MLR Review Software
Good MLR review software helps Medical, Legal, and Regulatory reviewers approve promotional material faster — and the right AI layer is an assist, not an approver. It flags candidate issues; your reviewers judge. When you evaluate one, demand four things you can verify on your own hardware: a signed audit trail per call, no egress, model provenance, and enumerated capacity. See it working at mlr.hyfstele.com.
In pharma, no promotional piece — a detail aid, a website, an email, a conference banner — ships without passing Medical, Legal, and Regulatory review. MLR review software is the system of record for that gauntlet: it moves each piece through the reviewers, ties every claim to its reference, tracks fair balance and required safety information, and captures who decided what and when.
An AI assist changes the speed of the first pass, not the authority of the decision. A good assist reads the piece and the annotated references and surfaces candidate issues for humans to rule on: a claim that outruns its citation, a missing or stale reference, an omitted fair-balance statement, an off-label implication. It shortens the reviewer's search. It does not sign the approval.
It flags. You decide.
Flag → Judge → Prove. There is no LLM inside the flag decision plane.
This distinction is the whole ballgame for a regulated buyer. Hyfstele's MLR assist runs the model in the Flag stage only: it proposes candidate issues with the passage and reference attached. A Medical, Legal, or Regulatory reviewer performs the Judge stage. The Prove stage anchors both to a tamper-evident record. Because the model output feeds a queue for humans rather than a decision gate, a generation can never become an approval on its own.
Treat any product that markets "automated MLR approval," "auto-clear," or "the AI signs off" as a red flag. In a GxP context, an approval is a human act with an accountable name attached to it. Software that implies otherwise is selling you regulatory risk, not throughput.
The AI part of MLR software is a model you did not train, running somewhere, touching your unreleased promotional and clinical claims. The right questions are not "is it accurate" but "can I prove what it did, and can it leak." Hyfstele makes four controls independently verifiable on your own hardware:
The weights running on your box are proven byte-identical to the public open-weight artifact. You verify the model is the model you think it is.
Dormant and latent capacity is enumerated, not hand-waved. You get an accounting of what the model can do, on the record.
No route to the internet and no name to resolve. Things go in, nothing comes out. Your unreleased claims never leave your perimeter.
Every inference call is anchored to a tamper-evident audit chain signed with post-quantum cryptography (ML-DSA). Attributable and non-repudiable.
Turn these into a vendor scorecard:
21 CFR Part 11, GxP, data residency, and model-provenance expectations all converge on one requirement: you must be able to prove, after the fact, what happened and who was responsible. A signed audit chain gives an MLR program exactly that for its AI layer — a time-ordered, attributable, non-repudiable record of every issue the assist flagged and every decision the reviewer made.
Because the chain is tamper-evident and signed with post-quantum cryptography, an entry cannot be quietly altered or backdated without breaking the signature. During an inspection you are not reconstructing the AI's behavior from logs you hope are complete; you are producing cryptographic evidence. That is the difference between "we think the model flagged this" and "here is the signed record that it did."
Hyfstele's pharmaceutical MLR promotional-review assist is running live. The demo walks the full Flag → Judge → Prove loop: the model flags candidate issues in a promotional piece, a reviewer judges them, and each step is proven against the signed audit chain.
Open the live MLR demo, or talk to us about a deployment inside your own perimeter.
Open the live demo Talk to usMLR review software supports the Medical, Legal, and Regulatory approval of promotional material in pharma. It routes claims, references, and fair-balance requirements past reviewers and keeps a record of decisions. An AI assist adds a flagging layer: it surfaces candidate issues — unsupported claims, missing references, fair-balance gaps — for the reviewers to judge. It does not approve material; the human reviewer does.
No. Hyfstele's doctrine is "It flags. You decide." The model surfaces candidate issues; the Medical, Legal, and Regulatory reviewers make every judgment. There is no LLM inside the flag decision plane, so a model output can never itself constitute an approval. Be skeptical of any tool that implies automated approval.
Every inference call is anchored to a tamper-evident audit chain signed with post-quantum cryptography (ML-DSA). That gives you an attributable, time-ordered, non-repudiable record of what the assist flagged and what the reviewer decided — the kind of records integrity Part 11 and GxP expect. The chain is verifiable independently on your own hardware.
Four diligence criteria: (1) a signed audit trail per inference call; (2) no egress — no route to the internet and no name to resolve; (3) model provenance, meaning weights proven byte-identical to the public artifact; and (4) capacity enumeration of dormant or latent behavior. Ask the vendor to demonstrate each on your own hardware rather than take their word.
Yes. A live demo of the pharmaceutical MLR promotional-review assist is at mlr.hyfstele.com. It shows the Flag, Judge, Prove workflow: the model flags candidate issues, the reviewer judges, and the decision is proven against a signed audit chain.