Enterprise AI
Factory Private
Enterprise AI needs approved engineering context
September 24, 2026 - 2 minute read
Enterprise AI adoption often stalls before an agent writes a useful patch. The repository contains the implementation, while the reason for changing it lives in a ticket, an architecture document, or an old conversation. Engineers still have to connect those sources under the organization’s access rules.
Nav’s published case study describes that problem across on-premises GitLab, Confluence, Jira, and Slack. Nav reported a 60% reduction in context-switching time and twice-as-fast feature development cycles after adopting Factory. These are customer-reported outcomes, not a controlled benchmark or a forecast for another team.
CTO Gian Perrone describes the security requirement directly:
“Factory provided us with a secure, controlled way to unify our engineering context without compromising our compliance requirements.”
Enterprise AI needs the reason behind a change
Consider a modification to a financial integration. The code describes today’s behavior. A ticket explains the requested change. A design decision may explain why an apparently simpler implementation was rejected. Supplying only the repository forces the agent or its reviewer to rediscover the missing context.
Start a pilot with a real change that spans those sources. Record how long an engineer normally spends gathering the relevant material, resolving access problems, and explaining it to a reviewer. That baseline makes context retrieval visible instead of burying it inside a broad productivity number.
Factory’s MCP documentation describes how Droid connects to external tools and data sources. The integration must still run with approved credentials and permissions. Access to a server should not imply access to every document or action it exposes.
Make one acceptance criterion the quality of the resulting explanation. A useful patch identifies the requirement it satisfies, the behavior it preserves, and the evidence that validation passed. Faster code generation without that record can move work from the implementer to the reviewer.
Enterprise AI requires explicit deployment choices
Nav’s story was published before the Factory Private launch. It supports the value of approved context access, but it does not establish that Nav deployed the newly launched Private product.
For organizations making that choice now, Factory’s deployment patterns distinguish cloud-managed, hybrid, and airgapped operation. Decide where the control plane, inference, runtime, and retained evidence belong. Do not infer the answer from the location of the Git server alone.
Apply Enterprise Controls to model availability and other managed settings. Keep source permissions and merge approval requirements in the systems that already enforce them. An agent’s instruction to be careful cannot replace those controls.
Evaluate accepted changes, time spent gathering context, reviewer corrections, and access-related interruptions together. The goal is a workflow that reaches a reviewed result with less manual reconstruction of the background.
Nav’s reported improvement makes context switching a credible place to look for waste. The deployment decision still belongs to the organization’s own evidence, contracts, and operating requirements.
