What problems does the AI-powered patent and sequence platform solve?

The solution automates ingestion, annotation, semantic search, and metadata enrichment for patents and biological sequences. It eliminates manual processing bottlenecks, improves data quality, and accelerates research and IP analysis workflows.

How does the system improve search and discovery?

The platform combines Elasticsearch with vector databases and LLM-based embeddings to deliver semantic and similarity search. This enables researchers to locate related patents, sequences, and annotations with higher accuracy and contextual relevance.

What AI models and technologies were used?

The solution uses LLaMA-based models, OpenAI APIs, machine learning pipelines, NLP techniques, Elasticsearch, vector similarity search, AWS cloud services, and on-prem components such as MinIO and PostgreSQL for flexible deployment.

How does the platform ensure data quality?

Continuous anomaly detection, metadata validation, template checks, and automated quality reports ensure consistent and compliant datasets. AI models are retrained regularly to maintain extraction accuracy and reduce errors.

Can the platform be deployed on-premises?

Yes. The platform supports both cloud and on-prem deployments. Components such as MinIO, PostgreSQL, and modular AI pipelines allow secure, compliant, and scalable operation within the client's internal infrastructure.

AI-Powered Patent & Sequence Intelligence Platform

Azati developed an AI-driven platform that enables the client to intelligently analyze patents and biological sequences. The solution automates search, annotation, and structuring of large-scale datasets, helping researchers and IP analysts gain actionable insights faster and more accurately.

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50M+

documents and sequences processed

72%

reduction in manual work via AI

91%

search accuracy and result relevance

All Technologies Used

Python

Luigi

RabbitMQ

n8n

MinIO

PostgreSQL

Elasticsearch

AWS

LLaMA

OpenAI

Motivation

The project aimed to process massive volumes of unstructured patent and biological sequence data while ensuring high-quality metadata, scalable processing, efficient retrieval, compliance with global IP standards, and automation of labor-intensive workflows. It focused on enabling actionable insights, faster discovery, and improved efficiency for researchers and IP analysts.

Main Challenges

Patent documents and biological sequences were in multiple formats (PDF, XML, FASTA, GenBank) and updated continuously. Processing terabyte- to petabyte-scale datasets required large-scale ingestion, normalization, cleaning, and indexing pipelines capable of handling diverse and unstructured data.

Many records lacked standardized metadata, hindering search, classification, and contextual understanding. Relationships between sequences, annotations, and patent claims were often lost during manual processing, reducing analytical value and complicating compliance and reproducibility.

Annotation, summarization, and monitoring were labor-intensive, error-prone, and difficult to scale. Researchers spent significant time curating data, tracking updates, and maintaining quality control, limiting overall operational efficiency.

The client required a modular AI system capable of automating metadata enrichment, semantic search, intelligent summarization, and workflow automation. The solution had to integrate with existing pipelines, support cloud and on-premises deployment, and be flexible for future AI-driven capabilities.

Our Approach

AI Module Integration

Developed several AI modules including: AI Assistant for interactive patent interpretation and sequence annotation; AI Summary for automatic domain-specific summaries; AI Dataset Analysis & Enhancement for cleaning, clustering, and enriching large datasets using ML, NLP, and vector similarity search.

Flexible Architecture and Deployment

Implemented modular architecture supporting cloud (AWS S3, RDS, Step Functions) and on-premises (MinIO/PostgreSQL) deployments. Integrated LLaMA/MCP models with OpenAI API/Amazon Bedrock, Elasticsearch, and vector databases for semantic search and embeddings.

Error Detection and Quality Assurance

Introduced automated anomaly detection, continuous AI model retraining, template and metadata consistency checks, and quality assurance reporting to ensure accurate extraction and high-quality data.

Performance Monitoring and Scalability

Enabled real-time workload monitoring, operational dashboards, dynamic cloud resource scaling, and performance analytics to maintain system stability and handle large-scale digitization projects.

User Training and Documentation

Prepared detailed user guides and onboarding materials for researchers and IP analysts to ensure smooth adoption of AI-assisted workflows and automated data processing.

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Solution

AI-Powered Data Ingestion & Normalization Module

This module automates large-scale ingestion and normalization of patent documents and biological sequences across formats (PDF, XML, FASTA, GenBank). It standardizes heterogeneous datasets, removes duplicates, cleans corrupted records, and prepares them for AI-powered search and metadata enrichment.

Key capabilities:

Multi-format document & sequence ingestion
Automated normalization, cleaning, and deduplication
Scalable indexing pipelines for terabyte–petabyte datasets

AI Metadata Enrichment & Context Recognition Module

This module enriches unstructured records with high-quality metadata, restoring lost connections between sequences, annotations, and patent claims. It ensures compliance with IP standards and improves search, classification, and structuring..

Key capabilities:

AI-based metadata generation & standardization
Context and relationship extraction
Compliance-ready structured data output

Semantic Search & Discovery Module

Provides powerful semantic and similarity-based search across patents and sequences using Elasticsearch, vector databases, and LLM embeddings. It enables fast and accurate retrieval with context-aware ranking.

Key capabilities:

Semantic, hybrid, and vector similarity search
Domain-specific embeddings for patents & sequences
Intelligent exploration and filtering of large datasets

AI-Assisted Summaries, Annotation & Insights Module

LLM-powered components automate sequence annotation, patent interpretation, and domain-specific summarization. This significantly reduces manual workload and accelerates research workflows.

Key capabilities:

AI-driven patent and sequence summarization
Automatic annotation & insight generation
Interactive AI assistant for researchers and IP analysts

Business Value

Massive Data Processing: The platform successfully processed over 50 million patent documents and biological sequences, enabling scalable analysis of terabyte- to petabyte-scale datasets.

Reduction in Manual Work: Automated annotation, summarization, and metadata enrichment reduced manual effort by 72%, freeing researchers and IP analysts to focus on higher-value tasks.

Enhanced Search Accuracy: AI-driven semantic search and enriched metadata improved search accuracy and result relevance to 91%, enabling faster discovery of patents and sequence similarities.

Accelerated Research Workflows: AI-generated summaries and automated insights significantly reduced the time required for patent analysis and sequence interpretation, accelerating scientific research and IP evaluation.

Operational Transparency: Real-time dashboards and performance monitoring provided administrators with complete visibility into data volumes, workflow progress, and system performance, ensuring stability during large-scale processing.

Actionable Insights: Researchers and IP analysts gained structured, interpretable data with AI-enriched annotations and contextual relationships, enabling faster decision-making and more accurate IP analysis.