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

Talk to an AI ArchitectSee AI Projects

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
  • 20% 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
  • 15% 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
  • 2-3x 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
  • 35% 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.

Enterprise ClientFinancial 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
Enterprise ClientHealthcare

Manual claims processing taking 72 hours average

4 hoursReduced processing time
88%Automated accuracy
35%Reduction in manual work
Document AIAzure OpenAIPython
See All AI Case Studies

Beyond Delivery

AI models need continuous care. Post-deployment MLOps, drift detection, retraining, and SLA-backed operations are part of how we engage.We run what we build.

Model Operations

24/7 monitoring of production models, drift detection, and incident response. When a model starts misbehaving at 2am, we're on the call.

SLA-Backed Support

Contractual response times for model failures, defined escalation paths, and accountable ownership — not a ticket into a vendor queue.

Continuous Retraining

MLOps pipelines that retrain, validate, and redeploy models as data shifts. Models improve over time instead of decaying silently.

Evolution as Partners

Roadmap co-ownership, new model delivery, and AI strategy evolution. We're with you as the AI landscape shifts around you.

Why Choose ACI for AI & ML

What makes us different

Production, Not Pilots

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

Models running 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.

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Explore related capabilities

Data Engineering
Most AI problems are data problems.
Databricks
MLflow, registry, and production ML.
Cloud Modernization
The infrastructure AI runs on.

Applied AI FAQ

Frequently asked questions

What does a production GenAI implementation require?
More than a prompt and an API key. You need governed data for retrieval, a RAG or agent design that fits the use case, evaluation before launch, guardrails, and monitoring after. The model is the easy part.
How do you pick the first AI use case?
We look for a job with clear value, available data, and room to be wrong sometimes. Chasing the flashiest use case first is how pilots die. We would rather ship one that pays for itself and earns the next one.
How long from idea to production AI?
A scoped use case with data in reasonable shape reaches production in 8 to 14 weeks. If the data is not ready, that comes first, and we will tell you plainly rather than paper over it.
How do you keep models from drifting or misbehaving?
Evaluation gates before launch, then monitoring for drift, cost, and quality after, with a human in the loop where the stakes need it. A model nobody is watching is a model nobody should trust.
Who owns model risk and governance?
We build it in: model registry, versioning, audit trails, and policy checks. Regulated buyers have to show their work, and bolting governance on after the fact never goes well.

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