For commercial pharma marketing teams
From brand plan to MLR-ready content, without the rework.
FairBalance AI turns your approved brand plan and claims library into channel briefs and first drafts, then checks every draft for fair balance and off-label risk before it reaches your MLR reviewers.
Runs inside your own environment. Your data and IP stay with you.
[Brand] is indicated for adults with [condition] who have not responded to first-line therapy.
Patients see results faster than with any other treatment.
Important Safety Information follows below.
Claims Matcher: superiority claim has no match in the approved claims library.
Risk Router: sent to medical reviewer with the flagged line highlighted.
The problem
The brand-to-content pipeline is broken in three places.
01
Strategy silos
The brand plan lives in one deck. Channel teams and agencies work from their own reading of it. Messages drift before a single asset is written.
02
Slow production
Briefs take weeks to draft. Assets go back and forth for rework. Your team spends its time rewriting instead of planning.
03
MLR bottlenecks
Off-label language and unsupported claims get caught late, in review. Every rejection costs another cycle and pushes launch dates back.
How it works
One pipeline. Plan in, compliant drafts out.
Two connected stages. The first writes from your brand plan. The second reviews what was written, before any human reviewer sees it.
Stage 1
Content strategy & generation
- 1
Upload your brand plan. Brand rules, key messages and market signals are pulled out and structured.
- 2
Get a content strategy and agency brief. Channel-specific briefs your team or agency can work from right away.
- 3
Draft from what already passed. Rep, email and digital copy written from your own past approved content and tied to approved claims.
Stage 2
FairBalance review
Six review agents read every draft the way an MLR team would: fair balance, off-label risk, claim support and past rejection patterns. Issues are flagged, explained and routed to the right reviewer.
- Catches problems before review, not during it
- Every check and change is logged
- Your reviewers keep the final say
The review agents
Six specialists, one pre-MLR check.
Fair Balance Checker
Checks that risk information is given the same weight and visibility as benefit claims.
Off-Label Language Scanner
Flags wording that reaches beyond the approved indication or patient population.
Claims Matcher
Matches every claim in a draft to your approved claims library and its references.
Rejection Pattern Analyzer
Learns from your past MLR feedback so the same issue is not sent back twice.
Risk Router
Sends each flagged item to the right reviewer, medical, legal or regulatory, by risk level.
Audit Trail Logger
Records every check, flag and edit, so review history is complete and easy to show.
Compliance knowledge layer
Rules written by pharma people, not guessed by a model.
Behind the agents sits a business glossary and a set of guardrails. They define what terms like fair balance, off-label and superiority claim mean for each therapeutic area and market, including the differences between FDA and EMA rules.
This layer is built and kept current by pharma content specialists as labels and regulations change.
Your data stays yours
Installed in your environment. Nothing leaks out.
- Deployed inside your firewall, running on the LLM you choose to host.
- Your brand content is never used to train a public model.
- Full audit trail on every draft and every review.
- You keep ownership of your data and everything produced from it.
The pilot
Prove it on one brand in six weeks.
Best fit: one active commercial brand with high multi-channel asset turnover.
Weeks 1–2
Setup & ingestion
We load your brand book, SOPs and claims library, and install inside your environment.
Weeks 3–4
Asset sprints
Your team drafts real multi-channel email, detail aids and digital copy with the platform.
Weeks 5–6
MLR & ROI review
We measure cycle time and first-pass approval against your current baseline.
What the pilot is designed to test
10x
faster brief turnaround
>80%
first-pass MLR approval
<48h
asset repurposing cycle
These are the targets each pilot is benchmarked against. They are not guaranteed results. Your numbers are measured during the pilot and shared with you.
Who is behind it
Pharma expertise and engineering, in one team.
Arpita Pani
Lead Consultant, nexgAI
Pharma domain expert and author of the Content Excellence Transformation framework. Leads every client engagement, from pilot scoping to MLR review.
The nexgAI team
Compliance knowledge
Builds and maintains the compliance knowledge layer: the glossary, guardrails and market rules the review agents depend on.
SJ Innovation
Engineering partner, New York
AI-native software company that designs, builds and supports the platform, and handles installation inside your environment.
Talk to us about a pilot on one of your brands.
A 30-minute call with Arpita to look at your current content workflow and whether a six-week pilot makes sense. No commitment.
Or call us: (646) 666-9714