ChatGPT, in-house RAG, or Asthra — what to use for regulatory writing.
Three categories of tool show up in evaluations: general-purpose chat assistants (ChatGPT, Claude direct), in-house RAG builds (your data team's bespoke pipeline), and a purpose-built agentic platform for regulatory writing. Below: 20+ capabilities side by side. Where each breaks down, where Asthra earns its keep, and where we don't claim more than the product delivers.
What matters in regulated writing.
Three things only
a purpose-built tool gets right.
Cross-section consistency is a state problem, not a prompt problem
The reason CSRs and PSURs are 60% reconciliation work is that the methods, results, and discussion are written by different people from overlapping sources. Asthra writes from a shared citation graph, so the same claim in §3, §9, and §13 stays consistent automatically — within a drafting run. A chatbot has no concept of the rest of the document. An in-house RAG build has retrieval per query — no shared state across sections.
Word renders. Asthra holds the state.
Other tools put a chat box next to a Word document and call it AI writing. Asthra runs the document from a structured backend — the Dossier State — with Word as the renderer. Walk-away drafting, mid-draft literature search, flag resolution with tracked changes, end-of-run QC, audit-ready run bundles — these only work as one product because every action mutates the same state, not a transient chat thread. State-backed authoring is the architectural property the rest of the platform sits on.
Module rules and audit-readiness are not optional
ICH E3, E2C, E2F, MDR, M4Q are document obligations, not style preferences. Asthra encodes them as validated rules per module. And every plan, retrieval, draft step, and gap flag is recorded in an append-only ledger embedded in the .docx — survives offline, survives vendor changes, survives regulatory archival. That is a regulated-writing property, not an analytics dashboard.
Try it on a real CSR.
30-day pilot. We benchmark Asthra's output against whatever you're using today — generic AI, in-house build, or manual.