When Every Document Must Be Inspection-Ready
The client is a global clinical research leader supporting pharmaceutical and biotech organizations with large-scale clinical trial operations. Across thousands of studies, every patient form, site record, approval, signature, consent artifact and safety document must be processed accurately to preserve compliance, quality and inspection readiness.
Trial records arrive from sites worldwide in varied formats, languages and structures. Each document carries study identifiers, site metadata and sensitivity markers that determine classification and access. In inspection scenarios, missing signatures, misclassified documents or delayed availability surface as audit findings—not operational inconveniences.
Manual processing across languages, signatures and access controls creates delay, risk and uneven throughput.When document integrity underpins regulatory confidence, scale becomes a structural problem—not a staffing one.
Manual Processing at Scale Humans Cannot Sustain
Today, the process is manual. Human agents must:
- Read documents line by line
- Identify study and site details
- Detect language
- Extract metadata
- Recognize wet and digital signatures
- Verify signing authority
- Determine blinded versus unblinded access
- Detect duplicates
- Categorize documents correctly
- Save into the document repository as the system of record
At scale, this creates compounding operational constraints:
- Volume and workload: High intake volumes require sustained staffing simply to keep pace.
- Compliance risk: Inconsistent interpretation or incorrect handling of signatures weakens inspection readiness.
- Delayed downstream access: Clinical teams wait longer for updated documents in the centralized system.
- High dependency on individuals: Only specific authorized agents can access certain blinded documents.
- Operational bottlenecks: Documents routed through individual Outlook inboxes stall when agents are unavailable.
A systematic review of data processing in clinical research found medical record abstraction carries a pooled error rate of 6.57%, reinforcing that at scale, manual accuracy varies and errors become statistically inevitable.
Manual work does not fail loudly. It fails quietly, one misfile at a time.
When the work is compliance-bound, automation cannot be “fast.” It has to be governed.
Redesigning Intake Before Automating Anything
Persistent partnered with the client to replace manual document handling with a multi-agent automation model—designed around security constraints, regulatory expectations and production realities.
Secure Intake by Design
Early proposals relied on agents reading personal inboxes. The client rejected this outright due to unacceptable security exposure beyond trial documents.
The adopted approach introduced a compliant intake layer:
- A controlled shared intake location for each trial (shared mailboxes, SharePoint sites, shared file locations etc.)
- Direct routing of site documents to this trial-specific shared intake location
- Automation agents restricted to reading only from these controlled, isolated intake locations
Secure intake is not a feature. It is the foundation that enables automation to be deployed.
Once intake is controlled, the real challenge becomes reliable interpretation across signatures, access rules and metadata.
Multi-Agent Orchestration with Governance Built In
Persistent designed a coordinated multi-agent framework, with each agent responsible for a discrete, auditable task:
- Document reader agent: Extracts language, study ID, site ID, drug code, page count and metadata
- Duplicate detection agent: Checks for existing files in the document repository
- Blinding validation agent: Determines sensitivity and access rights
- Signature extraction agent: Identifies wet and digital signatures and matches them to a signature repository
- Authorization validator agent: Confirms the signer’s authority for the document type
- Classifier agent: Assigns document category with justification
- Repository sync agent: Uploads documents into the correct repository location
A US Government Accountability Office review reported that from fiscal years 2012–2020, 3% of FDA clinical research inspections resulted in “official action indicated” classifications, reflecting serious deficiencies requiring regulatory action.
Agentic AI is only as credible as its audit trail.
That is why orchestration and governance were designed together—not bolted on later.
Human-In-The-Loop as Transition Model
Go-live begins with agents performing extraction and categorization, followed by human quality checks. Corrections feed back into the system, improving accuracy over time.
As the model stabilizes:
- Human dependency reduces progressively
- Operations shift from full manual processing to exception handling
- A 67%+ reduction in effort becomes achievable as workloads move to a smaller oversight team
Learning loops remain governed and review-based. Models do not self-modify on live production data without human validation. This approach preserves expertise for the cases that genuinely require judgment—while removing avoidable manual effort.
From Submission to Document Repository in Near Real Time
The end-to-end flow operates as a single, governed pipeline:
- Sites submit documents to a designated shared intake location for each trial (shared mailbox, SharePoint site, shared file location etc.)
- Agents extract metadata and structure
- Signatures are identified and validated
- Blinded access rules are enforced
- Duplicates are detected
- Documents are categorized with justification
- Files are uploaded into the document repository as the system of record
- SMEs confirm outputs during early stabilization
When the pipeline is designed end-to-end, timeliness no longer depends on who is online—it depends on system throughput.
Business Impact
- Cost optimization: Progressive reduction in manual dependency, targeting 67%+ effortreduction as work shifts to exception handling
- Faster document availability: Automation begins immediately upon intake, reducing latency to system-of-record access
- Categorization accuracy: Machine-driven processing improves materially as agent decisions stabilize, driving error rates down into low single-digit percentages compared to manual baselines
- Better compliance and control: Predictable routing, auditability, and consistency across sites and study teams
- Higher confidence: The document repository becomes a cleaner, timelier single source of truth
- Scalable foundation: The same pattern can extend to other document-heavy clinical clusters
The goal is not fewer people. The goal is fewer avoidable errors.
At this point, “how” matters as much as “what”—because production-grade governance is what makes agentic systems safe.
Why Persistent
- Deep understanding of clinical document workflows, signatures, blinding and multilingual content
- Ability to design secure, compliant intake and processing models
- Experience operationalizing agentic AI with access boundaries, audit logs and validation
- Collaborative co-creation across product, compliance and delivery teams
When automation enters regulated workflows for the first time, delivery discipline becomes part of compliance.
A Practical Starting Point
This initiative demonstrates how clinical trial operations can move from manual bottlenecks to governed, agent-orchestrated workflows. With controlled intake, multi-agent verification and synchronized updates into the document repository, the client strengthens inspection readiness while accelerating access to critical trial records.
Explore a shared-intake-based pilot. Define agent boundaries and audit controls. Scale from human QC to exception handling with Persistent.
