Reviewing large volumes of patent data across multiple jurisdictions was slow, manual, and prone to missing conflicts or overlaps.
We implemented an AI-powered platform combining ML-based text extraction, NLP classification, and semantic search. The tool continuously monitors new filings and flags relevant patents in real time.
The client was dealing with over 200,000 duplicate or incomplete entries from marketing lists and partner referrals, leading to poor CRM hygiene and wasted sales effort.
We designed a rule-based and machine learning-driven fuzzy matching engine. It categorised matches by confidence level and integrated directly with the CRM for seamless deduplication and enrichment.
Departments used inconsistent BI reporting practices, creating confusion and inefficiencies in analytics.
We deployed standardized Power BI templates, semantic data models, and role-based security. This enabled consistent design and data access governance.
Finance and Ops teams were spending significant time manually consolidating reports from disconnected systems.
We created a unified data model connecting financial and operational data. Automated Power BI dashboards provided real-time visibility into spend and performance.
Inconsistent security, identity, and networking standards made cloud governance difficult and exposed the business to compliance risks.
We implemented an Azure Landing Zone with standardized VNETs/subnets, Azure AD for identity, Azure Policy for governance, and Infrastructure as Code templates.
Data silos across Finance, Sales, and Ops prevented integrated reporting and strategic insight.
We implemented a cloud-based data warehouse with standardised ETL processes and master data. Architecture was designed to scale with business needs.