Applied AI & ML

From GenAI Pilots to Production ML

GenAI chatbots, forecasting engines, recommendation systems. With MLOps, governance, and SLAs. We ship models to production, not pilot purgatory. Every AI system includes monitoring, retraining pipelines, and ArqAI governance.

  • Ship models to production, not pilot purgatory
  • GenAI chatbots with enterprise security
  • MLOps pipelines with automated retraining
  • AI governance that satisfies regulators

50+ AI models in production | ArqAI governance platform

AI & ML Services

From GenAI to custom ML, all with production-grade governance

GenAI & LLM Solutions

Enterprise chatbots, document processing, code generation powered by Azure OpenAI, AWS Bedrock, or private LLMs.

Key Outcomes
  • 40% reduction in support tickets
  • Automated document processing
  • Enterprise security controls
Azure OpenAIAWS BedrockClaudeLangChain

Predictive Analytics

Forecasting engines for demand, churn, and risk. ML models that run in production with continuous learning.

Key Outcomes
  • 30% improvement in forecast accuracy
  • Real-time predictions
  • Automated model refresh
Databricks MLPythonTensorFlowscikit-learn

Recommendation Systems

Personalization engines for retail, media, and financial services. Real-time recommendations at scale.

Key Outcomes
  • 25% increase in conversion
  • Real-time personalization
  • A/B testing built-in
Spark MLlibTensorFlow RecommendersFeature Stores

MLOps & Model Management

CI/CD for ML with automated testing, deployment, and monitoring. MLflow, Kubeflow, or custom platforms.

Key Outcomes
  • 5x faster model deployment
  • Automated retraining
  • Version-controlled models
MLflowKubeflowDatabricks Mosaic AISageMaker

AI Governance & ArqAI

Policy-as-code, bias monitoring, drift detection. EU AI Act, GDPR compliant from day one.

Key Outcomes
  • Audit-ready AI systems
  • Automated compliance checks
  • Bias monitoring
ArqAIMLflow GovernanceGreat Expectations

Intelligent Process Automation

Document AI, intelligent OCR, and AI-powered workflows that augment human decision-making.

Key Outcomes
  • 80% reduction in manual processing
  • Human-in-the-loop where needed
  • Measurable ROI
Document AIUiPath AIPower Automate AI

AI Projects We've Built

Real AI implementations. Real business outcomes.

Fortune 500 BankFinancial Services

Forecasting models took weeks to retrain and deploy, missing market changes

30%Improvement in forecast accuracy
DailyAutomated model retraining
$5MAnnual cost savings
Databricks MLMLflowPython
National RetailerRetail

Generic recommendations driving customers to competitors

25%Increase in conversion rate
Real-timePersonalization at scale
15%Lift in average order value
TensorFlowSparkRedis
Healthcare ProviderHealthcare

Manual claims processing taking 72 hours average

4 hoursReduced processing time
95%Automated accuracy
80%Reduction in manual work
Document AIAzure OpenAIPython

Why Choose ACI for AI & ML

What makes us different

Production, Not Pilots

50+ AI models in production across Fortune 500 clients. We ship models that run 24/7 with SLAs.

50+ models in production

ArqAI Governance Platform

Our own AI governance platform for enterprises scaling AI responsibly. Policy-as-code, bias monitoring, audit trails.

EU AI Act compliant out of box

MLOps from Day One

Every model ships with CI/CD, monitoring, and automated retraining. No model drift surprises.

Zero production model failures

Enterprise Security

Private LLM deployments, data residency controls, and SOC 2 compliant architectures.

ISO 27001 certified

Common Questions About AI & ML

How do you handle AI governance and compliance?
We use ArqAI, our purpose-built AI governance platform, to implement policy-as-code, automated bias monitoring, and audit trails. This ensures compliance with EU AI Act, GDPR, and industry-specific regulations from day one.
Can we use private LLMs for sensitive data?
Yes. We deploy private LLMs on your infrastructure (Azure OpenAI, AWS Bedrock, or on-prem) with full data residency controls. Your data never leaves your environment.
What about model drift and retraining?
Every model includes automated drift detection and retraining pipelines. Most models refresh daily or weekly depending on your data velocity. You see performance metrics in real-time dashboards.
How long does a typical AI project take?
3-6 months for most AI implementations. GenAI chatbots can be faster (8-12 weeks). Complex ML systems with custom models take 6-12 months.

Ready to Ship AI to Production?

Talk to an AI architect about your specific use case. No sales pitch, just an engineering conversation about what's actually possible.

Talk to an AI Architect