Apr 2, 2026
By Ravi Ramachandran, Chief Science Officer, Peer AI
If you've spent time around regulatory writing, you can usually tell within a few pages whether a clinical study report is going to hold up. The inconsistencies show up quickly. A number that does not reconcile across sections of the same document. A safety narrative that drifts from the underlying tables. A conclusion that will not survive an agency's follow-up questions. That part of the work moves fast, because it depends on pattern recognition built over time.
Producing that draft is the harder problem. Writing and verification are still treated as separate phases, and that separation is where the real bottleneck lives.
A CSR does not begin as a document. It begins as a set of materials that were never designed to line up cleanly. A protocol, a statistical analysis plan, key messages, and then thousands of pages of tables, figures, and listings. The writer has to turn that into something coherent while making sure every statement can be traced back to source data.
We all recognize the setup. A draft on one screen, source tables on another, notes or mappings somewhere else. Sometimes it is still binders, marked up and checked manually. You write a section, verify it, and realize something does not line up. You go back, adjust, then check again. That loop is part of the process, not an exception, and it is where more time goes than most plans account for.
I have seen teams spend days chasing a single inconsistency across a CSR, not because the issue itself was complex, but because it showed up in multiple places that had to be reconciled. Faster drafting does not make that go away.
The difficulty is not just scale. It is what happens as more of it gets layered in. Each dataset introduces new dependencies, and analyses connect across multiple sections. By the time you are deep into a document, a small inconsistency can surface in several places at once. Sometimes it is as small as a decimal that does not match between a table and a summary, but it forces a full pass across multiple sections. Fixing it is rarely isolated. It propagates.
A concept from computer science maps surprisingly well onto this. The P vs NP problem asks whether problems that are easy to verify are also easy to solve. The prevailing assumption is that they are not - that finding a solution is fundamentally different from checking one. Writing and verification, it turns out, have the same asymmetry: they are structurally different kinds of work that do not reduce to each other.
Regulatory writing has the same asymmetry. If writing and verification are genuinely separate problems, making one faster does not dissolve the other. The constraint shifts, it does not disappear. Writing a document in two days instead of twenty does not help much if verification still takes weeks on the other side. The time saved at the front shows up later in review and reconciliation.
Meanwhile, the rest of drug development has compressed.. Drug discovery, trial design, even parts of clinical operations have compressed. The documentation layer has not kept pace. As timelines shorten elsewhere, more of the delay accumulates here. Documentation is now on the critical path.
At Peer AI, we approached this by treating traceability as a starting constraint, not something to add later. Documents are generated at the level of subsections, with source data mapped before anything is written. The system is deliberately constrained to use only the data relevant to that subsection, so if there is no verifiable source, the statement is not generated. It is flagged instead, which is usually where the conversation needs to happen anyway.
In a regulatory context, that constraint is not optional. Unsupported content is not something you clean up in review. It is something you avoid creating.
Verification happens in the same pass as authoring. The writer is not reconstructing traceability manually across hundreds of pages after the fact. They are working inside a structure where the link to source data is already present. It changes the work more than it looks like it should. In the language of that analogy: we did not make writing or verification faster in isolation. We restructured the problem so the separation itself disappears.
That does not eliminate judgment. There are still cases where the data supports more than one interpretation, especially around edge cases in safety or subgroup analysis. The system does not resolve that for you. It makes it visible earlier.
The effect shows up in timelines, but not always in a neat progression. CSR cycles that used to run forty days can come down to under five. Protocol drafts that previously took weeks are now measured in days. Some safety narratives that used to take several hours can be generated in minutes, while others still take longer depending on complexity.
Across document types, reductions in authoring time run between fifty and ninety percent. Because traceability is built into the authoring process, the document is reviewable as written.
Less effort goes into assembling and reconciling documents. More goes into work that requires actual judgment: regulatory strategy, agency interaction, interpreting what the data is saying and where it is ambiguous.
If we keep treating regulatory writing as a drafting problem, we will keep pushing the real work downstream. That is where timelines slip, review cycles extend and the agencies ask questions you rather would have answered before submission.
