From Kafka streams to RAG indexes to forecasting models — how Anagha builds the data infrastructure that converts the data you already own into decisions that run at machine speed.
The average Fortune 500 enterprise runs 1,000+ data-producing systems — ERPs, CRMs, IoT sensors, transaction databases, application logs, clickstream events, third-party APIs. The data volume doubles every 18 months. The decision quality hasn't improved proportionally, because data and decisions remain fundamentally decoupled: data sits in warehouses, decisions sit in dashboards, and the analyst who connects them is a bottleneck measured in days.
The gap isn't storage or compute. It's the intelligence layer between data and action — the ability to detect that a customer is about to churn before they cancel, that a supply chain disruption is emerging before inventory runs out, that a patient's vitals trajectory requires escalation before the alarm threshold is reached. This is what Anagha's intelligent data practice builds.
Key finding: In our engagements, enterprises already have 85–95% of the data needed for the AI-powered decisions they want to make. The gap is architecture and operationalization — not data collection. The work is building the pipelines that activate the data, not acquiring more of it.
Anagha's anomaly detection pipeline processes event streams in sub-100ms latency. The architecture uses a two-stage approach: a lightweight statistical detector (rolling Z-score, MAD) flags candidates in real time; a heavier ML model (Isolation Forest, Autoencoder) scores flagged events for confirmation. This avoids the ML model being called on every event (too expensive) while still catching subtle anomalies that statistical methods miss.
For financial services: detecting suspicious transaction patterns in real-time payment streams. For healthcare: flagging vital sign trajectories that precede adverse events. For hospitality: identifying demand anomalies that trigger dynamic pricing adjustments before the RevPAR window closes.
Enterprise forecasting (demand, revenue, inventory, capacity) traditionally runs overnight in batch. Anagha's forecasting pipelines run on 15-minute cadences, incorporating live event streams alongside historical patterns — so the forecast responds to today's anomalies, not just yesterday's averages. We use ensemble approaches: Prophet for seasonality and trend decomposition, gradient boosting (XGBoost/LightGBM) for feature-rich tabular forecasting, and neural architectures (TiDE, N-BEATS) for long-horizon multivariate forecasting.
The most immediate business value often comes from making unstructured enterprise knowledge — policy documents, historical case notes, product catalogs, email threads, support tickets — answerable by natural language query. Anagha's RAG platform connects to your existing data sources via Airbyte connectors, builds and maintains a vector index, and exposes a query API that grounds every response in your actual enterprise data. No generic LLM hallucinations about your specific products, contracts, or procedures — only answers sourced from your own knowledge base.
Use case example: A healthcare payor's claims team processes 2,000 prior auth requests daily. RAG over 12 years of clinical policy documents + claim history answers 73% of authorization questions automatically — in 2 seconds, compared to 4 hours of manual policy lookup. The remaining 27% route to clinical staff with the relevant policy excerpts pre-surfaced.
Anagha's data intelligence assessment maps your existing data assets to the highest-value decision pipelines — and shows you the gap in concrete architecture terms.