SOFTWARE FACTORY
The AI software factory that turns your backlog into shipped software
Autonomous agents do the work. Humans control the critical decisions
3X
Faster to meet compliance standards
100%
Visibility across every agent and model
10 -
40%
Lower LLM costs
42%
Of AI projects fail without governance
Trusted by teams operating AI in production
BEYOND CODING AGENTS
Faster coding does not fix software delivery.
Coding agents accelerate individual tasks. The wider system of planning, testing, review, security and deployment still determines what reaches production
Local productivity
Individual developers move faster, while the wider delivery system remains unchanged.
Uncontrolled autonomy
More agents create more decisions, tool calls and changes that need to be governed.
Invisible economics
Token usage and compute increase, but the cost of an accepted production outcome remains unclear.
A NEW OPERATING MODEL
From approved backlog to production in one governed run.
The Software Factory turns approved backlog items into coordinated production runs. Specialized agents execute each stage while the control plane governs access, models, spend and where humans must intervene.
HUMAN-GUIDED BY DEFAULT
Autonomous where it should be. Human-controlled where it matters.
Teams define where agents can act independently, where review is required and where a human must approve the next step.
SOFTWARE ECONOMICS
Measure outcomes - not generated code.
The control plane makes the financial and operational performance of the Factory visible at every layer. Track what each run consumes and connect it to what reaches production.
WHAT A RUN CONSUMES
HRS
Human effort per run
TOK
Model and token cost
CPU
Cloud, hosted or on-prem compute cost
WHAT IT TAKES TO GET THERE
LTD
Lead time per accepted change
RWK
Rework and failed runs
STG
Cost by pipeline stage
WHAT REACHES PRODUCTION
THL
Backlog throughput
CPO
Cost per production outcome
INC
Production quality and incidents
Compare models and infrastructure strategies, identify expensive stages and decide where more autonomy creates value - and where human involvement still pays for itself.
BUILT AROUND YOUR ESTATE
Your repositories Policy and permissions
The Software Factory operates inside your existing engineering environment. Connect the backlog, repositories, delivery pipelines, infrastructure, approved models and tools your teams already use.
Your repositories
Agents work against the codebases and standards already in place.
Policy and permissions
Define which agents can access specific systems, data, models and tools.
Your delivery pipelines
Connect the Factory to your existing CI/CD, testing and deployment workflows so changes move through the systems your teams already use.
Models and tools
Use approved models, developer tools and infrastructure without locking the Factory to a single provider or stack.
START WITH ONE WORKFLOW
Prove the model before you scale it.
You do not need to automate the entire SDLC on day one.
EXPLORE THE THINKING
The move from coding agents to software factories.
FAQs
Frequently Asked Questions
What is an AI software factory?
A software factory is an AI-powered system that automates and coordinates the software development lifecycle, from planning and coding to testing and deployment. Specialized AI agents handle the work, while humans set the direction and approve critical decisions. The result: faster delivery of production-ready software, with governance, security and control built in.
How is a Software Factory different from a coding agent?
A coding agent helps one developer write code faster. A Software Factory automates the whole path from ticket to production: picking up work from the backlog, writing and testing the code, opening pull requests, and handing finished work to your team for review. It's not a smarter autocomplete. It's an extra production line running alongside your engineers.
Does the Software Factory replace software engineers?
No. It takes over the work your engineers would rather not do: backlog tickets, maintenance, migrations, bug fixes, and test coverage. That frees your team to focus on architecture, product decisions, and the hard problems only they can solve. Same team, more output.r
Where do humans remain involved?
At every point that matters. Your engineers decide what goes into the factory, set the standards it works to, and review every change before it's merged. Nothing ships without human approval, and your team stays in control of priorities, code quality, and release decisions.
How does it connect to our existing SDLC and infrastructure?
It plugs into the tools you already use, including your repositories, issue tracker, and CI/CD pipelines. Work flows through your existing review and deployment process, so there's no new workflow for your team to learn. Your code and data stay within your environment and governance rules
How do we measure its cost and return?
Every task the factory completes is tracked, so you can see exactly what was delivered and what it cost. We measure return in terms your leadership already uses: backlog reduction, cycle time, engineering hours freed up, and cost per delivered change compared to doing the same work manually. Most teams start with a scoped pilot so the numbers are proven before scaling.
A couple of things worth checking with product before publishing: the exact integrations you support (GitHub, GitLab, Jira, Azure DevOps, etc.), whether it runs in the customer's own environment or an EU-hosted one, and whether you offer a pilot. If you can name those specifically, the last two answers get a lot stronger.
See what a Software Factory would look like in your environment.
Bring us one workflow. We’ll help map the agents, controls, human gates and economics required to run it safely.