SOFTWARE FACTORY
Turn your backlog into shipped software
An AI software factory where autonomous agents do the work and humans control the critical decisions.
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 and skills are managed locally.
Fragmented context
Institutional knowledge is lost when engineers leave, forcing teams to rediscover solutions and repeat mistakes.
Cognitive load
Constant switching between agents, tools and tickets drains engineers' focus, leaving less time for high-value work.
Autonomy & control
Without proven safety and control, every step requires human intervention, limiting the ability to scale autonomous AI.
A NEW OPERATING MODEL
From backlog to production. One governed workflow.
Specialized AI agents plan, build, test and ship software together. Humans stay in control at critical approval points.
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.
Make the financial and operational performance of the Software 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
Compute costs
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 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, policies 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 and skills
Agents work against the codebases and skills already in place.
Policy and permissions
Define which agents can access specific systems, data, models and tools.
Your delivery pipelines
Connect the Software Factory to your existing CI/CD, testing and deployment workflows so changes move through the systems your teams already use.
Infrastructure
Run it on our cloud or bring your own.
START WITH ONE WORKFLOW
Prove the model before you scale it.
You do not need a fully agentic 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?
An AI 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 the Software Factory different from a coding agent?
A coding agent helps one developer write code faster. The 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.
Where do humans remain involved?
At every point that matters. Your engineers decide what goes into the Software Factory and set the standards it works to. For each type of change, your team chooses the level of control: agents act on their own for low-risk work such as docs and tests, a human reviews after, a human approves before the next step, or the work stays human-owned. 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. Run it on our cloud or bring your own. Either way, access to code, data, models and tools follows your policies and permissions.
How do we measure its cost and return?
Every task the Software 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.
See what the 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.
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