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What is an AI software factory?
By Mikko Laakkonen · Taiga co-founder and CEO
Published · Updated
An AI software factory connects software intent, project context, implementation and review across a delivery workflow. Taiga supports Learn, Deliver and Operate; configured checks and human decisions have distinct roles. Evaluate the supported workflow and agreed service scope, not the category name alone.
Code generation is solved. The system around it is not.
Generating code is good enough to be the cheap part. The expensive part is everything around it: the architecture decisions, the security baseline, the compliance checks, the deployment, the monitoring, and the years of maintenance after launch.
The generated code itself still needs that system. In DryRun Security's March 2026 study, 87% of AI-agent pull requests introduced at least one vulnerability. Veracode's tests across more than 100 models found AI-generated code introduced a security flaw in 45% of tasks.
A coding assistant helps one developer type faster. It has no view of the organization's policies, no audit trail of what shipped, and no ownership of what happens after the code is written. An AI software factory owns that whole system. That system is the product.
From a policy to an implemented control
Every regulated enterprise already has the rules: privacy policy, security baseline, architecture standards, approval gates. They were written for a world where humans wrote the code and humans reviewed it. None of them live where AI generates code at machine speed.
Some policy requirements can become automated checks. Others need a contextual decision or a human review. For each requirement, identify the implemented control, its scope and the evidence from the relevant build. Do not treat the policy’s presence in a prompt as proof that the result complies.
Three loops: Learn, Deliver, Operate
The scope of delivery and operations depends on the service agreement and configuration. In Taiga, project context and published policies guide work, implemented checks produce scoped results, and people decide whether to accept a change. The connected pipeline executes deployment; hosting and runtime responsibilities follow the agreed mode.
- 01 Learn
- Work out the intended behavior in conversation. Publish the user flows and choose the interface before building, or import the system you already have.
- 02 Deliver
- Prioritize initiatives in one queue. Taiga plans and builds within your chosen controls, then follows the approved change through your deployment pipeline.
- 03 Operate
- Turn repository findings and cited policy gaps into the next change. Verify repairs with fresh scans or policy assessments and follow the running application through its configured signals.
How it differs from an AI coding assistant
An AI coding assistant, such as Cursor, GitHub Copilot, or Claude Code, is built for one developer's productivity, sold per seat, and lives in the editor. It is good at what it does, and a software factory is a complement to it, not a replacement.
The difference is scope. A coding assistant speeds up writing code. A software factory delivers a governed production system: one platform, one audit trail, one accountable vendor. See the full comparison.
Who it is for
An AI software factory is built for regulated enterprises and the public sector: finance, healthcare, defence, energy, and government, where software has to pass an audit, meet compliance requirements, and stay accountable for years. If your industry has a regulator, the governance is not optional. See how the factory is configured per industry.
Frequently asked questions
Is an AI software factory the same as an AI coding assistant?+
An AI software factory connects software intent, project context, implementation and review across a delivery workflow. Taiga supports Learn, Deliver and Operate; configured checks and human decisions have distinct roles. Evaluate the supported workflow and agreed service scope, not the category name alone.
What does “governed” mean here?+
An AI software factory connects software intent, project context, implementation and review across a delivery workflow. Taiga supports Learn, Deliver and Operate; configured checks and human decisions have distinct roles. Evaluate the supported workflow and agreed service scope, not the category name alone.
What is policy-as-code?+
Policy-as-code means translating a policy into executable rules where that is possible. Written instructions can also guide agent context and human review. Ask which controls are automated, which are advisory and which require a person; the existence of a policy document alone does not establish enforcement.
Who needs an AI software factory?+
Regulated enterprises and public-sector organizations in finance, healthcare, defence, energy, and government, where software must meet compliance requirements and pass audits.
See how Taiga runs the three loops
See how a requirement or a repository finding becomes a reviewed change.