Smarteeva Orchestra:
Your Team Builds the AI. No Code. No Data Scientists.

When every complaint sits in the same queue with the same priority, your team has to read through them one by one to determine which matters most. A critical adverse event report can sit next to a routine product inquiry for hours -or days, before someone triages it. The result: high-risk complaints don't get escalated fast enough, reporting deadlines tighten unnecessarily, and your team is constantly reactive.

The moment a complaint is captured, Smarteeva's AI runs automated decision trees to assess severity, regulatory impact, and risk level. Every complaint is classified and risk-scored in real time based on product history, complaint type, patient involvement, and historical patterns.
Critical complaints, those involving potential adverse events, serious injuries, or malfunctions, are flagged and escalated immediately to the appropriate team. Routine complaints are routed automatically based on configurable rules. Your team's attention goes where it has the most impact, not where it happens to land in the queue.
The classification isn't a black box. Smarteeva shows your team exactly how the AI scored each complaint and which factors drove the classification, so your quality professionals can review, validate, or override with full context.

High-risk complaints surfaced immediately. Routine complaints handled automatically. Your team focuses on quality decisions instead of queue management.

This is where the stakes are highest. When adverse event detection depends entirely on a human reading complaint narratives, often dozens or hundreds per week, reportable events slip through. The language in complaints is inconsistent, often vague, sometimes buried in technical detail. A patient injury might be described in a single sentence in the middle of a paragraph about a completely different issue. A product malfunction that constitutes a reportable event might be described in terms that don't match the regulatory definition.
The consequence of missing even one: an unreported adverse event, a late MDR filing, an FDA warning letter, and most importantly, a patient safety signal that didn't get the attention it deserved.

Smarteeva's natural language processing engine scans every complaint narrative line by line, every single time, to detect potential adverse events, product malfunctions, patient injuries, and safety signals. The AI doesn't rely on keyword matching. It understands context, identifies clinical concepts, and recognizes patterns in language that indicate reportable conditions, even when the complaint doesn't use standard regulatory terminology.
When a potential adverse event is detected, Smarteeva flags it immediately with supporting evidence: the specific language that triggered the flag, the type of event detected, and the regulatory framework under which it may be reportable. Your regulatory team gets a clear, evidence-backed alert, not a vague notification.
This isn't about replacing human judgment. It's about making sure no complaint narrative goes unreviewed, no matter how large the volume. AI processes every single complaint with the same level of attention - something that's impossible to sustain with manual review alone.

A safety net that never gets tired, never skips a complaint, and never overlooks a sentence. Your regulatory team evaluates flagged events with full context and evidence, not guesswork.

When an investigation is triggered, your team starts with a blank form. The investigator has to search for similar past complaints, try to remember what root causes were identified in comparable cases, figure out which assessment questions to ask, and manually build the investigation record from scratch. Investigations take weeks, not because the analysis is complex, but because the setup and data gathering consume enormous amounts of time.

 Smarteeva's machine learning engine doesn't leave your investigators starting from scratch. When an investigation begins, AI analyzes the complaint data and draws on your organization's historical complaint records to provide intelligent starting points.
The system surfaces similar past complaints and their outcomes, so investigators can see what root causes were identified in comparable cases. AI suggests investigation paths and assessment questions based on the complaint type, product category, and historical patterns. Investigation forms are auto-populated with relevant complaint data, eliminating the re-entry of information that's already in the system.
For CAPA recommendations, Smarteeva's AI identifies emerging complaint trends and patterns across products, regions, and time periods, surfacing potential systemic issues before they become audit findings. When a trend is detected, the system recommends whether a CAPA is warranted and provides the supporting data to justify the decision.

Investigations start with intelligence, not blank forms. Your team spends time analyzing root causes and making quality decisions instead of searching for data and building records.

Preparing regulatory submissions is one of the most time-consuming tasks in post-market surveillance. A single MedWatch report requires gathering data from complaint records, adverse event assessments, investigation findings, product information, and patient outcomes, then mapping that data to the specific fields required by the regulatory framework. When you're doing this for MDRs, MIRs, CVRs, and PSURs across multiple regulatory agencies - FDA, EU, PMDA, TGA, the manual burden is staggering.
And then there are PMS reports. Under EU MDR and UK MDR, companies must produce Post-Market Surveillance Reports (PMSR), Periodic Safety Update Reports (PSUR), Post-Market Performance Follow-up Evaluation Reports (PMPF-ER), and other surveillance documents that require compiling complaint data, adverse event trends, literature references, and clinical evidence across entire product families. These reports have traditionally taken regulatory teams 100+ hours to compile manually, per report.

Smarteeva auto-generates regulatory submissions by mapping your complaint, adverse event, and quality data directly to the required reporting fields. For MedWatch and MDR reports, the system produces draft submissions with pre-populated fields, narrative sections, and supporting data - ready for your regulatory team to review, refine, and submit.
Before any submission goes out, Smarteeva validates data integrity and flags missing information. Fields that don't meet regulatory requirements are highlighted. Inconsistencies between the complaint record and the submission are surfaced. Your team catches errors before submission, not after an agency query. For PMS reports like EU PSUR, UK PSUR, EU PMSR, UK PMSR, PMPF-ER, and HCSR , Smarteeva delivers its most dramatic impact. The platform pulls data automatically from your complaint records, adverse event reports, and quality data, assembles the required sections, generates statistical summaries and trend analyses, and produces complete, audit-ready documents. What used to take your regulatory team 100+ hours of manual work per report is now completed in 8 minutes.
This isn't a template that you still have to fill in. It's a fully assembled document, generated from real data, formatted to regulatory specifications, and ready for expert review.

Regulatory submissions that are generated, not assembled. Your team's time goes to ensuring submission quality and crafting accurate event descriptions - not copying data between systems.

Most medical device companies discover quality trends the hard way: during an FDA audit, during an annual management review, or after a cluster of similar complaints has already become a systemic problem. The data was there all along, but buried across complaint records, investigation files, and disconnected reports; no one could see the pattern until it was obvious.

AI continuously monitors complaint volume, severity distribution, and device-level patterns. It clusters complaints across markets and languages — translating and normalizing data so a complaint filed in Thailand about the same device issue filed in Germany is recognized as part of the same pattern. Anomaly detection flags statistical deviations from baseline complaint rates. Trend reports are generated on demand, not on a quarterly schedule.

A quality system that sees problems coming instead of documenting problems that already happened. Your team drives continuous improvement with data, not retrospection.