Beyond the Hype:
Enterprise-Grade
Deep Learning,
Engineered for ROI.
The era of AI tourism is over. We transition your organization from experimental PoCs to high-performance, "Evergreen" value streams using precision engineering and sovereign infrastructure.
The “Industrial AI” Reality Check
"In the modern enterprise, the model is a commodity; the data infrastructure is the competitive advantage. While consultancies sell the vision of AI reinvention, Prism Infoways engineers the reality. We move you beyond ad-hoc data preparation to a Unified Feature Architecture—ensuring that the data guiding your billion-dollar decisions is mathematically identical in training and production."
Engineering Excellence
Precision-engineered solutions for the most demanding data environments.
Unified Feature Stores
Implementation of Tecton, AWS SageMaker, or Databricks Feature Stores to centralize logic.
Real-Time Streaming Pipelines
Low-latency engineering using Apache Flink and Spark Streaming for sub-100ms inference.
Point-in-Time Correctness
Complex temporal joins to eliminate data leakage and "time travel" errors in historical training sets.
Batch & Historical Backfill
Optimized processing of terabytes of data lake history (Snowflake/Delta Lake) without cost blowouts.
Drift Detection & Observability
Integration of Arize or Evidently AI to monitor feature quality and alert on distribution shifts.
Governance & Lineage
Full audit trails mapping raw data sources to model predictions for GDPR/SR 11-7 compliance.
Need a custom architecture? Let's discuss your specific requirements.
Schedule Infrastructure AuditStandard Data Chaos
Prism Architecture
The Hard Statistics
of Precision
Velocity (Time-to-Market)
Slash the "productionization gap." Move features from a Data Scientist's notebook to a live API in minutes, not months.
Reliability (Zero Skew)
Guaranteed Consistency: The logic you train on is the exact logic you serve. No more "silent failures" in production.
Economic Efficiency
40% Productivity Gain: Liberate your high-value data scientists from "janitorial" data cleaning so they can focus on modeling.
Scalability
Write Once, Reuse Everywhere: Build a "Customer_LTV" feature once and let Fraud, Marketing, and Sales teams reuse it instantly.
The Strategic Flow
The Audit & Definition
We map your current data silos, define feature requirements (batch vs. streaming), and select the right platform (Tecton vs. Custom).
Transformation Engineering
We refactor ad-hoc Python scripts into robust Spark/Flink jobs, establishing the "Offline" (History) and "Online" (Serving) stores.
Materialization & Sync
We configure the automated pipelines that backfill history and keep the serving layer fresh within milliseconds.
Governance & Scale
We implement feature catalogs for discovery and set up monitoring for data drift, handing off a "Glass Box" system to your team.
Who Needs This?
Tailored precision for every stage of industrial growth.
The “MVP” Accelerator
Focus: Rapid deployment of your first Real-Time Fraud or Recommendation pipeline.
Tech: Open Source (Feast) or Managed (AWS SageMaker).
Goal: Get to production in < 4 weeks.
The “Industrial AI” Transformation
Focus: Governance, Legacy Integration (Mainframe/SQL), and Multi-Department Feature Sharing.
Tech: Tecton, Databricks, Snowflake.
Goal: Reinvention and Governance.
We Are Platform Agnostic.
We Build What Fits You.
Feature Platforms
Compute
Storage
Observability
The Deep Dive
Standard DE focuses on moving data. Feature Engineering focuses on transforming data for ML, specifically solving latency (speed) and skew (accuracy) issues that standard ETL ignores.
Yes. A Data Lake is great for history (training), but it is too slow for real-time inference (serving). We build the bridge that connects your Lake to your App.
Absolutely. We specialize in "Brownfield" deployments, extracting signals from legacy logs and SQL databases without disrupting core operations.
It is the #1 reason AI projects fail. It happens when the data in production looks different than the training data. We architect systems specifically to prevent this.
Our "Real-Time Jumpstart" engagement typically deploys a working production pipeline in 4–6 weeks.
Still have technical questions?
Deploy Your
Feature Store.
Stop managing ad-hoc scripts. Initialize a governed, production-grade feature platform in minutes, not months.