Running in Production. Most enterprise AI stays in proof-of-concept because the data is not ready, the model is not integrated into the decision workflow, and nobody is monitoring for drift. Symhas builds production AI on your operational data — forecasting, classification, and generative AI that produces business outcomes, not PowerPoint slides about potential outcomes.
Actionable Intelligence.
Every capability delivered by Symhas data and AI architects. Fixed price. Production-ready. Your team owns the models before we leave.
Demand forecasting, churn prediction, fraud detection, and risk scoring — ML models trained on your historical operational data and deployed into the systems your teams already use, with monitored accuracy and automated retraining when performance drifts.
Production ML platform deployment on Vertex AI, SageMaker, and Azure ML — MLOps pipelines automating model training, evaluation, and deployment, Feature Stores ensuring training-serving consistency, and generative AI (Gemini, Bedrock, GPT-4) on private enterprise data with citation and governance.
Cloud data warehouse architecture and engineering on BigQuery, Snowflake, Azure Synapse, and Oracle Autonomous Database — Dataflow and dbt pipelines transforming raw Oracle Fusion, ERP, and operational data into governed, analytics-ready datasets with full lineage, classification, and access controls.
Governed self-service analytics on Looker, Power BI, Oracle Analytics Cloud, and Looker Studio — semantic layers with canonical metric definitions, row-level access controls filtering each user to their authorised data, and executive dashboards that produce one consistent number across every report.
No proof-of-concepts. No projected outcomes. Numbers from live deployments.
↑33% Accuracy. ↓21% Holding Costs.
National retailer with 500+ locations running fragmented reporting across 12 databases. Oracle SCM connected to BigQuery via GoldenGate CDC, Vertex AI demand forecasting trained on real-time inventory data, and Looker dashboards replacing 12 separate BI tools with one definition of every metric.
↓73% False Positives. ↑41% Detection Accuracy.
Asset management firm with $25B AUM running manual transaction review with a 73% false positive rate. NLP fraud detection model on Azure ML, trained on 47 data sources unified in Azure Synapse, integrated directly into the Oracle Fusion AP workflow — zero additional headcount required for the increased volume.
↓35% Unplanned Downtime. 72hr Advance Warning.
Fortune 100 manufacturer with 12 sites and 45,000 employees running reactive maintenance across critical production equipment. ML model on sensor telemetry data predicted equipment failure 72 hours in advance, reducing unplanned downtime by 35% and saving $6.3M annually in avoided production losses.
↓18% Readmission Rate. HIPAA-Compliant Architecture.
Regional health system with 450+ beds unifying 14 clinical and financial systems on Oracle Cloud. Readmission prediction model trained on unified clinical history data, deployed into the care coordination workflow — 18% readmission reduction, zero HIPAA findings at audit, architecture validated by external compliance review.
You Are Trying to Automate. A 30-minute AI assessment with a Symhas data scientist. We will review your data landscape, identify the highest-value use case, and tell you whether your data is ready to support it — before the engagement begins. No commitment. No vendor pitch. An honest assessment of what your data can support today.