Most brand teams don't lose launch weeks because the Medical-Legal-Regulatory (MLR) committee is slow. They lose them because the same asset goes round three or four times for issues that someone could have caught before it was ever submitted. Using AI for MLR review does not mean handing compliance decisions to a model. It means adding a disciplined pre-check step, grounded only in your approved references, so that reviewers receive cleaner packages and spend their time on real judgement calls.
This guide is for brand managers and production leads at pharma companies and agencies who move promotional materials and Veeva CLM / IREP eDetailers through MLR. It covers what an AI pre-check can safely do, what must stay with human reviewers, and how to pilot one without creating new compliance risk.
Why MLR review is the bottleneck, and where most rejection rounds come from
If you manage promotional content, you know the pattern. The asset goes in, comes back with comments, gets revised, goes back in, and the launch date quietly moves. When you look at the comments afterwards, many of them are not deep scientific or legal debates. They are things like:
- Unreferenced claims – a benefit statement on slide 6 with no supporting citation.
- Wording drift – copy that says "significantly reduces" when the approved reference supports a narrower statement, or a qualifier that was dropped during design.
- Outdated references – a citation to a superseded PI/SmPC version or an older study.
- Missing or misplaced fair balance and ISI – safety information absent from a pop-up, tab or leave-behind where your SOPs require it.
- Inconsistencies across assets – the eDetailer, the emailer and the print leave-behind state the same claim three slightly different ways, or a localised version no longer matches the master.
These are largely mechanical checks. A careful, well-briefed reader with the claims matrix and reference library open could catch most of them before submission. The problem is that this careful reader is rarely available at the end of a production sprint, and the checks are tedious across dozens of slides, tabs and markets. That is exactly the kind of structured, repetitive verification where AI fits.
The core distinction matters: AI does not replace the MLR committee. It sits upstream, as a pre-submission pre-check that hands reviewers a better-annotated package. The committee still reviews, interprets and approves. For the human side of the process – how to structure submissions and reduce rounds – see our guide to the eDetailer MLR review process.
What an AI pre-check can do: claim extraction and reference matching
A useful pre-check is not a chatbot you paste copy into. It is a defined pipeline with clear steps and a predictable output.
Step 1: Extract every claim
The system pulls claims from every place they live: body copy, slide text, chart titles and labels, footnotes, and – for eDetailers – on-screen HTML content including pop-ups, tabs, swipe-in panels and hidden states. Missing content inside interactive elements is a common reason claims slip through manual checks, so extraction needs to cover the full built presentation, not just the storyboard.
Step 2: Map each claim to the approved reference library
Each extracted claim is matched against your closed, approved source set: the PI/SmPC, the approved claims matrix and the published studies on file. Claims with no supporting reference are flagged immediately.
Step 3: Compare wording against the reference
This is where most avoidable comments come from. The pre-check compares the claim as written with the matched reference passage and flags:
- overstatement or stronger language than the source supports
- missing qualifiers ("in adults", "as add-on therapy", "at week 26")
- mismatched numbers, populations or endpoints
- comparative claims without a matching comparative source
Step 4: Check citation hygiene
Footnote numbering in sequence, references cited in the footnotes but not used in the asset (and vice versa), and reference versions that don't match the current approved library.
The output: an annotated report per asset
For each asset, reviewers get a report listing every claim, the matched reference passage, and a flag status – supported, needs attention, or no reference found – with a confidence indication. The point is speed of verification: a reviewer should be able to confirm or dismiss each flag in seconds because the source passage is right there.
Crucially, the pre-check works only against your approved sources, not the open web. That closed-source design is what makes the output auditable. Every flag traces back to a specific document and version your organisation has already approved.
Other checks worth automating: fair balance, consistency and localisation drift
Claim matching is the core, but several other mechanical checks are good candidates.
Fair balance and ISI presence. Is safety information present, and present where your SOPs require it – on the relevant slides, in pop-ups that carry efficacy claims, in leave-behinds and emailers? The check doesn't judge whether the balance is adequate; it confirms the required elements exist and are positioned according to your rules.
Cross-asset consistency. The same claim should be worded the same way across the eDetailer, emailers and print, and across slides within one CLM presentation. AI is well suited to spotting near-duplicates that have quietly diverged.
Localisation drift. Translated or market-adapted versions can change the meaning of a claim or drop a qualifier. A pre-check can compare the localised claim against the master and the local reference set and flag divergence for a native-language reviewer. Our guide to eDetailer localisation for Veeva CLM multi-market rollouts covers the production side of this.
Brand and style rules. Trademark symbols, product name formatting, mandatory statements, job codes and date stamps – rules that are easy to write down and easy to miss.
What stays with people. Be explicit about the limits. Judgement on context, implied claims, visual emphasis (a chart that is technically accurate but misleading in scale), overall impression, and regulatory interpretation for a given market all remain with your medical, legal and regulatory reviewers. A pre-check that pretends otherwise creates risk rather than reducing it.
Guardrails that make AI pre-checks safe in a regulated workflow
The guardrails are not optional extras. They are what separates a useful tool from a liability.
- Human approval as a hard gate. AI annotates and flags; people decide. Nothing is auto-approved, auto-corrected in the final asset, or auto-submitted to Veeva.
- Grounding only in approved references. Every flag cites the exact source passage and document version, so reviewers verify rather than trust.
- Audit trail. Log the inputs, the reference library version, the model outputs and each reviewer's decision on each flag. If someone asks why a claim was accepted, the record exists.
- Confidentiality. Unreleased claims, pipeline data and brand strategy need controlled handling: NDAs, clear data-processing terms with any vendor, and clarity on where data is processed and whether it is retained. Sensitive names and identifiers can be blocked in code rather than relying on instructions alone.
- Cost and scope controls. Per-run budgets and narrow, well-defined tasks rather than open-ended chat. A pre-check that does five specific things reliably is worth more than a general assistant.
- Evaluate before trusting it. Build a test set from past MLR comments and known-bad claims, run the pre-check against it, and measure what it catches and what it misses. Repeat whenever the model, prompts or reference library change.
How we approach this at Ranbanka: the same pattern, proven on our own pipelines
To be clear: we have not delivered an MLR pre-check tool for a pharma client. What we bring is two sets of directly transferable experience – building claim-checking AI pipelines with human approval gates, and building the eDetailers that go through MLR.
Claim-against-approved-source checking. Our AI Content Pipeline is a LangGraph pipeline that drafts SEO articles, fact-checks every claim against our own portfolio data, runs SEO checks and waits for human approval before anything is published. It has published 29 blog articles, scored 12/12 on its fact-check evaluation, and every post has human approval. That is the same pattern an MLR pre-check needs: extract claims, verify them against a closed approved source, and route everything through a person.
Confidentiality enforced in code. Our AI Case-Study Writer turns a short project brief into a full case study, fact-checks every claim against the brief and our site data, blocks confidential client names in code, and publishes only after human approval. The name-leak guard is the kind of hard control that matters when unreleased claims and brand data are involved.
Cost guardrails. Our AI Sales Assistant runs with a hard $2/day spend cap, and our AI Lead Triage – which rates every enquiry and drafts a reply for the team but never emails the visitor – runs under a daily budget cap. Predictable cost and narrow scope are designed in, not added later.
Knowing the assets. Through the agency Lebeyon, we built interactive eDetailers for Eli Lilly's Humalog brand (Veeva CLM, IREP and Salesforce integration, touch-optimised), for Eli Lilly's Trulicity in India (Veeva CLM and Salesforce), and a compliant eDetailer for Novartis China integrated with Vmobile, Veeva CRM and IREP. We didn't perform MLR review on these projects, but we understand how claims, references, pop-ups and ISI are actually structured inside CLM presentations – which is what a pre-check has to parse. If you're weighing external support for the build side, our guide to Veeva CLM development outsourcing may help.
As Shantanu Karmakar, Director - Business Planning & Client Services at Lebeyon Marketing Communications, put it: "Their expertise in frontend development and eDetailer solutions streamlined our document automation processes, making them faster and more reliable. Their team's attention to detail and commitment to delivering quality work on time truly set them apart."
You can see more of our AI work on our AI solutions page and the full range of what we do on services.
Piloting an AI MLR pre-check: a practical starting plan
Start small and prove value before expanding.
- Pick one brand and one asset type. A single CLM presentation is a good unit. Assemble its approved claims matrix and reference library, with versions clearly marked.
- Run in shadow mode. Let the pre-check run alongside your normal process without changing it. Compare its flags with the comments MLR actually raises.
- Measure what matters to your team. How many avoidable comments would have been caught before submission? How long do reviewers spend verifying each flag? How many false positives are there, and are they tolerable?
- Involve MLR reviewers from day one. Medical, legal and regulatory colleagues should help define which checks run and what the report looks like. A report they didn't shape is a report they won't trust.
- Decide build versus integrate. A standalone pre-check report may be enough to start. Later, flags can surface inside your existing production workflow, before the asset is uploaded to Veeva.
If shadow mode shows the pre-check consistently catches the mechanical issues that drive repeat rounds – with false positives your reviewers can live with – you have a case to expand to more brands, asset types and markets.
Talk to us about a pre-check pilot
If repeat MLR rounds are slowing your launches, we can help you scope a focused, human-gated AI pre-check around your own claims matrix and reference library. We offer a free initial consultation, work under NDA, and respond within 24 hours. Explore our AI solutions, then book a free consultation to discuss your assets and workflow.