Core Technologies

AI Architectures for Compliance

Advanced NLP, NER, and domain-adapted LLM architectures optimised for legal and medical precision — built on proprietary scientific datasets.

Natural Language Processing (NLP)

Domain-adapted NLP pipelines engineered for the linguistic complexity of medical and legal regulatory text.

Architecture Overview

  • Modified transformer architecture with 7B parameters, pre-trained on 1.2T tokens of scientific and regulatory literature
  • Hierarchical attention mechanism handles documents up to 32,768 tokens — critical for long-form Clinical Study Reports
  • Sparse mixture-of-experts (MoE) routing with 8 expert modules specialised by document type and regulatory domain
  • Flash Attention 2.0 integration reduces memory footprint by 3.2× and inference latency by 2.1× vs. standard attention

Domain-Specific Capabilities

  • Regulatory section classification: 98.4% accuracy across 34 standard CSR section types (ICH-E3 format)
  • Cross-lingual understanding: processes English, Hindi, and regional language clinical documents with shared semantic space
  • Coreference resolution for patient references across multi-page adverse event narratives with 94.7% chain accuracy
  • Temporal reasoning for event timelines: correctly sequences drug exposure, symptom onset, and intervention in 96.1% of SAE cases

Performance Benchmarks

  • CDSCO Regulatory Understanding Benchmark (CRU-B): 89.3% accuracy vs. 71.2% for GPT-4 and 68.8% for Claude 3.5 (zero-shot)
  • Medical Text Comprehension (MTC-India): 92.1% F1 on Indian clinical document understanding tasks
  • Inference throughput: 850 tokens/second on single A100 GPU, 3,400 tokens/second with 4-GPU tensor parallelism
  • Batch processing: 10,000 SAE narratives summarised in under 4 hours on standard deployment configuration

Named Entity Recognition (NER)

High-precision entity detection system identifying 47+ PII/PHI entity types with 99.4% accuracy on clinical trial data.

Model Architecture

  • Hybrid NER architecture combining CRF-BiLSTM for sequence labelling with transformer-based contextual embeddings
  • Custom BioBERT variant fine-tuned on 2.4M Indian clinical documents with entity-level BIO tagging
  • Gazetteer integration: 890,000+ entries covering Indian drug names (DPCO list), hospital names, investigator databases, and geographic locations
  • Active learning pipeline identifies low-confidence predictions and routes them for expert annotation — improves recall by 2.3% per cycle

Entity Categories

  • Patient Identifiers (12 types): Aadhaar, PAN, passport, voter ID, driving license, UHID, ABHA ID, phone, email, address, date of birth, biometric references
  • Clinical Entities (15 types): diagnosis codes, drug names, dosages, lab values, procedure codes, vital signs, genetic markers, allergy information
  • Administrative Entities (10 types): investigator names, site addresses, IRB/ethics committee references, sponsor contacts, CRO details
  • Temporal Entities (10 types): admission dates, discharge dates, follow-up schedules, drug exposure periods, symptom onset timestamps

Detection Performance

  • Overall: 99.4% precision, 98.9% recall, 99.1% F1-score across all 47 entity types on held-out CDSCO test set
  • Critical entities (Aadhaar, health records): 99.8% recall — virtually zero false negatives on most sensitive data types
  • Cross-document entity linking: 97.2% accuracy in matching the same patient/investigator across related submissions
  • Processing speed: 2,400 entities/second on CPU, 12,000 entities/second on GPU — handles real-time streaming use cases

Generative AI (LLM)

Domain-adapted Large Language Model architecture optimised for regulatory summarisation, not creative generation — every output is grounded in source evidence.

Architecture Design

  • Retrieval-Augmented Generation (RAG) architecture with vector database storing 4.2M regulatory document chunks
  • Citation-grounded generation: every claim in model output includes traceable reference to source document section and paragraph
  • Constrained decoding with regulatory grammar: outputs conform to CDSCO-mandated formats, terminology, and section structures
  • Factuality-first beam search: modified beam scoring penalises tokens that decrease factual grounding score by 2× vs. standard beam search

Generation Capabilities

  • Regulatory summary generation: produces 150–300 word summaries from 10–50 page source documents in CDSCO-approved format
  • Template completion: auto-fills 340+ regulatory form fields based on source document analysis with field-level confidence scores
  • Comparative analysis: generates side-by-side comparison reports for drug-vs-drug, study-vs-study, and version-vs-version submissions
  • Regulatory query answering: natural language interface for querying CDSCO guidelines, precedents, and submission requirements

Quality Metrics

  • Factual accuracy: 96.2% of generated claims independently verified against source documents by expert reviewers
  • Hallucination rate: 0.8% — among the lowest in the regulatory AI space (industry average: 4–8% for domain-specific LLMs)
  • ROUGE-L: 0.72 against expert-written regulatory summaries (human inter-annotator agreement: 0.78)
  • Regulatory compliance score: 94.8% of generated outputs pass automated CDSCO template validation without manual correction

RLHF Training Pipeline

Reinforcement Learning with Human Feedback aligns model behaviour with regulatory expert preferences, prioritising accuracy over fluency.

Training Architecture

  • Three-stage RLHF pipeline: Supervised Fine-Tuning (SFT) → Reward Model Training → Proximal Policy Optimisation (PPO)
  • Bradley-Terry reward model with 1.3B parameters trained on 50,000+ expert preference pairs per iteration
  • Custom reward decomposition: factual accuracy (40%), regulatory compliance (30%), completeness (20%), clarity (10%)
  • KL-constrained PPO (β=0.02) prevents catastrophic forgetting of domain knowledge during reward optimisation

Alignment Features

  • Expert panel: 16 regulatory affairs professionals with average 12+ years CDSCO experience provide preference judgments
  • Preference stratification: routine submissions (40%), complex multi-site trials (35%), edge cases (25%) ensure balanced alignment
  • Iterative refinement: each RLHF cycle improves factual accuracy by 2–4% while maintaining or improving other quality dimensions
  • Safety alignment: red team testing with 6 adversarial testers ensures no harmful or misleading regulatory advice generation

Impact Metrics

  • Post-RLHF factual accuracy improvement: +8.3% over supervised fine-tuning baseline
  • Expert preference win rate: 78% of outputs preferred over SFT-only model by regulatory professionals
  • Hallucination reduction: 62% fewer hallucinated regulatory citations post-RLHF alignment
  • Regression rate: < 0.5% of previously correct outputs degraded — conservative optimisation preserves gains

Infrastructure

Enterprise-Grade Deployment

Production infrastructure designed for regulatory-grade reliability, security, and performance.

Compute Infrastructure

  • Primary: 8× NVIDIA A100 80GB GPU cluster for model training and fine-tuning
  • Inference: 4× NVIDIA L4 GPUs per region for low-latency production serving
  • Auto-scaling: 2–16 GPU instances based on request queue depth with < 60s scale-up
  • Multi-region deployment: Mumbai (primary), Chennai (DR), with < 50ms latency for Indian clients

Data Infrastructure

  • Vector database: Qdrant cluster with 4.2M document embeddings for RAG retrieval (< 15ms P95 latency)
  • Document store: PostgreSQL with pgvector extension for metadata-enriched document search
  • Object storage: S3-compatible storage for raw documents, model checkpoints, and audit logs (AES-256 encrypted)
  • Data pipeline: Apache Kafka-based streaming for real-time document ingestion and processing (12,000 events/second throughput)

Security Architecture

  • Zero-trust network architecture with mutual TLS between all microservices
  • Data encryption: AES-256 at rest, TLS 1.3 in transit, HSM-backed key management for anonymisation keys
  • SOC 2 Type II certified infrastructure with annual third-party audit by Big Four firm
  • VAPT (Vulnerability Assessment & Penetration Testing) conducted quarterly by CERT-In empanelled auditor

Observability Stack

  • Distributed tracing: OpenTelemetry-based tracing across all microservices with < 1% sampling overhead
  • Metrics: Prometheus + Grafana dashboards with 250+ custom metrics for model performance and system health
  • Alerting: PagerDuty integration with escalation policies — P1 incidents acknowledged within 5 minutes, 24/7
  • Log aggregation: Elasticsearch cluster with 90-day retention for full-text search across all system and application logs

Replicability

Cross-Sector Application

Our modular architecture is designed for replication across any sector requiring strict compliance automation.

Financial Reporting

Reserve Bank of India (RBI)

  • Automated KYC document verification and PII anonymisation for banking compliance
  • Financial statement summarisation for RBI Annual Financial Inspection (AFI) reporting
  • Completeness assessment for Basel III regulatory submissions and SEBI filings

Legal Contract Review

Ministry of Corporate Affairs (MCA)

  • Contract clause extraction and risk scoring for M&A due diligence workflows
  • Regulatory filing summarisation for annual return compliance (MCA-21 portal)
  • PII anonymisation in legal discovery documents for data protection compliance

Public Health Surveillance

Indian Council of Medical Research (ICMR)

  • Epidemiological report summarisation for IDSP (Integrated Disease Surveillance Programme)
  • Anonymisation of patient-level surveillance data for research collaboration and publication
  • Completeness assessment for notifiable disease reporting across 735 district surveillance units

Explore Our Technology

Request a technical deep-dive with our AI engineering team.