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What is an AI software factory?
It runs three connected loops — Learn, Deliver, Operate — on one platform, enforcing your policies as code at build time before any code ships. Code generation is the cheap part; the governed system around it is the product.
AI can write code. Inside a regulated enterprise, it still cannot take that code to production. An AI software factory closes that gap: it takes business intent as input and produces governed, production-grade software as output, including application code, infrastructure, deployment pipelines, tests, and a full audit trail. It governs the entire delivery lifecycle, not a single developer's keystrokes.
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.
Governance encoded once, enforced before the AI writes a line
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.
A software factory encodes those rules once as machine-readable policy and enforces them at build time, before anything ships, not during a code review afterwards. Legal still owns the rule. The CISO still owns the boundary. What changes is enforcement: the gate that used to take a week becomes an automated check that runs on every build. This is policy-as-code. For security reviewers, the trust center covers the specifics, from data residency to audit retention.
Three loops: Learn, Deliver, Operate
An AI software factory runs the full lifecycle as three connected loops, and one platform owns all three.
- 01 Learn
- You define intent. The platform structures it into a specification with acceptance criteria and checks it against policy before a line of code exists.
- 02 Deliver
- Agents build, test, and release. Every output is validated against your security baseline and architecture standards as it is produced, and every decision is logged.
- 03 Operate
- The same platform deploys, monitors, and maintains what it built. When a dependency reaches end-of-life or a regulation changes, it regenerates the affected components inside the same audit trail.
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?+
No. A coding assistant helps an individual developer write code faster. A software factory governs the entire delivery lifecycle and produces a complete production system: code, infrastructure, tests, and an audit trail. The two are complementary.
What does “governed” mean here?+
The organization's own policies for privacy, security, architecture, and compliance are encoded as machine-readable rules and enforced before any code ships. Every output is traceable to the intent it came from and the policy it obeyed.
What is policy-as-code?+
Policy-as-code turns written governance, such as a privacy policy or a security baseline, into machine-readable rules the platform enforces automatically at build time, rather than relying on a human to catch violations in review.
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
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