How Asthra compares
Honest comparisons against generic LLMs, dedicated regulatory AI platforms, and manual authoring — so you can decide where Asthra fits and where it doesn't.
Every team evaluating AI for regulatory writing already has three alternatives on the table: a general-purpose LLM like ChatGPT or Claude used directly; a dedicated regulatory-AI platform; or the status quo — manual authoring by experienced regulatory writers. Below, Asthra is compared to each on the capabilities that actually drive a buy decision in regulated life sciences.
Asthra vs. generic LLMs (ChatGPT, Claude direct, etc.)
What changes when the LLM is wrapped in a regulated-writing system
| Capability | Asthra AI | Generic LLMs |
|---|---|---|
| Closed-system retrieval (no open-internet, no training-memory leakage) | Retrieval is locked to your uploaded source documents only. | General-purpose LLMs retrieve from training memory and often the open web. |
| Sentence-level citations with file, page, and exact passage | On-demand for every generated claim. | Some general chat UIs can attempt citations, but they are generated post-hoc and routinely incorrect. |
| Flags missing or contradictory source data | Gaps surface inline; no fabrication. | Generic LLMs fill gaps with plausible invention. |
| Works natively inside Microsoft Word | Word add-in with task pane, track changes, and in-document refinement. | Copy-paste workflow between a chat UI and Word. |
| Template-specific, section-by-section drafting | Onboarded per template (ICH E3, ICH E2C, EU MDR, eCTD Module 3). | Prompt-by-prompt generation; template fidelity relies on the user. |
| Immutable transaction ledger embedded in the .docx | Audit trail travels with the document. | Chat history is ephemeral and not embedded in the output. |
| No customer data used for model training | Contractual via Anthropic commercial API + Asthra terms. | Depends on the plan and settings; often requires enterprise tier. |
| SOC 2 / ISO 27001 / HIPAA / GDPR posture | SOC 2 Type 1 ready; SOC 2 Type 2, ISO 27001, GDPR, HIPAA in progress. | Varies by vendor and plan; rarely life-sciences-specific. |
Takeaway. Generic LLMs are excellent general writers. They lose in regulated environments on the three things that matter most — closed-system retrieval, honest citations, and an audit trail that travels with the document.
Asthra vs. dedicated regulatory AI platforms
Among purpose-built platforms, where Asthra is different
| Capability | Asthra AI | Other regulatory AI platforms |
|---|---|---|
| Writer-defined provenance (human sets source rules per section) | Configured during template onboarding, enforced at retrieval time. | Some platforms rely on the model to choose sources, with human review after. |
| Agentic drafting loop with human-approvable retrieval plan | Plan → Retrieve → Draft → Hand off. Writer can approve the plan before drafting runs. | Many platforms run end-to-end generation without an approval checkpoint. |
| Chat-mode refinement after the draft | Natural-language edits inside Word, using the same closed-system retrieval. | Usually a separate prompt cycle; citations may not update automatically. |
| Built on Anthropic Claude | Claude Opus / Sonnet / Haiku selected per task. | Many use GPT-family models; model choice is often opaque. |
| Support across CSR, PSUR / PBRER / DSUR, CER, and CMC Module 3 | All four are production document types today. | Most platforms specialise in one or two of these and partner for the rest. |
| Managed SaaS or customer-managed VPC deployment | AWS, Azure, or GCP VPC for customers with data-residency or validation requirements. | Not all platforms offer VPC deployment. |
Takeaway. Most purpose-built platforms got the document-type coverage right. Asthra's differentiators are the human-approvable retrieval plan, chat-mode refinement inside Word, and the .docx-embedded transaction ledger — all aimed at making the audit trail travel with the writer, not stay in a vendor's backend.
Asthra vs. manual authoring
Where writer time gets reclaimed — and where it still belongs
| Capability | Asthra AI | Manual authoring |
|---|---|---|
| Time to CSR first draft | About 30 minutes of agent time; writer review on top. | Typically 6+ weeks per study. |
| Time to PSUR first draft | About 60 minutes of agent time; writer review on top. | Weeks of synthesis across fragmented safety sources. |
| Consistency across sections and reporting periods | Session-level caching + template-level prompts keep the voice and structure stable. | Depends on writer continuity and QC rigor. |
| Sentence-level traceability without extra effort | Generated during drafting, not retrofitted. | Possible manually, but expensive and easy to drop under deadline pressure. |
| Full clinical and regulatory judgment | Asthra drafts; writers interpret, conclude, and sign off. | Qualified humans retain full judgment authority. |
| Complex statistical reanalysis | Out of scope — Asthra reports from the sources, it does not recompute. | Statisticians and programmers handle this upstream. |
Takeaway. Asthra collapses the mechanical half of authoring (retrieval, reformatting, initial narrative, cross-referencing). The interpretive half — clinical judgment, benefit-risk, sign-off — stays with the writer, which is where it belongs.
Where Asthra is not the right fit
Asthra is not a statistical analysis tool. It reports what the source documents contain and does not recompute TFL, re-derive populations, or run primary statistics. That work belongs upstream with your biostatistics team.
Asthra is not a clinical judgment system. Benefit-risk conclusions, causality assessments, and regulatory strategy remain with qualified medical and regulatory professionals. Asthra surfaces evidence; humans decide what it means.
Asthra is not a document management system. It reads from your DMS when you integrate one, but it does not replace one. Version control, access policy, and long-term archive belong in your DMS of record.
If your use case is outside regulated life-sciences writing — general business documents, marketing copy, code — Asthra is the wrong tool. Use a general-purpose LLM for those.
See Asthra next to what you're using today
Request a demo. Bring the document type you care about. We'll show you exactly where Asthra changes the picture — and where it doesn't.
Request a demoLast updated: 16 April 2026