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AI coding assistants vs. an AI software factory
By Mikko Laakkonen · Taiga co-founder and CEO
Published · Updated
A coding assistant and a delivery platform can overlap. Compare their actual context, controls, review records and operating responsibilities. Taiga’s published offer uses monthly Learn and Deliver + Operate fees; it is not priced per delivered system.
Choose tools around the work you need to complete. Code generation, policy context, implementation checks, approval and operation are different responsibilities. A product’s category does not prove that it covers all of them.
What a coding assistant does well
Coding tools can help write, explain and change code, and some offer project context, agent workflows and enterprise controls. Capabilities and pricing vary by product and configuration; inspect them directly rather than assuming every assistant has the same limits.
Where the factory continues
After code is written, someone still owns review, acceptance, deployment and operation. Check which responsibilities a tool supports, which your pipeline handles and which remain with your team.
That review work is not marginal. In Veracode's tests across more than 100 models, AI-generated code introduced a security flaw in 45% of tasks.
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.
Side by side
| Capability | AI coding assistant | AI software factory |
|---|---|---|
| Primary use | Varies: coding assistance, agent tasks and related developer workflows | Taiga: project context, initiatives, implementation and review |
| Pricing | Depends on the vendor and plan | Taiga: monthly Learn and Deliver + Operate fees |
| Policy and controls | Inspect the product’s instructions, controls and configuration | Published instructions plus the checks configured for the project |
| Review evidence | Inspect the task, repository and organization records available | Project documents, initiative/run records, checks and pull requests |
| Runtime responsibility | Depends on the product and operating agreement | Determined by the agreed hosting mode; deployment executes in the connected pipeline |
| Acceptance | Your team evaluates the result against the intended scope | Your team reviews and accepts the delivered change |
They work together
Complement, not competitor. A specification produced by a software factory can be implemented with Claude Code, OpenAI Codex, a local coding agent, or the factory's own orchestrator. The assistant accelerates the keystrokes; the factory governs the system around them. Where the assistant stops, the factory continues.
Which one do you need?
If you want individual developers to write code faster, a coding assistant is the right tool. If you have to deliver software that passes an audit, enforces policy, and stays accountable for years, that is what an AI software factory is for. What is an AI software factory?
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.
See how a requirement or a repository finding becomes a reviewed change.