Using AI To Optimize Data Flow

SAP BTP Integration Suite with AI: The Next Evolution of SAP CPI SAP has enhanced its Cloud Platform Integration (CPI) capabilities under the SAP Business Technology Platform (BTP) Integration Suite, now infused with AI and automation for smarter, self-healing integrations. Key AI-Powered Features in SAP BTP Integration Suite 1. AI-Assisted Integration Flows (SAP AI Core & Joule) Smart Mapping: AI suggests field mappings between systems (e.g., SAP S/4HANA ↔ Salesforce) by learning from past integrations. Anomaly Detection: AI monitors message processing and flags unusual patterns (e.g., sudden API failures or data mismatches). Self-Healing: Automatically retries failed calls or suggests fixes (e.g., OAuth token renewal). Example: An EDI 850 (Purchase Order) from a retailer has inconsistent product codes. AI recommends corrections based on historical data before forwarding to SAP S/4HANA. 2. Generative AI for Accelerated Development (Joule + OpenAI Integration) Natural Language to Integration Flow: Describe an integration in plain text (e.g., "Sync customer data from Salesforce to SAP every hour"), and Joule generates a draft CPI flow. Auto-Generated Documentation: AI creates integration specs and test cases. Example: A developer types: "Create a real-time API that checks credit risk before approving orders." Joule proposes: A webhook trigger from SAP Commerce Cloud. A call to a credit-scoring API. A conditional router in CPI to approve/reject orders. 3. Event-Driven AI Integrations (SAP Event Mesh + AI) Smart Event Filtering: AI processes high-volume event streams (e.g., IoT sensor data) and forwards only relevant events to SAP systems. Predictive Triggers: AI predicts when to initiate integrations (e.g., auto-replenish inventory before stockouts). Example: A logistics company uses SAP Event Mesh to track shipment delays. AI analyzes weather + traffic data to reroute shipments proactively. 4. SAP Graph + AI for Context-Aware Integrations Unified Data Access: SAP Graph provides a single API endpoint for cross-SAP data (S/4HANA, SuccessFactors, Ariba). AI Adds Context: Example: When fetching a customer record, AI automatically enriches it with related sales orders and support tickets. Real-World Use Case: AI-Powered Invoice Processing Scenario: Automatically validate supplier invoices against POs and contracts. AI Extraction: Invoice arrives via SAP Document Information Extraction (DocAI). AI parses unstructured PDFs into structured data. Smart Matching: CPI calls SAP AI Core to compare invoice line items with SAP Ariba POs. AI flags discrepancies (e.g., price changes, missing items). Self-Healing Workflow: If discrepancies are minor, AI auto-approves. If major, CPI routes to a SAP Build Workflow for human review. Result: 70% faster invoice processing with fewer errors.

We released Alto, a new system for orchestrating distributed compound AI applications that automatically streams and parallelizes execution across components like language models, retrievers, and rerankers. ℹ️ Compound AI applications chain together subcomponents such as generative language models, document retrievers, and embedding models. Applying traditional systems optimizations such as parallelism and pipelining in compound AI systems is difficult because each component has different constraints in terms of the granularity and type of data that it ingests. New data is often generated during intermediate computations, and text streams may be split into smaller, independent fragments (such as documents to sentences) which may then be re-aggregated at later parts of the computation. Due to this complexity, existing systems to serve compound AI queries do not fully take advantage of parallelism and pipelining opportunities. 💡 We present Alto, a framework that automatically optimizes execution of compound AI queries through streaming and parallelism. Bento introduces a new abstraction called nested ancestry, a metadata hierarchy that allows the system to correctly track partial outputs and aggregate data across the heterogeneous constraints of the components of compound AI applications. This metadata is automatically inferred from the programming model, allowing developers to express complex dataflow patterns without needing to reason manually about the details of routing and aggregation. 📈  Implementations of four applications in Alto outperform or match implementations in LangGraph, a popular existing AI programming framework. Alto implementations match or improve latency by between 10-30%. Link to the paper: https://lnkd.in/dV5D9b25

🚀 Big AI updates from Current Bengaluru today! Apache Flink is getting some major upgrades in Confluent Cloud that make real-time AI way easier: 🔹 Run AI models directly in Flink –Bring your model and start making predictions in real time. No need to host externally. 🔹 Search across vector databases – Easily pull in data from places like Pinecone, Weaviate, and Elasticsearch as well as your real-time streams. 🔹 Built-in AI functions – Flink now has built-in tools for forecasting and anomaly detection, so you can spot trends and outliers as the data flows in. Additionally, Tableflow for Iceberg is now GA, and Delta Lake is in early access, making it easier to connect real-time data streams to your AI workflows without managing ETL pipelines. 💡 Why this matters – AI needs fresh, fast data. These updates make it way easier to run models, retrieve data, and build real-time AI apps without stitching together a dozen different tools. Exciting times for AI + streaming! #Current2025 #Confluent #ApacheFlink #AI #RealTimeData #StreamingAI