AI & Machine Learning Consulting Services Purpose Built AI & ML Create new capabilities and transform your organization with artificial intelligence by establishing an AI strategy, building practical ai solutions and operationalizing machine learning models all supported by strong ml ops and lifecycle management. Talk to Concurrency Custom AI and Machine Learning Consulting & Development Establish Your AI Strategy Define a clear roadmap for how artificial intelligence will create value for your organization, aligning AI initiatives with business goals and priorities. Build Practical AI Solutions Develop and deploy AI-powered applications tailored to real business needs, driving innovation and measurable outcomes. Operationalize Machine Learning Models Move machine learning models from experimentation to production, ensuring they deliver ongoing value through robust MLOps and lifecycle management. Common AI Development & Implementation Challenges We Solve Align AI to Your Business Defining a clear AI strategy that aligns with business goals and delivers measurable value. Business Value Focused AI Developing practical AI solutions that address real business needs and drive innovation. Production-Ready ML Models Moving machine learning models from experimentation to production, ensuring robust MLOps and lifecycle management. Responsible AI Governance & Development Ensuring responsible governance, security, and compliance throughout the AI and ML implementation process. Ready to talk to us about building AI that actually delivers business value? Let’s Talk!! AI & Machine Learning Consulting Services AI Strategy & Value Roadmap Turn AI ambition into a clear, executable plan. Learn More Natural Language Solutions Enable systems that understand and respond like humans. Learn More Generative AI & RAG Solutions Ground generative AI in your data—safely and responsibly. Learn More Predictive ML Models Anticipate outcomes and make smarter decisions. Learn More Detection ML Models Identify anomalies, risks, and issues before they escalate. Learn More MLOps & Model Lifecycle Management Move models from experimentation to production—with confidence. Learn More AI Strategy & Value Roadmap Natural Language Solutions Generative AI & RAG Solutions Predictive ML Models Detection ML Models MLOps & Model Lifecycle Management AI Strategy & Value Roadmap Turn AI ambition into a clear, executable plan. Learn More Natural Language Solutions Enable systems that understand and respond like humans. Learn More Generative AI & RAG Solutions Ground generative AI in your data—safely and responsibly. Learn More Predictive ML Models Anticipate outcomes and make smarter decisions. Learn More Detection ML Models Identify anomalies, risks, and issues before they escalate. Learn More MLOps & Model Lifecycle Management Move models from experimentation to production—with confidence. Learn More AI Strategy & Value Roadmap AI Strategy & Value Roadmap helps organizations define how artificial intelligence will deliver real business value. We work with leaders to identify high‑impact use cases, align AI initiatives to business priorities, and create a practical roadmap that balances quick wins with long‑term capability building. Define a clear AI vision aligned to business goals Prioritize use cases based on value, feasibility, and risk Establish governance, security, and responsible AI guardrails Create a phased roadmap from pilot to enterprise scale Natural Language & NLP Solutions Natural Language Solutions leverage AI to interpret, generate, and interact using human language. We help organizations apply natural language capabilities to real business scenarios—improving access to information, automating interactions, and enhancing user experiences. Enable conversational and language‑driven experiences Extract meaning and intent from unstructured text Improve access to information through natural language interfaces Apply NLP to real workflows and business processes GenAI Knowledge & Assistant Solutions (RAG) GenAI Knowledge & Assistant Solutions use Retrieval‑Augmented Generation (RAG) to deliver accurate, context‑aware responses based on your enterprise data. We help organizations build assistants that answer questions, surface insights, and support decisions—without hallucinations or data leakage. Ground generative AI in trusted enterprise knowledge Improve accuracy and relevance of AI responses Enable secure, permission‑aware access to information Deploy assistants for employees, customers, or operations Predictive ML Models Predictive ML Models use historical data to forecast future behavior and trends. We help organizations build and deploy models that support planning, optimization, and proactive decision‑making across key business functions. Forecast demand, risk, or performance trends Support planning and optimization with data‑driven predictions Apply machine learning to structured business problems Turn historical data into forward‑looking insights Detection ML Models Detection ML Models focus on identifying unusual patterns, anomalies, or signals that indicate potential problems or opportunities. We help organizations deploy detection models that improve awareness, reduce risk, and support faster response. Detect anomalies and unusual behavior in real time Identify risks, defects, or compliance issues early Monitor systems and processes at scale Support faster investigation and response MLOps & Model Lifecycle Management MLOps & Model Lifecycle Management ensures machine learning models deliver ongoing value after deployment. We help organizations operationalize models with monitoring, governance, and automation—so AI solutions remain reliable, scalable, and compliant over time. Operationalize ML models for production use Monitor performance, drift, and model health Automate deployment, retraining, and versioning Ensure governance, security, and responsible AI practices Why Leading Enterprises Choose Concurrency for AI Milwaukee-based, Midwest-focused, nationally capable. Concurrency, Inc. is a Microsoft Solutions Partner headquartered in the Milwaukee area, helping Midwest manufacturers, financial institutions, and professional-services firms turn technology, and now AI, into business results. Since 1989. Leading enterprises choose Concurrency to move beyond AI experimentation and build purpose‑built, production‑ready AI that delivers measurable business value. We combine strategic guidance with deep engineering expertise to help organizations align AI to real business goals, build practical solutions, and operationalize machine learning with strong governance, security, and MLOps—so AI scales responsibly, differentiates the business, and continues to deliver value over time. Purpose Built AI & Machine Learning Consulting Frequently Asked Questions What is Purpose Built AI & Machine Learning? Purpose Built AI & Machine Learning focuses on creating AI solutions designed for specific business outcomes—not generic experimentation. It combines AI strategy, practical AI solutions, and production‑ready machine learning models to deliver measurable value with strong governance and lifecycle management. How is purpose‑built AI different from off‑the‑shelf or commodity AI? Commodity AI helps improve baseline productivity, but purpose‑built AI is designed around your data, workflows, and business goals. Purpose‑built solutions integrate directly into business processes, differentiate your organization, and deliver outcomes that competitors can’t easily replicate. Why is an AI strategy and value roadmap important? An AI strategy and value roadmap ensures AI investments are aligned to business priorities and measurable outcomes. Without a clear roadmap, organizations risk fragmented pilots, unclear ROI, and AI initiatives that never scale beyond experimentation. What types of AI solutions fall under Purpose Built AI & ML? Purpose Built AI & ML includes generative AI knowledge assistants (RAG), natural language solutions, predictive machine learning models, detection and anomaly models, and production‑ready AI systems supported by robust MLOps and governance. How does MLOps support long‑term success with AI and machine learning? MLOps ensures machine learning models remain reliable, secure, and effective after deployment. It supports monitoring, retraining, versioning, and governance—allowing AI solutions to scale responsibly, adapt to change, and continue delivering value over time. Case Studies 01 Building a User ID Agent to Modernize Client Services 02 Data Platform Assessment Creates a Clear Path to Modernization 03 Turning Sales Order Backlog into Actionable Business Insight 04 Preserving Critical Engineering Knowledge with an AI Assistant 05 Creating a Trusted Sales Data Foundation with Microsoft Fabric 06 Building AI Into the Flow of Service Operations 01 Building a User ID Agent to Modernize Client Services How a Global Data and Analytics Organization Used AI to Automate User Access Management and Create a Foundation for Service Operations Transformation View Details 02 Data Platform Assessment Creates a Clear Path to Modernization How a Manufacturing Organization Used a Focused Assessment to Identify Reporting Risks, Select a Future-State Architecture, and Define Its Next Investment View Details 03 Turning Sales Order Backlog into Actionable Business Insight How a Global Manufacturer Is Using Microsoft Fabric and Power BI to Improve Shipment Readiness, Backlog Visibility, and Business Decision-Making View Details 04 Preserving Critical Engineering Knowledge with an AI Assistant How a Specialized Manufacturer Is Turning Institutional Expertise into a Governed, Searchable Engineering Resource View Details 05 Creating a Trusted Sales Data Foundation with Microsoft Fabric How a Manufacturing Organization Is Replacing Duplicated Reporting Logic with a Governed, Scalable Analytics Platform View Details 06 Building AI Into the Flow of Service Operations How a Global Data and Analytics Organization Used Microsoft AI to Modernize IT Service Management View Details Previous Next Blog Azure OpenAI, Data & AI AI Governance Framework: Responsible AI & Model-Risk Management for Enterprises August 18, 2026 Brian Haydin Azure OpenAI, Data & AI RAG Is the Enterprise AI Starting Point, Not the Finish Line July 28, 2026 Brian Haydin Azure OpenAI, Data & AI From Pilot to Production: How Enterprises Scale Generative AI July 16, 2026 Brian Haydin
