Service — AI, Machine Learning & Automation

Practical AI and automation built on your existing data and cloud infrastructure

We help organizations move from AI curiosity to operational capability—selecting the right models, building on sound data infrastructure, and integrating machine learning and intelligent automation into workflows that produce consistent, measurable results.

Discuss an AI Engagement

AI that connects to the work, not just the conversation

Most organizations have had the AI strategy conversation. Fewer have translated it into systems that produce consistent, measurable business value. The gap between "we're exploring AI" and operational capability is almost always a data problem, an architecture problem, or an integration problem—not a technology problem.

We are model-agnostic. Whether the right answer for your use case is Claude from Anthropic, Gemini from Google, GPT-4o from OpenAI, Grok from xAI, or an open-weight model like Llama or Mistral—we evaluate options against your requirements and help you build on the best fit. Our implementation work connects to our data architecture and cloud governance practice areas so AI runs on a foundation that's actually ready for it.

What we consistently find: AI initiatives stall because the underlying data isn't clean, governed, or accessible. Before building models or deploying AI services, organizations need the data foundation to support them. Our data architecture practice and AI practice are designed to work in sequence—or together.
What We Deliver

Core capabilities

Strategy

AI Readiness Assessment & Roadmap

Before recommending tools or building models, we assess your data maturity, infrastructure readiness, and organizational capacity. We produce a clear AI roadmap that prioritizes initiatives by business value and feasibility—not hype.

Data readiness and quality evaluation
Use case identification and prioritization
Azure AI platform and tooling assessment
Build vs. configure vs. integrate decision framework
Skills gap and change readiness evaluation
Phased implementation roadmap with clear milestones
LLM Selection

LLM Evaluation & Model Selection

Choosing the right large language model matters. Capability, cost, latency, data privacy terms, deployment options, and context window size all vary significantly across providers. We evaluate the leading frontier models against your specific use case and help you build on the one that actually fits—not the one with the most marketing spend behind it.

Anthropic Claude — strong reasoning, long context, reliable instruction-following, safety-focused design
OpenAI GPT-4o / o1 / o3 — broad capability, multimodal, deep ecosystem and tooling support
Google Gemini — strong multimodal performance, deep Google Workspace and Vertex AI integration
xAI Grok — real-time data access, strong for reasoning-heavy and research-oriented applications
Open-weight models — Llama, Mistral, Phi, and others for on-premises, air-gapped, or cost-sensitive deployments
Model benchmarking against your actual tasks, data, and quality thresholds
Data privacy and residency evaluation by provider
Total cost of ownership analysis across hosting and inference options
AI Platform

AI Platform & Cloud Service Integration

Once the right model is selected, we handle the infrastructure side—deploying through managed API endpoints, Azure AI Studio, Vertex AI, or self-hosted runtimes depending on your data residency, latency, and cost requirements. We integrate AI capabilities into your existing systems, not alongside them.

Azure OpenAI Service and Azure AI Foundry deployment
Anthropic Claude API integration (direct and via AWS Bedrock or Vertex AI)
Google Vertex AI and Gemini API implementation
Azure AI Search for retrieval-augmented generation (RAG)
Self-hosted model deployment (Ollama, vLLM, Azure ML endpoints)
Prompt engineering, system prompt design, and output validation
Azure Cognitive Services (vision, language, speech, document intelligence)
Responsible AI review and content filtering configuration
Machine Learning

Custom ML & Predictive Modeling

When pre-built AI services aren't sufficient for your use case, we design and build custom machine learning pipelines using Azure Machine Learning—from data preparation and feature engineering through model training, evaluation, deployment, and monitoring.

Azure Machine Learning workspace configuration
Data preparation and feature engineering pipelines
Model training, validation, and experiment tracking
MLOps pipeline design (CI/CD for models)
Model deployment to endpoints (real-time and batch)
Drift detection and model performance monitoring
Automation

Intelligent Process Automation

Many operational inefficiencies don't require machine learning—they require well-designed automation. We identify manual, repetitive, and error-prone processes and build intelligent automation using Power Automate, Logic Apps, and Azure Functions that free your team to do higher-value work.

Process assessment and automation opportunity mapping
Power Automate flow design and implementation
Azure Logic Apps for system integration workflows
Azure Functions for custom serverless automation
Approval, notification, and routing automation
RPA evaluation and implementation guidance
Copilot & M365 AI

Microsoft Copilot & Microsoft 365 AI Integration

Microsoft 365 Copilot and related AI capabilities represent a meaningful productivity opportunity—but realizing that value requires the right licensing, data governance, and configuration foundation. We help organizations deploy and govern these tools effectively and securely.

Microsoft 365 Copilot readiness assessment
Data governance prerequisites (sensitivity labels, access review)
Copilot deployment and rollout planning
SharePoint and Microsoft Graph data optimization
Custom Copilot Studio agent development
Adoption enablement and prompt guidance for teams
AI-Ready Data

AI-Ready Data Architecture

AI is only as good as the data it operates on. We design data architectures specifically optimized for AI and ML workloads—ensuring data pipelines, storage layers, and semantic models are structured to support model training, retrieval-augmented generation, and AI-driven analytics.

Lakehouse and feature store architecture design
Vector database and embedding storage for RAG patterns
Data pipeline optimization for ML training workloads
Data quality and governance for AI reliability
Microsoft Fabric for unified AI + analytics platform
Lineage and auditability for AI inputs and outputs
Databricks

Databricks Platform & Lakehouse Engineering

Databricks is one of the most capable platforms for unified data engineering, machine learning, and analytics at scale. We design and implement Databricks environments built on sound lakehouse architecture—with Unity Catalog for governance, medallion patterns for data quality, and full lineage visibility from source to model.

Databricks workspace setup and Azure integration
Medallion architecture design (Bronze / Silver / Gold layers)
Unity Catalog implementation and metastore configuration
Data lineage tracking across pipelines and notebooks
Delta Lake table design and optimization
Databricks Workflows and job orchestration
ML experiment tracking with MLflow on Databricks
Unity Catalog–enforced access controls and data classification
Applied Use Cases

Where organizations are putting AI to work

These are representative examples of the AI and automation initiatives we help organizations design and implement — grounded in real business value, not proof-of-concept demonstrations.

01
Internal knowledge retrieval from unstructured documents
Azure AI Search + Azure OpenAI (RAG architecture) enabling staff to query internal policy documents, contracts, and knowledge bases in natural language—with citations back to source documents.
02
Automated document classification and routing
Azure Document Intelligence + Power Automate to classify incoming documents, extract structured data, and route them through approval and processing workflows—eliminating manual data entry.
03
Predictive analytics for operational decisions
Custom ML models built on Azure Machine Learning to forecast demand, flag anomalies, identify at-risk conditions, or predict outcomes from historical operational data.
04
Microsoft 365 Copilot deployment and governance
End-to-end Copilot readiness—data access review, sensitivity labeling, SharePoint cleanup, deployment sequencing, and team enablement so Copilot produces value without creating information risk.
05
Repetitive process automation across systems
Power Automate and Logic Apps replacing manual hand-offs between systems—HR onboarding, IT provisioning, approval workflows, cross-system data sync, and notification routing.
06
Security and operations anomaly detection
ML-assisted detection of anomalous behavior patterns in identity and cloud workload telemetry, feeding into Microsoft Sentinel rules and alerts for faster security response.
07
Databricks lakehouse with end-to-end data lineage
Medallion architecture on Databricks (Bronze/Silver/Gold) with Unity Catalog enforcing access controls across all data assets—and full lineage visibility from ingestion source through to ML model inputs and analytical outputs.
08
Migrating a legacy data warehouse to a Databricks lakehouse
We design and execute migrations from SQL Server, Synapse, or other warehouse platforms to Delta Lake on Databricks—preserving business logic, improving query performance, and establishing Unity Catalog governance that the old platform never had.

How AI connects to the rest of what we do

AI and automation aren't standalone disciplines in our practice—they connect directly to cloud governance, data architecture, and security. The organizations that get the most from AI are the ones that have their cloud, data, and security foundations in order first.

Connected to
Azure Cloud Management
AI workloads need governed Azure environments—proper identity controls, resource policies, network design, and cost management. We build the cloud foundation that AI services operate on.
Azure Cloud
Connected to
Data Architecture & Engineering
Every AI capability depends on accessible, clean, governed data. Our data practice builds the pipelines, models, and platforms that AI services consume. Without this foundation, AI delivers inconsistent results.
Data Architecture
Connected to
Cybersecurity
Copilot and AI tools can expose sensitive data if access controls and sensitivity labels aren't in place first. We ensure AI deployments don't create new security exposure through ungoverned data access.
Cybersecurity
What Changes

From exploration to operational capability

Less
Manual, repetitive work across operations, administration, and data handling — replaced by reliable automation
Faster
Time to insight from data — AI-assisted analysis and retrieval reducing hours of manual research to minutes
Durable
AI systems built on governed data and cloud foundations — reliable, auditable, and improvable over time
Get Started

Ready to move from AI conversation
to operational capability?

We start with an honest assessment of your data readiness and use case fit—so the first thing we build is the right thing to build. Let's talk.

Discuss an AI Engagement Also: Data Architecture