Our team's documentation and commit message standards require consistent, clear prose. We've been evaluating AI writing assistants to help with this, but our primary constraint is data privacy—all processing must be on-premises or via a strictly zero-data-retention SaaS API. Grammarly is out for obvious reasons. Wordtune's privacy policy is better, but we still hesitate.
I'm researching self-hosted or privacy-first alternatives that offer similar sentence rewriting and tone adjustment capabilities. The ideal candidate would:
* Operate via a local Docker container or a dedicated, secure API.
* Not train on or retain our technical documents, RFCs, or code comments.
* Provide an API for integration into our CI/CD documentation generators or potentially even PR review bots.
So far, I've looked at:
* **LanguageTool**: Offers self-hosted (HTTP) server via Docker. It's more grammar/style focused than creative rewriting.
* **Hemingway Editor**: Purely local software, but no API and its suggestions are more structural.
* **Custom setups** with something like the `transformers` library (e.g., T5 models for paraphrasing) deployed internally. This is the most private but also the most engineering-heavy.
Has anyone implemented a similar tool in a regulated or high-privacy environment? I'm particularly interested in:
1. Any open-source projects that have built a containerized rewriting service.
2. SaaS alternatives with clear, auditable data processing agreements (DPAs) that genuinely allow zero data retention.
3. Whether integrating such a tool into a workflow (e.g., a GitHub Action that comments on PR text) is even worthwhile.
Any insights from teams that have navigated this would be valuable.
--crusader
Commit early, deploy often, but always rollback-ready.
Your research is heading in the right direction, but you're correct to see a gap. The self-hosted LanguageTool server is excellent for rule-based corrections, but its rewriting capabilities are indeed limited compared to an LLM-driven tool. The custom transformers pipeline is the only real path for true paraphrasing under your constraints.
The major caveat with deploying a model like T5 or BART for paraphrasing on-prem is the quality cliff. Out-of-the-box, these general models often produce rewrites that are technically correct but sound oddly formal or lose technical nuance, especially with code comments or RFC jargon. You'll need to budget for fine-tuning on a representative corpus of your own technical writing, which ironically requires careful data handling itself.
For a middle ground, investigate whether any of the smaller LLM-as-a-service providers (like OctoAI, Together, or even private Azure OpenAI) offer enforceable data processing agreements with true zero-retention and no training clauses. Some do, but you'll need to get the legal team to parse the B2B addendum, not just the public privacy policy. It's often more feasible than building the entire inference stack internally.
Measure twice, cut once.