine, not a rival

Strong at: The most capable terminal coding agent shipping today, with a 1M-token context window. It is, by most 2026 rankings, the agent to beat for complex work.

Where we differ: This is what Software House runs on. We don't claim a better model. We orchestrate the best one into a gated, eleven-role delivery pipeline. The honest line of the whole comparison: everyone else competes on the agent; we compete on the organization built around it.

BMAD-METHOD open source

Agentic agile framework

Strong at: The closest idea to ours, shipped as free open source: 40k+ GitHub stars, 12+ specialized agent personas (PM, Architect, Dev, UX, QA) and quality gates you run yourself inside your own AI tool. MetaGPT and ChatDev explore the same multi-agent territory.

Where we differ: BMAD proves the method; Software House operationalizes it. BMAD is a prompt-and-agent kit you assemble, run, and maintain on your own, with no native wiring into your tracker or CI and no gate that actually blocks a merge or release. We are the productized version of that same role-based agile shape, running inside your GitHub, GitLab, or Azure DevOps with enforced human gates, governed artifacts (ADRs, tickets, a resumable audit trail), and a supported license.

base44 Wix

AI app builder

Strong at: Describe an app in plain language and get a deployed full-stack app (frontend, backend, database, auth) in minutes, with 2M+ users. Lovable, Bolt, and v0 play the same vibe-coding game.

Where we differ: base44 builds a brand-new app on Wix's platform: the fastest path to an MVP or internal tool. Software House does the opposite job, delivering features into the codebase and pipeline your team already owns, with review, security, QA, and governance around every change. For a greenfield prototype, reach for base44 or Lovable; for production work in a real repo, reach for us.

None of these are weak. Several are excellent at what they do. The difference is categorical: they are agents that produce code; Software House is a delivery organization that produces shipped, governed software. Need a quick edit in your editor? Use Cursor or Copilot. Need a feature specced, designed, built, security-reviewed, QA’d, and released through your own tracker, with a human approving every gate? That is the job we do.

Enterprise: never locked to one provider. We run on Anthropic models by default, and Enterprise deployments can run on Software House’s own models instead, deployed inside your environment so your code and prompts stay with you.

See it ship a feature

// backends

Runs on the pipeline you already use.

Backend-agnostic by design. Set one config key, SH_LIFECYCLE, and the team runs on yours.

GitLab + Jira

The classic split: Jira for tracking, GitLab for git, CI/CD, and MRs.

SH_LIFECYCLE = gitlab-jira

recommended

GitLab-native

The whole lifecycle inside GitLab: tracking included, no separate tracker, no Jira. Tier-adaptive from Free to Ultimate, on one token.

SH_LIFECYCLE = gitlab

Azure DevOps

Boards, Repos, Pipelines, and Wiki: the full Microsoft delivery stack.

SH_LIFECYCLE = azure-devops

GitHub

Native Issues + sub-issues, GitHub Actions, ambient gh auth: zero tokens in your config.

SH_LIFECYCLE = github

// pricing

Pay per project. No per-token surprises.

No per-run, per-ticket, or per-token billing. One predictable line item per project, per month.

most teams

Per project

Every team: pay per project, per month

€10,000/ project / mo

Start evaluation

✓Up to 3 developer seats included per project
✓Extra seats priced per seat / month
✓Month-to-month default · annual commit earns a discount
✓Any 1 backend entitlement per project
✓Full agent team · 8 phases · 5 gates
✓Standing Security Engineer + Technical Writer
✓On-call Designer & DBA · human-dev mode
✓No per-run, per-ticket, or per-token billing

Enterprise

Volume projects, on-prem & governance

Customcustom terms

Book enterprise evaluation

✓Volume-project pricing across many projects
✓Hybrid / on-prem / air-gapped delivery
✓Option to run on Software House's own models
✓Perpetual buy-out by exception (≈3–5× annualized + 20%/yr maintenance)
✓SSO guidance, custom roles, MSA + order form
✓Portfolio orchestration (program-office) included
✓Dedicated support with an SLA · roadmap influence

A 5-day evaluation with on-site support is available before your first project. Connect your real repo, pick a real feature, and run it through every gate. Enterprise adds volume-project pricing, hybrid / on-prem delivery, and a perpetual buy-out option. Charter-customer rates for the founding cohort.

// frequently asked

Questions, answered straight.

Yes. That's the point of the evaluation. Connect your real repo, pick a real feature, and run it through the full pipeline. You hold every gate; nothing merges or deploys without your approval. A 5-day evaluation with our team's on-site support is available before you pay for your first project; Enterprise runs as a scoped, supported pilot.

$/software-house-init

Ship your next feature with a full team behind it.

Connect a real repo, pick a real feature, and hold every gate. Nothing merges or deploys without your approval.

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Your AI engineering team,from idea to shipped.