Enterprise AI
Software Factory
How enterprises actually automate software development
June 24, 2026 - 3 minute read
Enterprise AI
Software Factory
June 24, 2026 - 3 minute read
Large enterprises automate software development at three layers. The pipeline layer covers CI/CD and release tooling, the developer-experience layer covers platform engineering and IDE tooling, and the work itself is where software development agents take a unit of intent through to a shipped change. This guide surveys the third layer using public, verifiable examples.
The guide draws on published case studies and product documentation. It does not speculate about private deployments at firms that have not announced one.
The phrase is overloaded. In current enterprise usage it covers at least four distinct activities.
Most enterprise programs adopt activities 1 through 3 first and only move to the fourth once their software factory has the audit and replayability properties it needs. Factory's missions reference describes one implementation of that fourth activity.
Enterprises adopt levels 1-3 first, then move to 4
Developer stays the primary author; the tool suggests completions.
Developer hands off a scoped task and reviews the result.
Agents file, review, or remediate changes inside the pipeline.
An agent orchestrates multi-day, multi-feature work across repositories.
Factory reports software factories running in production across large organizations including NVIDIA, EY, Adobe, Palo Alto Networks, Adyen, Blackstone, Wipro, and Comarch, per the Factory 2.0 announcement. Four of those deployments have published detailed, customer-authored outcomes, and the numbers below come from each customer's own case study.
Numbers from each customer’s case study
Chainguard operates a software supply-chain security business that depends on building and maintaining a large catalog of hardened open-source packages. The team ran a single, two-week Droid session across six repositories that built 80 packages. The full account is in the Chainguard case study.
Groq, the inference-hardware company, uses Factory's model-agnostic agents for day-0 launches of new models. Reported outcomes are 3x faster medium-complexity feature development and 5x faster quick-turn tasks. See the Groq case study.
Empower is an insurance and benefits provider. Its engineering organization reports a 40% reduction in incident response time, a 50% reduction in pull-request approval time, and a 50% reduction in product-to-engineering Q&A delay after adopting Factory. See the Empower case study.
Nav is a fintech serving small-business credit. The team reports a 2x increase in feature development velocity, a 60% reduction in context-switching, and 100% unification of engineering context, while preserving the compliance requirements of financial services. See the Nav case study.
The complete library is at Factory case studies.
Consulting firms and systems integrators are now named among the organizations running software factories in production. Factory lists EY and Wipro in the Factory 2.0 announcement, alongside enterprises such as NVIDIA, Adobe, and Palo Alto Networks. Detailed, firm-authored case studies for these consulting deployments are not yet public, so the requirements below are the ones that consistently drive tool selection in that context rather than a claim about any single rollout.
For engineering leaders evaluating tools in the consulting context, the relevant requirements are usually the following.
These requirements apply equally to global consulting firms, regional systems integrators, and in-house enterprise IT organizations.
A common rollout sequence, derived from published case studies and Factory's own documentation, looks like the following.
Each phase compounds on the one before it
Each step compounds because the traces, evaluations, and review history produced at one step become the inputs for the next.
Whatever platform a team chooses, the metrics that distinguish a working software factory from a set of disconnected tools are the same.
Factory provides one implementation of this measurement layer in Factory Analytics and through usage, cost, and productivity analytics.
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