How we validate
How we generate numbers your regulatory committee can defend
We don’t publish outcome claims without showing you exactly how they were produced. Every metric is anchored to a specific pharmaceutical data source, simulation protocol, and standards namespace — so your procurement and regulatory affairs teams can evaluate the evidence, not just the headline.
01
High-fidelity pharma simulation datasets
Outcomes derive from simulations replicating real-world clinical feeds, cleanroom events, and claims distributions. No cherry-picked retrospective cohorts are used.
02
Traceable, Standards-Based Logic
PrajnaAI uses deterministic reasoning anchored to ICD-10, SNOMED CT, and FHIR standards for full traceability. All regulatory outputs strictly exclude non-deterministic inference.
03
Scalable Cross-Indication Benchmarking
Outcomes are measured across multi-site simulation environments, moving beyond limited single-program pilots. Risk-adjusted normalization ensures consistent benchmarking across diverse therapeutic areas and configurations.
04
Audit-Ready Governance
The Governance Layer ensures 100% section-level traceability by citing specific source standards and reasoning chains. This provides audit-ready outputs suitable for regulatory agencies, QA reviewers, and internal committees.
Standards compliance
Regulatory standards
enforced at every layer
We don’t publish outcome claims without showing you exactly how they were produced. Every metric is anchored to a specific pharmaceutical data source, simulation protocol, and standards namespace — so your procurement and regulatory affairs teams can evaluate the evidence, not just the headline.
SDTM / ADaM
Clinical Data Standards
CDISC-compliant data mapping from source study data to submission-ready SDTM and ADaM datasets. Required for all FDA and EMA NDA/BLA submissions.
HL7 FHIR R4
Interoperability Standard
FHIR-native resource modeling for clinical encounter data, room visibility events, and ADT-driven staffing models across manufacturing and clinical ops.
21 CFR Part 11
Electronic Records Compliance
FDA regulation governing electronic records and signatures in pharmaceutical manufacturing. PrajnaAI enforces full audit trail generation on every output.
eCTD / ICH E3
Submission Dossier Format
Electronic Common Technical Document format required for drug applications globally. ICH E3 governs Clinical Study Report structure and narrative content requirements.
SNOMED CT · MeSH
Biomedical Ontologies
SNOMED and MeSH vocabulary standards anchor all biological reasoning outputs — from pathway mapping to off-target effect prediction — to internationally recognized nomenclature.
ALCOA+ · GxP
Data Integrity Principles
ALCOA+ (Attributable, Legible, Contemporaneous, Original, Accurate) and GxP principles enforced throughout cleanroom monitoring and document validation workflows.
Validated use cases
Each capability validated through high-fidelity simulations reflecting real operating conditions.
AINalyzer
Pharmaceutical Semantic Lineage
Interprets longitudinal patient signals and biological pathways—delivering traceable, high-frequency clinical discovery for R&D teams who previously struggled with fragmented data silos.
Challenge
- EHR data optimized for billing, not biological intelligence
- Fragmented longitudinal patient signals across systems
- No geospatial access inequity quantification
Prajna AI approach
- Standards-native analytics across HL7 FHIR® and X12 claims
- Longitudinal care modeling with biological pathway mapping
- High-frequency clinical pathway discovery engine
Observed outcomes
↓
engineering assessment time — deterministic reasoning replaces manual interpretation of fragmented biological data.
↑
capacity forecast accuracy — 89% accuracy at a 30-day horizon using FHIR Encounter streams and AINalyzer models.
↑
decision repeatability — 68% of clinical pathways driving downstream procedures surfaced via X12 claims and ICD-10 stratification.
↓
expert dependency — Discovery teams operate without the typical bottlenecks of manual SME data reconciliation.
DocuDigest · Equipment Intelligence
Governed Data Conformance
Automates structural validation and compliance scoring—delivering section-level audit trails for regulatory teams who previously relied on manual, subjective clause-by-clause reviews.
Challenge
- Manual clause-by-clause validation creating submission bottlenecks
- Subjective review introducing inconsistency in audit trails
- Missing regulatory clauses identified too late in the process
Prajna AI approach
- Automated structural parsing across 837 claims, ICD-10, and SNOMED CT
- Weighted compliance scoring with deterministic validation logic
- Objective document scoring with full section-level audit traceability
Observed outcomes
↑
audit trail precision — 100% section-level traceability on all regulatory outputs using standards-constrained validation logic.
↑
compliance confidence — 80% threshold consistently met via weighted scoring engines and ICD-10/SNOMED validation.
↓
review cycle time — Automated structural parsing eliminates the friction of manual cross-referencing for eCTD and ICH E3 standards.
↓
submission risk — Early identification of missing regulatory clauses through deterministic, objective document scoring.
AINalyzer · Clinical Analytics
Encounter Records to Early Clinical Signals
Converts structured encounter data into clinically meaningful insight — identifying disease progression patterns, delayed diagnosis risks, geographic access effects, and emerging capacity pressures using existing clinical and administrative data sources.
Challenge
- EHR data optimised for billing, not intelligence
- Fragmented longitudinal patient record visibility
- Unquantified access-to-care disparities
- Retrospective utilisation-based demand planning
Prajna AI approach
- FHIR Encounter / Condition / Procedure sequence mining
- Risk-adjusted benchmarking across multi-EHR environments
- Geospatial access inequity quantification
- ADT + FHIR streams → predictive demand signals
Observed outcomes
12%
of conditions drove 68% of downstream procedures — surfaced from FHIR Encounter sequences
74%
of high-acuity readmissions linked to top 3 progression chains
31%
higher late-stage diagnoses in patients travelling >25 miles — geospatial inequity quantified
89%
forecast accuracy at 30-day capacity horizon
VisionIQ · Computer Vision
Visual Observation to Measured Patient Flow
A privacy-preserving computer vision system that quantifies patient flow, room utilisation, and congestion dynamics using existing camera infrastructure — with zero PHI exposure.
Challenge
- Unmeasured throughput latency across flow stages
- Manual room and resource status tracking
- Heuristic staffing vs encounter-driven demand
- HIPAA / PHI exposure risk with camera systems
Prajna AI approach
- FHIR-driven room & chair visibility
- HL7 v2 ADT-informed staffing optimisation
- HIPAA-compliant edge analytics — no PHI stored
- Sub-second zone state updates (<1s latency)
Observed outcomes
0
manual data entry — fully automated patient flow capture
<1s
room & zone state update latency — real-time operational visibility
10–15%
throughput uplift potential via turnover optimisation
0
PHI or biometric identity persisted — anonymous tracking only
DocuLens · Document Intelligence
Contract Interpretation to Structured Compliance Validation
Standardises contract validation across heterogeneous healthcare documents — from 837 claims artifacts to payer contracts — eliminating reviewer-dependent interpretation and missed requirement exposure.
Challenge
- Clause-by-clause manual validation overhead
- Missing requirement exposure risks
- Reviewer-dependent interpretations
- No structural visibility across document sets
Prajna AI approach
- 837 claims, HL7 messaging, ICD-10, SNOMED CT aware
- Automated structural parsing & semantic indexing
- Reference-standard deviation detection
- Weighted compliance scoring + audit-ready reports
Observed outcomes
80%
minimum compliance confidence threshold — below which findings are escalated for review
100%
section-level traceability — every finding linked to source clause
↓
manual review effort significantly reduced across contract sets
↑
audit preparedness — structured, reproducible validation output
DocuDigest · Equipment Intelligence
Equipment Specs to Deterministic Performance Validation
Interprets equipment manuals, safety constraints, and operating specifications — delivering traceable, explainable validation for biomedical and clinical engineering teams who previously depended on SME availability.
Challenge
- Fragmented device / equipment data sources
- Unvalidated safety & constraint rules
- SME-dependent operational analysis
- No explainable decision reasoning
Prajna AI approach
- HL7, FHIR, device manuals, SOPs ingested natively
- Deterministic + AI reasoning for constraint-aware analysis
- Automated OEM / accreditation compliance validation
- Cross-device benchmarking — imaging to therapeutic assets
Observed outcomes
↓
engineering assessment time — deterministic reasoning replaces manual interpretation
↑
safety validation confidence — OEM limits and accreditation thresholds enforced programmatically
↑
decision repeatability — answers traceable to source documentation, not SME memory
↓
expert dependency — engineering teams operate without specialist availability bottlenecks
Operational comparison
The shift to a predictive
pharmaceutical enterprise
Across every pharmaceutical function — from discovery to submission — legacy workflows create delays that PrajnaAI’s agentic framework systematically eliminates.
| Function | Traditional environments | With Prajna AI |
|---|---|---|
| Discovery | Fragmented omics signals, no cross-repository pathway visibility | Unified knowledge graph with 47+ hidden pathway discovery and 30% higher target confidence |
| Manufacturing | Manual cleanroom monitoring, human error driving batch loss and idle capacity | Sub-second zone state updates and 10–15% throughput uplift via automated GxP compliance |
| Regulatory | Clause-by-clause manual validation with subjective scoring and audit exposure | Automated weighted conformance scoring with 100% section-level traceability |
| Medical Writing | Manual CSR authoring consuming months with recurring narrative discrepancies | 60% faster first-draft CSR delivery with zero source table discrepancies |
| Engineering | Equipment assessment dependent on static tables and SME interpretation | Deterministic SOP-aware analysis with cross-device benchmarking and safety validation |
| R&D Planning | Retrospective capacity reporting with no early-warning signals | 89% forecast accuracy at 30-day horizon with 85% early capacity strain detection |
Agentic architecture
Five layers. Every decision is defensible.
The Prajna AI Precision Architecture operates across five critical layers to ensure every pharmaceutical output — from batch release to regulatory submission — is traceable, auditable, and standards-compliant.
Layer 01
Control
Standards-Aligned Intake
Interprets HL7 v2, FHIR, SDTM/ADaM, and 837 transactions. Normalizes multi-source pharmaceutical data into standards-native representations for downstream reasoning.
Eliminates ambiguity across EHR, LIMS, CMMS, and clinical trial management systems.
Layer 02
Intelligence
Deterministic Scientific Reasoning
Anchored to ICD-10, SNOMED CT, MeSH, and HCPCS. Enables biological pathway detection, clinical signal extraction, and progression pattern recognition across multi-indication datasets.
Detects biological patterns and clinical pathways invisible to siloed analytical systems.
47%hidden pathways surfaced
Layer 03
Execution
Operational Scientific Intelligence
Converts biological and operational signals into early-warning indicators, submission-ready narratives, and cleanroom compliance alerts. Supports real-time throughput optimization in sterile manufacturing environments.
85%early capacity strain detection
60%faster CSR first-draft
Sub-second zone state updates
Layer 04
Governance
Compliance-Safe Reasoning
Built on 21 CFR Part 11, GxP, and ALCOA+ compliant validation logic. Ensures standards-constrained reasoning with full audit trail generation and deterministic output verification.
100%section-level traceability
80%compliance confidence threshold
Audit-defensible across FDA, EMA, and internal QA workflows.
Layer 05
Presentation
Scientist-Readable Intelligence
Transforms complex pharmaceutical analytics into decision-grade outputs for scientific leadership, regulatory authors, and manufacturing operations—without requiring data science expertise.
Reduces interpretation overhead and accelerates submission timelines with structured, citation-ready outputs.
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