Our Advanced Data Enrichment solution embeds “man-in-the-middle” enrichment steps within your ETL pipelines. We apply business rules, large language models, and third-party data feeds to generate tags, scores, and derived attributes tailored to your needs.
Organizations struggle to extract meaningful context from disparate datasets—leading to missed insights, manual workarounds, and suboptimal analytics outputs.
A richly enriched dataset that’s primed for reporting, machine learning, and decision support—delivering deeper segmentation, risk insights, and operational flags automatically.
We implement geographic, industry, and client-profile risk tiers (Low/Medium/High), compliance flags for PEPs, sanctions, and internal watchlists, and operational markers such as dormant accounts and transaction recency—all configured through intuitive rule screens.
Our solution provides sentiment analysis, next-action recommendations, and summary tags via large language models, complete with custom prompt engineering and human-in-loop validation for accuracy.
Integrations with market intelligence providers such as Preqin, PitchBook, Bloomberg, and Refinitiv deliver deep profiling, while flexible connectors ensure seamless enrichment without disrupting existing pipelines.
Enrichment steps are embedded directly in your ETL pipeline with configuration screens for mapping, threshold settings, and refresh schedules—no separate jobs or scripts required.
Immediate access to enriched attributes for reporting and dashboards.
Better feature sets drive more precise machine learning outcomes.
Automated flagging reduces regulatory risk and manual review overhead.
Hybrid model deployments adapt to batch and real-time requirements.
We replaced costly, unreliable tools with a custom Azure Databricks pipelines that unifies CRM and market data into a clean Delta Lake. A bespoke ML cross-sell model now fuels predictive recommendations and automated next-best-actions within the CRM, accelerating deals and boosting revenue.