Affine
Data Science & Machine Learning

ML in Production.
Not Just in Notebooks.

Affine builds ML systems engineered for production - forecasting, propensity, computer vision, MLOps - accuracy at enterprise scale, not promising prototypes.

Data Science and Machine Learning in production
The Challenge

Most ML Projects Die in PowerPoint.
Ours Run in Production.

Models that hit 90%+ accuracy in a notebook routinely fail to ship - broken pipelines, governance gaps, monitoring debt. Affine engineers the production-grade infrastructure around every model so ML stays live, observable, and accountable to the business.

Proof Points
0+
Years in Production ML
0+
Fortune 500 Clients
0%
Client Retention Rate
Solutions

Four Capabilities. One Production-Ready ML Stack.

Each layer is independently deployable and proven in production - combine them for compounding returns.

01

Forecasting & Propensity Models

Tailored ML models for demand forecasting, customer propensity, anomaly detection, and dynamic pricing. Deployed at SKU and customer granularity in production - measurable lift on the KPIs that matter.

0%
accuracy uplift
02

Deep Learning & Computer Vision

Production vision pipelines for defect detection, facial recognition, traffic monitoring, and retail behavior analytics. NLP and speech models for the unstructured side - extracting decision-grade signal from raw input.

0%
accuracy
03

MLOps & Model Lifecycle

CI/CD pipelines, A/B testing, model monitoring, and feature-store governance. Every model under version control, observable in production, deployable on demand.

0%
faster time-to-market
04

Reinforcement Learning & Optimization

RL strategies for continuous decision refinement and scenario optimization for high-stakes planning. Systems that learn and adapt while business strategies stay resilient under uncertainty.

Adaptive decisioning at scale
Our Approach

The Engine Behind Production ML.

Four proven systems - each battle-tested in enterprise deployments - working in concert.

01

MLOps Framework

End-to-end model management lifecycle - ingestion, training, validation, deployment, monitoring. A stable foundation for ML at enterprise scale, aligned to business goals not lab metrics.

Stable model ops
02

Azure ML Reference Architecture

Production patterns for Azure-native ML - Model-as-a-Service, Feature Store, DevOps pipelines. Reduce time-to-market on Azure deployments without rebuilding from scratch.

Azure-native ML at speed
03

Customer 360° Accelerator

Pre-built hyper-personalization stack - user journey analysis, pain-point detection, behavioral segmentation - operating across the full customer view.

Personalization at scale
04

Industry Use-Case Library

Pre-built ML solutions tested in CPG, Insurance, Retail, and Automotive. Production-grade starting points - not greenfield builds - for industry-specific outcomes.

Cross-industry deployments
Case Studies

Proof, Not Promises.

Automated Defect Detection for a CPG Femcare Line

Deep-learning vision pipeline replacing manual inspection across high-velocity production - error rates dropped, throughput climbed, operators redeployed to higher-value work.

Measured Impact
  • →0% defect-detection accuracy
  • →0% reduction in manual inspection effort
  • →~1,000 man-days reclaimed annually
CPG · Manufacturing Quality

Demand Forecasting & Inventory Optimization for an F&B Enterprise

Production ML stack streamlining product-portfolio decisions - SKU-level demand signal feeding inventory and replenishment workflows.

Measured Impact
  • →SKU × geography forecast accuracy lift across the portfolio
  • →Inventory carrying cost reduction at scale
  • →Replenishment shifted from weekly cycles to demand-driven
F&B · Demand Planning

Agent Performance Platform for an Insurance Enterprise

CRM-integrated KPI dashboard surfacing benchmarks, conversion patterns, and intervention triggers across the full agent network.

Measured Impact
  • →Measurable lift in agent conversion performance
  • →Faster identification of underperforming segments
  • →Real-time KPI visibility across the agent base
Insurance · Agent Performance
Start Now

Ready to put ML in production?

Talk to our experts and get a tailored roadmap for your business.

Book a Discovery Call Explore the Agentic Launchpad