AI Software Factory

RalphYour AI software factory

A platform where AI agents analyze, design, build, test, document and prepare software deliveries, coordinated on a shared context and under your company's rules. Whatever reaches production is always approved by a person.

From need to delivery
  1. 1Prioritized need
  2. 2Isolated environment
  3. 3Agents execute
  4. 4Quality controls
  5. 5Pull request + evidence
  6. 6Human approval
Why Ralph

More development capacity, with full control

Why

Expand development capacity without relying only on hiring more people

And adopt AI while keeping control, traceability and security over AI-assisted work.

How

Coordinated agents on a shared context, integrated into your current workflow

Bound by the rules, permissions, controls and evidence your company defines.

What

A platform that takes a prioritized need all the way to a verifiable delivery

Code, tests, documentation, pull request and evidence, ready for human review.

What is Ralph

From individual AI use to a governed software factory

Today · Individual AI use

  • Everyone works with their own tool and their own context.
  • Decisions get lost in scattered conversations.
  • Results depend on who asks and how they ask.

With Ralph · Governed software factory

  • Specialized agents share the same system context.
  • Processes, permissions and controls defined by the company.
  • Every run leaves a log, artifacts and evidence.

Capacity

More deliveries without growing headcount at the same rate.

Control

All work is logged and auditable.

Visibility

Know what is happening, its status and what it costs.

How it works

End-to-end workflow

01

A concrete need is prioritized

A clear issue with acceptance criteria.

02

Ralph creates an isolated environment

Its own branch and workspace, without touching anything else.

03

Agents analyze and execute

They analyze, build, test and document.

04

Defined controls are applied

Quality and validation according to company rules.

05

Ralph delivers the work

Code, tests, documentation and evidence.

06

A person reviews and approves

The change goes through the usual flow.

Illustrative example

“We need the supplier portal to approve purchases by amount and record who approved them.”

Ralph analyzes the repository, implements the change in an isolated branch, runs tests and opens a pull request with the evidence for the team to review.

The last step is always human. AI does not decide what goes to production.

Ways of working

Two ways to work with Ralph

A

Structured or autonomous runs

  • Start from a prioritized need.
  • Follow a defined engineering process.
  • Apply quality and validation controls.
  • Produce deliverables, pull requests and evidence.
  • Can be paused, retried and resumed.
B

Collaborative sessions

  • Happen in conversations shared by the team.
  • Let you research, plan and debate.
  • Build documentation with history.
  • Direct the agents interactively.
  • Preserve the team's decisions and context.

Both share the same context: the team explores and decides in conversation, then executes in a structured way when the work is ready.

Engineering lifecycle

The common foundation for the whole lifecycle

RequirementsArchitectureDevelopmentTestingSecurityDocumentationDeploymentMetrics
Ralph

The problem

Today each stage usually lives in a different tool, and context is lost moving from one to the next. Ralph keeps context, rules and evidence across stages.

It integrates, it doesn't replace

It works with the tools your team already uses: repositories, projects, pull requests, CI and approved model providers.

Governance

Governance, evidence and operations

Six controls that make every AI-assisted change traceable, secure and measurable.

Versioned processes and instructions

You know which process version was used for each change.

Roles, permissions and individual identity

Admin, coordinator, developer and QA, with attribution for every change.

Approved AI models and usage limits

Only approved providers, with limits per user or project.

Run log

Decisions, errors, retries and artifacts from every run.

Isolated environments and approved tools

Each run works in its own space, with only what is allowed.

Operational visibility

Projects, statuses, blockers, capacity, usage and costs.

Bring Ralph to your engineering team

We support you through implementation, role-based training and launching your first use case.

Let's talk
WhatsApp