$1M
Total annual investment
13 mo
Kick-off to full production scale
6
Workflow stages automated
3
Phased releases, each board-signed
Why this matters
Regulated content production is expensive and slow because it has to be. Every claim needs evidence. Every asset needs sign-off. Every market needs its own version. Today that is handled through headcount, manual review cycles, and document trails nobody enjoys maintaining.
Content·AI Studio replaces the slow parts without touching the parts that must stay slow. Briefing, first-draft generation, claims mapping, asset assembly, pre-review preparation, and localisation all run through a single governed platform — so the people doing the hard thinking spend their time on the hard thinking, not on reformatting a leave-behind for the fourth market in a row.
The six stages, one governed flow
| Stage | What the platform does |
|---|
| 1 · Strategic Briefing | Builds the brief from approved strategic documents and regional/local inputs |
| 2 · Content Building Blocks | Generates and improves claims, drawn only from the approved claims library |
| 3 · Asset Assembly | Drafts the email, web or presentation asset on approved brand templates |
| 4 · Pre-MLR | Prepares AI-assisted review notes against each market's rules — a draft for the reviewer, never a decision |
| 5 · Localisation | Adapts approved content into local languages with the audit trail intact |
| 6 · Approval & Publish | Routes the finished asset into the existing review and publishing systems with status tracked end to end |
$1M
One fixed annual investment, fully allocated before work begins
No hourly overruns. No change-order surprises. The programme is scoped, priced and committed up front, with every category of spend visible to the board.
How the annual investment is allocated
$1,000,000 / year, all-in
A dedicated product team builds the platform across three phases, then operates and improves it — billed at a fixed monthly rate.
- Build & engineeringPlatform development, integrations and testing$360,000
- Run & optimisePlatform operations, model management and support$360,000
- Usage capacityAI inference, compute and storage at production volume$160,000
- ContingencyScope buffer, regulatory-change response and pilot iteration$120,000
What the investment returns
Content that today moves through review over a matter of weeks is designed to reach compliance-ready in a fraction of that time by the end of Phase 3. The platform is built, validated and handed to your team — it is yours to own and run, not a licence you rent indefinitely.
Built for governance, not just speed
Six workflow stages are automated end to end: brief intake, generative drafting, claims sourcing and annotation, asset assembly, AI-assisted MLR pre-review, and localisation packaging. Each stage produces a structured output the next stage consumes — so nothing falls into an inbox.
The platform runs on a modern, enterprise cloud stack with full audit logging at every step. Every generated asset carries a provenance record: which brief, which claims, which model version, which reviewer. If a regulator asks why something was approved, the answer is a few clicks away.
The principle that governs every feature
AI ASSISTS
HUMANS DECIDE
EVERYTHING IS TRACEABLE
The platform prepares; people decide. Every AI output is a draft for review, never a final deliverable. This is what lets a generative-AI tool live safely inside a regulated content process.
Three phases over 13 months
Each phase closes with a formal acceptance review before the next begins. The board has visibility at every gate.
| Phase | What it delivers | Window |
|---|
Phase 1
FOUNDATION | Brief intake and the generative drafting engine, with a working prototype in a pilot team's hands by week 10 | Months 1–4 |
Phase 2
CORE | Claims mapping, asset assembly, and the AI-assisted MLR pre-review module | Months 5–9 |
Phase 3
SCALE | Localisation, full audit trail, and production hardening for scale | Months 10–13 |
Nothing is published by the platform
The platform prepares; humans decide. Every output from the AI is presented as a draft for review, never as a final deliverable. The MLR pre-review module flags likely issues before an asset reaches the medical-legal-regulatory team — it supports them, it does not replace them.
Data handling and assurance
Information security throughout. Proprietary claims data is never used to train models. AI outputs are not retained beyond the working session unless the workflow explicitly requires it. The engagement operates under a formal data processing agreement from day one, on an enterprise cloud platform with recognised security and audit controls. Final assurance scope — validation standards, certifications and DPA terms — is confirmed during the opening phase.
