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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.
Coding assistants and agents vary. Some use repository instructions, integrate with CI and provide enterprise controls or audit records. Those features do not by themselves establish that a delivered application is safe to release. Taiga connects project documents, policies, implementation and review in one workflow; its checks and service responsibilities have explicit limits too.
From a policy to an implemented control
Privacy policies, security baselines, architecture standards and approval rules need a defined place in the delivery workflow. Instructions can guide an agent, automated checks can test specific conditions, and reviewers can make contextual decisions. Buyers should ask which of these is implemented for each requirement, rather than treating policy context as enforcement.
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
No. Coding assistants and agents can implement changes, use repository instructions and offer enterprise controls or activity records; the capabilities depend on the product and configuration. A software factory connects that implementation work to specification, project policies, review and ongoing maintenance. Compare the actual workflow and responsibility split, rather than assuming every assistant lacks governance features.
Governed delivery means being able to trace a change to its agreed intent, identify the checks that ran and their results, and see who made the review and release decisions. In Taiga, configured repository checks and permissions constrain building and merging. Automated checks cover only what they test; competent people still review generated output and decide whether it is suitable for production. The customer’s configured pipeline executes deployment, and production operation remains an agreed responsibility. Compare assistant and factory responsibilities. The current Taiga capability reference describes the supported checks, deployment tracking and monitoring boundaries.
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.
Choosing the workflow also means choosing who will staff it. Compare building your own factory with buying the service, then inspect the published Taiga plans and included capacity. Use the dated Taiga, Factory and Cognition comparison for vendor differences, and the public evidence and its limitations to assess what each claim establishes.
Frequently asked questions
Is an AI software factory the same as an AI coding assistant?+
No. Coding assistants and agents can implement changes, use repository instructions and offer enterprise controls or activity records; the capabilities depend on the product and configuration. A software factory connects that implementation work to specification, project policies, review and ongoing maintenance. Compare the actual workflow and responsibility split, rather than assuming every assistant lacks governance features.
What does “governed” mean here?+
Governed delivery means being able to trace a change to its agreed intent, identify the checks that ran and their results, and see who made the review and release decisions. In Taiga, configured repository checks and permissions constrain building and merging. Automated checks cover only what they test; competent people still review generated output and decide whether it is suitable for production. The customer’s configured pipeline executes deployment, and production operation remains an agreed responsibility.
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.