Query. Generate. Submit.

An Annual Product Review has predictable sections: batch summary table, deviation analysis, complaint trends, stability data, process changes. The data lives in your systems. The format is defined by regulation. Someone still spends days copying numbers into tables, writing summaries of what the numbers show, and formatting everything to spec.

Code can query data and populate tables. AI can read the data and write the summary. Together they produce a draft APR in minutes that a human reviews, edits, and approves.

How it works

A report template defines three things: what data to pull, how to structure it, and where AI should generate prose.

Data queries pull from Seal's structured records. "All batches for Product X in 2024" returns 186 records with yield, cycle time, deviations, release dates. "Deviations by root cause category" returns counts and trends. The queries are predefined in the template—no manual export, no reconciliation.

Code assembles the structure. Tables populate automatically. Charts render from the data. Cross-references link to source records. Section 3.2 of the APR gets the batch table because the template says so.

AI writes narrative sections. Given the deviation data, AI drafts: "Q3 showed a 23% increase in deviations compared to Q2, primarily driven by equipment-related issues following the Line 2 upgrade. All deviations were investigated and closed within 30-day targets. Three CAPAs were initiated addressing calibration procedures." You review, edit if needed, approve. The AI draft becomes your text, backed by data you can trace.

Report types

Quality Management Reviews pull deviation metrics, CAPA status, training compliance, supplier scores—everything leadership needs for the monthly quality meeting. The template knows the format. Data flows in. AI summarizes trends and flags concerns. Review, approve, distribute.

Annual Product Reviews compile a year of manufacturing history into FDA-required format. Batch records, complaints, stability, process changes—all queried from the same platform where the work happened. AI writes the executive summary and trend analysis. You verify and sign.

Development reports—CMC sections, process validation summaries, tech transfer packages—follow the same pattern. Define what data goes where. Let code assemble it. Let AI draft the narrative. Review and approve.

Regulatory submissions adapt the same data to different formats. eCTD Module 3 for FDA. Variation dossiers for EMA. The underlying records don't change; the template handles destination-specific formatting.

Dashboards and scheduling

Not every report needs to be a document. Dashboards show live metrics—batch status, deviation aging, equipment qualification timelines—that update as operations happen. When you need a snapshot, export creates a versioned PDF with audit trail.

Scheduled reports run automatically. Weekly production summaries. Monthly quality metrics. Daily deviation aging alerts. The system generates and distributes; people review exceptions.