Illustrative scenario

Faster NDA Submissions Without the Consulting Overhang: AI Agents for eCTD Authoring

For a Head of Regulatory Affairs at a specialty pharma company, an NDA or BLA submission represents $2M to $15M in effort and carries the full weight of the company's pipeline. A meaningful share of that cost goes to third-party RA consultants handling Module 2 authoring, eCTD compilation, and the painstaking cross-referencing of integrated summaries against SDTM datasets. An AI agent doesn't replace regulatory science judgment — it removes the volume work that drives up consulting spend and slows submission timelines.

Up and running in ~18 wkFor: Head of Regulatory Affairs, specialty pharma
Estimate your payback
~5 mo
Payback period
$7.5M
Est. savings / year
+$4.5M
Year-1 net

Rough estimate — change the numbers to match your business. We scope the real figures with you on a call.

Where RA Consulting Budgets Get Consumed

Module 2 summary authoring is time-intensive work that requires close familiarity with clinical datasets but is largely templatable once the underlying data structure is understood. eCTD sequence compilation demands precision and knowledge of FDA Gateway requirements but is not, at its core, a task that requires a senior regulatory consultant's judgment on every line. The same is true for cross-referencing ISS and ISE tables against SDTM datasets — it's exhaustive, error-prone when done manually, and billed at consulting rates. These categories compound: a delayed or non-compliant eCTD sequence can trigger a refuse-to-file action, turning a cost issue into a pipeline-delay issue.

How the Agent Integrates with Veeva RIM and Your Authoring Workflow

An AI Labor Company agent starts by mining authoring workflows from regulatory-affairs team conversations in Microsoft Teams and Veeva RIM — mapping how your team structures submission packages and where the current bottlenecks sit. It then deploys agents to draft Module 2 summaries from clinical data inputs, auto-compile eCTD sequences to spec, and cross-reference ISS/ISE tables against your SDTM datasets. The Head of Regulatory Affairs sits at the approval gate before any submission goes to FDA Gateway. The agent surfaces accurate, structured first drafts and catches cross-reference gaps; the RA team applies the scientific and strategic judgment that no agent replaces.

Recovering Consulting Spend Per NDA Cycle

The primary business case is cost recovery: teams running this approach typically see third-party RA consulting spend drop by around 35% per NDA cycle, with efficiency gains in the 40–60% range on the authoring and compilation workload specifically. At $2M–$15M per submission, that recovery is meaningful — and it compounds across a multi-asset pipeline. Beyond pure cost, faster first-draft turnaround on Module 2 content can compress the internal review cycle and reduce the risk of timeline slippage that forces an NDA into a less favorable FDA review queue. The agent is live and integrated with Veeva RIM in approximately 18 weeks.

Questions

Can the agent produce eCTD-compliant sequences that meet current FDA Gateway technical requirements?

The agent is configured to compile sequences against current eCTD specifications and Gateway technical standards. Your RA team reviews all outputs before any submission, catching edge cases that require judgment — the agent handles the structural compilation work.

How does the agent handle SDTM cross-referencing for ISS/ISE tables?

The agent ingests your SDTM datasets and maps them against the ISS/ISE table structures, flagging any cross-reference gaps or inconsistencies for your team to resolve. It's designed to surface discrepancies rather than paper over them.

Does this replace our external RA consultants entirely, or supplement them?

In practice it supplements and reduces reliance, rather than replacing wholesale. Volume authoring and compilation work migrates to the agent; consultants remain engaged for complex scientific judgment, strategy, and FDA interactions where their expertise is genuinely necessary.

Related use cases

Illustrative scenario for healthcare, pharma & life sciences. Figures are example ranges, not guarantees — we scope real numbers with you on a call.

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