Methodology

Built for Precision, Not Creativity

A four-phase methodology engineered for legal and medical accuracy — from data sanitisation through continuous production monitoring.

Foundation of Accuracy

4–6 weeks per therapeutic area

Data Preparation & Sanitisation

Every AI model is only as good as its training data. Our data preparation pipeline ensures that regulatory documents are cleaned, structured, and annotated with the precision required for legal and medical applications.

Source Data Ingestion

Automated collection and cataloguing of regulatory documents from authorised data partners.

Ingestion of 18M+ documents from CDSCO archives, published clinical trial results, and partner pharmaceutical companies
Document type classification: Clinical Study Reports, SAE narratives, IND applications, NDA submissions, PSUR/PBRER reports
Optical Character Recognition (OCR) pipeline for scanned documents with 99.2% character-level accuracy
Metadata extraction: trial phase, therapeutic area, sponsor, investigator sites, submission dates, regulatory outcomes

Data Sanitisation & Anonymisation

Multi-layer anonymisation ensures zero patient data leakage into training pipelines.

Three-pass anonymisation: Rule-based pattern matching → NER-based entity detection → Manual expert review for edge cases
K-anonymity (k≥5) and l-diversity enforcement across all patient demographic combinations in training data
Differential privacy noise injection (ε=1.0) for aggregate statistical data used in model training
Cryptographic hash-based linking allows cross-document consistency without exposing original identifiers

Text Tokenisation & Normalisation

Domain-specific tokenisation preserves regulatory terminology and medical nomenclature.

Custom BPE tokeniser trained on regulatory vocabulary — handles INN drug names, ICD-10 codes, and CDSCO-specific terminology
Section boundary detection for structured documents: identifies study design, results, safety, and conclusions segments
Abbreviation expansion database of 12,400+ medical and regulatory acronyms with context-aware disambiguation
Unicode normalisation for multilingual documents — standardises Devanagari, Tamil, and other Indian script variants

Annotation & Quality Control

Expert-annotated datasets with multi-reviewer consensus for training label quality.

Team of 24 domain experts: regulatory affairs specialists, clinical pharmacologists, and medical writers
Inter-annotator agreement (Cohen's κ) maintained above 0.85 for entity labels and 0.80 for relation extraction
Three-tier review: Primary annotation → Peer review → Senior expert adjudication for disagreements
Annotation guidelines versioned and updated quarterly based on CDSCO regulatory changes and model performance analysis

Domain Adaptation

6–8 weeks per model variant

Supervised Fine-Tuning

Our foundation models undergo rigorous supervised fine-tuning using curated regulatory datasets, transforming general-purpose language capabilities into domain-specific regulatory intelligence.

Foundation Model Selection

Strategic selection of base architectures optimised for factual accuracy over creative generation.

Evaluation of 12+ base model architectures across dimensions: factual grounding, instruction following, multilingual capability, and inference latency
Selected architecture: Modified decoder-only transformer with 7B parameters, optimised for structured document understanding
Quantisation-aware training (QAT) ensures deployment efficiency without accuracy degradation — INT8 inference at 97.3% of FP16 accuracy
Domain-specific vocabulary extension adds 34,000 regulatory and medical tokens to the base tokeniser

Task-Specific Fine-Tuning

Separate fine-tuning tracks for each core module ensure specialised performance.

Anonymisation model: Fine-tuned on 2.4M annotated clinical documents with entity-level BIO tagging across 47 PII/PHI categories
Summarisation model: Trained on 180,000 expert-written summary pairs (source document → regulatory summary) with ROUGE-L optimisation
Completeness model: Supervised on 95,000 annotated submission forms with field-level completeness labels and severity classifications
Multi-task learning for shared representations: entity recognition, relation extraction, and document classification share lower transformer layers

Hyperparameter Optimisation

Systematic search for optimal training configurations using Bayesian optimisation.

Learning rate scheduling: Cosine annealing with warm restarts, peak LR selected via logarithmic sweep across 1e-6 to 5e-4
Batch size optimisation: Gradient accumulation across 4–16 micro-batches, effective batch sizes of 128–512 depending on task
Regularisation tuning: Dropout (0.1–0.3), weight decay (0.01–0.1), and label smoothing (0.05–0.15) searched independently per task
Early stopping with patience of 5 epochs on held-out validation set, model checkpoint selection based on task-specific primary metric

Validation & Benchmarking

Rigorous evaluation against domain-specific benchmarks and regulatory expert assessments.

Held-out test sets stratified by therapeutic area (oncology, cardiology, endocrinology, CNS, infectious disease) and document type
Blind evaluation by 8 regulatory affairs professionals scoring factual accuracy, completeness, and regulatory compliance on 1–5 scale
Comparison against three commercial regulatory AI tools — ReguAI achieves 12–18% higher accuracy on CDSCO-specific test cases
Failure mode analysis: systematic review of every error case to identify patterns and guide targeted data augmentation

Human-Aligned Accuracy

3–4 weeks per iteration cycle

RLHF Optimisation

Reinforcement Learning with Human Feedback (RLHF) ensures our models prioritise factual accuracy and regulatory correctness over fluency, directly aligning AI behaviour with regulatory expert preferences.

Expert Preference Collection

Regulatory professionals provide pairwise preference judgments to train the reward model.

Panel of 16 regulatory affairs experts with average 12+ years experience in CDSCO submissions across 6 therapeutic areas
Pairwise comparison protocol: experts rank two model outputs for the same input on accuracy, completeness, and regulatory appropriateness
Minimum 50,000 preference pairs collected per training iteration, with 15% overlap for inter-rater reliability measurement
Preference data stratified across difficulty levels: routine submissions (40%), complex multi-site trials (35%), edge cases (25%)

Reward Model Training

A separate model learns to predict human preferences, serving as the optimisation signal.

Bradley-Terry reward model architecture with 1.3B parameters, trained on accumulated preference data from all iteration cycles
Custom reward shaping penalises hallucination (fabricated regulatory citations), unsupported claims, and incorrect severity classifications
Calibration: reward model predictions validated against held-out expert judgments with Kendall's τ ≥ 0.78
Reward decomposition: separate reward heads for factual accuracy (40% weight), regulatory compliance (30%), completeness (20%), and clarity (10%)

PPO Policy Optimisation

Proximal Policy Optimisation fine-tunes the model to maximise the learned reward function.

PPO with KL-penalty (β=0.02) prevents excessive deviation from the supervised fine-tuned checkpoint
Conservative optimisation: we prioritise not making things worse over aggressive improvement — asymmetric loss penalises accuracy regressions 3× vs. improvements
Gradient clipping (max norm 1.0) and value function clipping (ε=0.2) ensure training stability across 500+ optimisation steps
A/B testing each RLHF iteration against the previous best model on 2,000 held-out regulatory queries before deployment promotion

Safety & Alignment Verification

Comprehensive safety testing ensures RLHF doesn't introduce unintended behaviours.

Red team evaluation: internal team of 6 adversarial testers attempt to elicit incorrect regulatory advice, hallucinated citations, or policy violations
Regression testing suite of 5,000 golden examples — RLHF model must match or exceed supervised baseline on 100% of critical safety cases
Hallucination detection: automated fact-checking pipeline verifies every regulatory citation, drug name, and dosage reference against authoritative databases
Bias audit: systematic evaluation across demographic subgroups, therapeutic areas, and geographic regions to ensure equitable performance

Production Excellence

Ongoing — 24/7 monitoring

Continuous Monitoring & Enhancement

Our dMRV (digital Monitoring, Reporting, and Verification) architecture ensures that deployed models maintain accuracy as regulatory requirements evolve, with automated alerting and human oversight loops.

Real-Time Performance Monitoring

Comprehensive observability across all deployed model endpoints.

Latency tracking: P50, P95, P99 response times monitored with automatic scaling triggers at P95 > 3 seconds
Accuracy drift detection: statistical process control (SPC) charts track key metrics with Western Electric rules for early warning
Usage analytics: document type distribution, peak usage patterns, and per-client SLA compliance dashboards updated every 5 minutes
Error categorisation: automatic classification of model failures into taxonomy of 28 error types for targeted remediation

Regulatory Change Detection

Automated monitoring of CDSCO regulatory updates triggers model retraining pipelines.

Web scraping pipeline monitors CDSCO, MoHFW, and DCGI websites for new circulars, notifications, and template updates every 6 hours
Gazette of India monitoring: automatic detection of new rules, amendments, and notifications relevant to drug regulation
Impact assessment: each detected change is classified by affected modules, estimated retraining effort, and deployment priority
Fast-track update SLA: critical regulatory changes (new mandatory fields, changed reporting timelines) reflected in production within 48 hours

Feedback Loop Integration

User feedback from regulatory professionals continuously improves model accuracy.

In-app feedback mechanism: users can flag incorrect outputs with severity rating and optional correction text
Weekly feedback triage by domain experts — confirmed errors added to regression test suite and prioritised for next training cycle
Quarterly model refresh incorporating accumulated feedback, new regulatory data, and architecture improvements
Client-specific model fine-tuning available for enterprise accounts with proprietary submission templates and internal guidelines

Audit & Compliance Reporting

Complete audit trail for regulatory inspections and internal governance requirements.

Model versioning with full lineage: every production model traced back to training data, hyperparameters, and validation results
Decision audit logs: every AI output stored with input hash, model version, confidence score, and any human override actions
SOC 2 Type II compliant infrastructure with annual third-party audits by Big Four accounting firm
Quarterly model governance reports for client compliance teams — includes accuracy metrics, incident reports, and improvement roadmap

Timeline

Typical Implementation Timeline

From initial engagement to production deployment in approximately 21 weeks.

Weeks 1–2

Requirements gathering, data audit, and infrastructure provisioning

Weeks 3–6

Data ingestion, sanitisation, and expert annotation campaigns

Weeks 7–14

Supervised fine-tuning with iterative validation and benchmarking

Weeks 15–18

RLHF preference collection, reward modelling, and policy optimisation

Weeks 19–20

Integration testing with SUGAM/MD Online portals and UAT

Week 21+

Production deployment, monitoring setup, and continuous improvement

Ready to Begin?

Our team will walk you through the implementation process tailored to your regulatory needs.