Azure AI Consulting

Azure AI Foundry: A complete enterprise AI offering from Microsoft with 10,000+ foundation and open models to build secure, scalable, and responsible AI solutions.
AI for voice, text, image and video: We help you implement voice, text, image, and video AI across workflows - from audio-driven support to image-based diagnostics and video summarization.
Do not miss the mark: Most enterprise AI efforts stall due to poor alignment, lack of governance, and disjointed execution - our strategic approach bridges technical delivery with real business priorities to ensure results.
Multi-agent systems at scale: We design multi-agent systems that manage complex tasks across business functions to solve complex challenges autonomously.
Azure AI, end-to-end: From Azure OpenAI and Prompt Flow to Azure AI Search, and Azure Cosmos DB - our solutions are built on the core of the Azure AI Foundry stack.
Microsoft and Cazton: We work closely with OpenAI, Azure OpenAI and many other Microsoft teams. Thanks to Microsoft for providing us with very early access to critical technologies. We are fortunate to have been working on GPT-3 since 2020, a couple years before ChatGPT was launched.
Top clients: We help Fortune 500, large, mid-size and startup companies with Big Data and AI development, deployment (MLOps), consulting, recruiting services and hands-on training services. Our clients include Microsoft, Google, Broadcom, Thomson Reuters, Bank of America, Macquarie, Dell and more.

Are you watching competitors gain market advantage through AI while your organization struggles to move beyond pilot projects? What if your AI initiatives could deliver the same measurable results as your most successful digital transformation projects?

Your organization is under pressure to implement AI solutions that drives real business value. Yet despite massive investments, 70-85% of enterprise AI projects fail to deliver meaningful results. The gap between AI promise and performance isn't about technology limitations - it's about implementation expertise.

Are you facing mounting pressure from stakeholders who see AI success stories in the news but wonder why your organization's AI initiatives aren't delivering similar results? What if you could transform that pressure into a competitive advantage through strategic AI implementation?

At Cazton, we bridge this gap. We help enterprise leaders transform AI potential into measurable business outcomes through strategic implementation of platforms like Azure AI Foundry. Our approach combines deep technical expertise with business strategy understanding to deliver AI solutions that actually work in your environment.

Azure AI is Microsoft's comprehensive suite of AI services like Azure OpenAI, Azure AI Services, Azure AI Search, Azure Machine Learning, and Azure AI Foundry - designed for enterprise use. It provides pre-built AI models, development tools, and infrastructure that organizations can use to build intelligent applications without starting from scratch.

The platform includes capabilities for natural language processing, computer vision, speech recognition, and machine learning. These services integrate seamlessly with existing Microsoft tools and can be customized for specific business requirements.

Azure AI Foundry, formerly Azure AI Studio, is Microsoft's unified platform for building, deploying, and managing AI solutions at enterprise scale. Think of it as your organization's AI operating system - a single environment that handles the entire AI lifecycle from development through production management.

The platform consolidates what previously required multiple tools and services into one integrated environment. This eliminates the complexity of connecting disparate AI tools while providing the governance and monitoring capabilities that enterprise deployments require.

Azure AI Foundry is designed specifically for organizations that need to move beyond pilot projects to production-scale AI solutions that handle real business processes and deliver measurable outcomes.

AI Foundry development environment: A comprehensive workspace where teams can build, test, and refine AI models using both pre-built solutions and custom approaches. The environment supports collaborative development while maintaining version control and project management capabilities.
Model catalog and management: Access a comprehensive catalog of 10,000+ AI models, including Microsoft's proprietary AI models alongside leading creators such as OpenAI, DeepSeek, Mistral, Meta, xAI, Cohere, and others. The catalog includes models optimized for different use cases, with clear guidance on selection criteria and implementation approaches.
Machine learning integration: Supports custom model development with built-in training pipelines, fine-tuning workflows, experiment tracking, and version control. Allows enterprises to iterate quickly while maintaining model lineage and reproducibility.
MLOps and production management: Enterprise-grade tools for deploying AI models into production environments, monitoring performance, and managing updates. These capabilities ensure AI solutions maintain reliability and accuracy as they scale.
Security and compliance framework: Built-in security controls, data governance tools, and compliance monitoring that meet enterprise requirements for handling sensitive information and regulatory oversight.
Observability: Provides end-to-end visibility into your AI workflows through Azure Monitor, Prompt Flow, and built-in dashboards - enabling teams to debug prompts, monitor agent behavior, and measure performance across environments.
Trustworthy & responsible AI: Delivers built-in capabilities for content moderation, bias detection, and explainability. Supports secure RAG pipelines and ensures AI applications align with ethical and regulatory guidelines.
Integration capabilities: Seamless connectivity with existing Microsoft ecosystem tools including Office 365, Teams, Power Platform, and Azure services. The platform also supports integration with non-Microsoft systems through comprehensive API frameworks.
SDKs & API references: Offers robust developer support with unified SDKs across .NET, Python, and JavaScript, allowing teams to build, deploy, and manage AI workloads using familiar languages and frameworks.

AI’s transformative promise is clear - but so are the challenges standing in the way of successful enterprise adoption. Despite high interest from executive leadership, very few organizations have been able to scale AI beyond isolated use cases. The reasons aren’t just technical - they’re strategic, organizational, and systemic.

Here are a few most common and critical challenges:

Unclear business value and lack of strategic alignment: Many AI initiatives lack clear business objectives or fail to align with broader organizational strategy, making it difficult to justify investment or measure success.
Poor data quality and insufficient data engineering: Organizations often struggle with dirty, incomplete, or inconsistent data. Data engineers are essential for cleaning and preparing data, but their work is undervalued, and there is often a shortage of skilled professionals.
Insufficient governance and regulatory readiness: Beyond security and compliance, organizations face challenges in establishing robust AI governance frameworks that address ethical use, accountability, and model explainability.
Security & compliance risks: 37% of enterprises cite regulatory and data privacy concerns as the biggest obstacles to AI adoption. Managing sensitive data responsibly while complying with industry regulations is non-negotiable, especially in sectors like finance, healthcare, and government.
Data fragmentation & silos: Nearly 90% of IT leaders report struggling with data silos. AI systems require holistic, accurate, and well-integrated data - but legacy systems and fragmented data architectures often block access to critical insights.
Integration issues with legacy systems: Many organizations are hindered by legacy IT systems that are difficult to integrate with modern AI solutions, creating bottlenecks and inefficiencies.
Unrealistic ROI expectations: 68% of CIOs face pressure to deliver immediate returns on AI investments, even though AI maturity takes time. This disconnect can lead to rushed decisions, underfunded initiatives, and missed long-term opportunities.
Talent & expertise gaps: The shortage of skilled AI professionals forces many organizations to either overextend internal teams or pause initiatives entirely. Meanwhile, governance complexities - around ethical AI use, accountability, and model transparency - compound the risk of missteps.

Successfully scaling AI requires more than deploying models - it demands a cohesive strategy that aligns technology with business priorities, modernizes data foundations, and prepares the organization for change. At Cazton, we help enterprises move beyond isolated pilots by designing secure, scalable AI architectures, unifying fragmented data, and embedding governance and adoption strategies from day one. Whether you're modernizing legacy systems, improving operational efficiency, or enabling intelligent decision-making, we bring the technical depth and strategic clarity needed to turn AI from a proof of concept into a competitive advantage.

Enterprise challenges don’t come in a single format. A single customer interaction might involve an email, a voicemail, and a photo - all of which need to be understood together. Multimodal AI enables systems to process and reason across text, audio, image, and video simultaneously, delivering a more complete and context-aware understanding.

We can help organizations build AI applications that listen, read, see, and interpret in real time. Whether it's streamlining insurance claims with scanned documents and phone calls, powering voice-activated diagnostics in the field, or analyzing training videos for safety risks - multimodal AI is already driving meaningful results across industries.

How Each Mode Delivers Value

Text: The foundation of most enterprise AI. Enables understanding of emails, documents, chats, and reports for tasks like summarization, classification, search and more.
Audio: Converts voice into actionable data through transcription, emotion detection, and speaker recognition. Key to enhancing support centers and hands-free workflows.
Image: Powers object detection, document processing, and visual QA - ideal for manufacturing, healthcare, and field inspections.
Video: Combines movement, visuals, and sound to detect anomalies, summarize meetings, or monitor compliance in real time.

Financial Services: Customer Service Intelligence and Risk Detection

Challenge: A regional credit union was under pressure to modernize its member services as larger competitors offered always-on, intelligent support experiences. Inquiry response times were delayed by manual triage, loan processing depended on document-heavy workflows, and fraud detection systems triggered too many false positives without identifying emerging threats. The organization faced operational limitations from a legacy core platform and a lean workforce unable to scale traditional methods.
Solution: We built a secure, AI-powered member service ecosystem by bridging legacy systems to enable natural language access to profiles and transaction data. A real-time dashboard surfaced conversational insights into account activity, loan status, and case histories. Lending documents were vectorized for intelligent analysis and contextual risk evaluation. Live event processing supported proactive fraud monitoring, while memory-aware AI workflows helped staff navigate multi-step tasks with greater efficiency. All components were designed to meet regulatory standards, ensuring traceability and secure data handling across interactions.
Business impact: Member services became faster and more responsive, with real-time insights accelerating decisions in loan processing and fraud detection. Staff were equipped with context-rich information, reducing manual overhead and improving service accuracy. The organization elevated its digital experience without overhauling core systems, enhancing compliance, boosting member retention, and attracting users seeking more personalized, technology-driven banking.
Tech stack: Azure AI Foundry, .NET APIs for COBOL system integration, Angular dashboard, Azure Cosmos DB for document vector storage, OpenAI integration, Azure AI Search, Semantic Kernel, SQL Server, Microsoft Fabric, PyTorch, Redis, Apache Kafka, compliance-aligned architecture (FFIEC, NCUA).

Manufacturing: Supply Chain Optimization and Quality Intelligence

Challenge: A precision electronics manufacturer struggled with recurring supply chain delays and inconsistent product quality. Global supplier variability, communication lags, and limited predictive analytics led to unanticipated inventory shortfalls and reactive procurement. Quality inspection processes relied on historical sampling methods that often missed systemic issues until customer complaints surfaced, impacting brand reliability.
Solution: We deployed a scalable intelligence platform that unified supplier, logistics, and production data through real-time event streaming and centralized data lakes. A graph-based model mapped complex sourcing relationships, enabling dynamic rerouting and contingency planning in response to disruptions. Advanced analytics processed IoT streams and inspection results to uncover defect trends linked to environmental and supplier factors. Plant managers and procurement teams accessed actionable insights through an intuitive dashboard with AI-enhanced natural language querying, facilitating exploration of supply chain risks and quality indicators.
Business impact: The manufacturer gained comprehensive, real-time visibility into supply chain performance and product quality, enabling proactive risk management and faster disruption response. Early detection of defect patterns reduced rework and improved supplier accountability. Collaboration across procurement, operations, and quality improved through data-driven insights and intuitive AI interfaces. Production schedules aligned more closely with material availability, optimizing inventory levels and reducing costly delays, all while preserving existing enterprise systems.
Tech stack: Databricks, Apache Spark, Apache Kafka, Microsoft Fabric, LangChain, MongoDB, React, Retrieval-Augmented Fine-Tuning, Azure OpenAI, Azure AI Search, graph-based sourcing engine.

Healthcare: Clinical Decision Support and Patient Care Coordination

Challenge: A regional hospital network was hampered by siloed systems, physician burnout, and inconsistent access to clinical insights. Doctors were spending excessive time navigating multiple applications during patient care, affecting efficiency and safety.
Solution: We developed a secure clinical decision support platform that unified patient data across siloed systems to provide real-time access to relevant medical information. Clinical notes, imaging reports, and historical records were correlated and made easily searchable via a mobile interface tailored for physicians. The system also incorporated real-time transcription and natural language processing to extract key clinical signals from physician interactions, enabling faster and more accurate decision-making.
Business impact: Clinical workflows were significantly streamlined, allowing physicians to dedicate more time to patient care rather than administrative tasks. Access to contextual, real-time insights improved decision quality and patient coordination, reducing errors and enhancing safety. Physician satisfaction increased as the burden of navigating multiple systems was alleviated, contributing to better overall care delivery.
Tech stack: Azure OpenAI, HL7/FHIR-compliant data standards, .NET 9 APIs, Angular, native iOS app, PostgreSQL, Azure AI Search, Voice AI Pro, Azure Cosmos DB for MongoDB (vCore), PyTorch.

Banking: Fraud Prevention and Customer Experience

Challenge: A community-focused bank was challenged by increasing fraud sophistication and customer dissatisfaction with blocked transactions. Existing rules-based systems flagged many legitimate activities while failing to detect subtle fraudulent patterns. Investigation teams were overburdened by manual workflows, leading to slow case resolution and poor customer communication.
Solution: We developed a real-time fraud detection and investigation platform that processed transaction streams to identify suspicious patterns without impacting performance. The system linked risk assessments directly to customer-facing portals, providing immediate, contextual feedback. Analysts accessed a conversational interface that summarized cases, highlighted relevant patterns, and suggested recommended actions, streamlining workflows and enabling faster, more informed decision-making.
Business impact: The institution enhanced fraud detection accuracy and investigation speed, reducing case resolution times significantly. Staff were better equipped to communicate decisions clearly, improving customer trust and satisfaction. The platform’s AI-driven insights enabled the bank to manage increasing fraud threats effectively without scaling the fraud team, delivering a more transparent and secure experience for customers.
Tech stack: Azure AI Foundry, Azure Event Hubs, OpenAI, Java microservices, React, Apache Kafka, Elasticsearch, LangChain, Redis.

Education: Student Success Analytics and Administrative Efficiency

Challenge: A mid-sized university lacked cohesive visibility into student performance, making it difficult to identify at-risk learners and coordinate timely interventions. Academic data was fragmented across learning platforms, financial aid systems, and student services, making support reactive and labor-intensive. Staff faced growing demands without the tools to personalize outreach at scale.
Solution: We delivered a scalable, containerized student success platform (web, mobile, and desktop) that securely unified academic and engagement data from diverse institutional sources. Advanced search capabilities enabled fast, contextual retrieval of student information to support timely interventions. Real-time data caching and in-memory computing optimized performance and responsiveness across large datasets. Students accessed personalized guidance and alerts via a flexible mobile and web app experience, while advisors received AI-driven summaries and recommendations. The platform leveraged orchestration tools to ensure reliable deployment, scaling, and compliance with privacy regulations.
Business impact: The institution achieved more proactive student engagement, improving academic outcomes while significantly reducing administrative overhead. Fast, contextual data access empowered advisors to deliver timely, personalized support. Students benefited from seamless, multi-channel access to insights and resources. The modular, cloud-native platform simplified integration and scaling, future-proofing operations while safeguarding sensitive information and maintaining regulatory compliance.
Tech stack: Azure AI Foundry, Azure Cosmos DB for Apache Cassandra, Node.js APIs for LMS/SIS integration, FERPA-compliant infrastructure, Ionic (iOS & Android), Progressive Web Apps (PWA), Electron.js, Solr and Fusion for enterprise search, Docker, Kubernetes, Terraform, LangChain, Retrieval-Augmented Fine-Tuning.

What if your AI systems could work together like a highly coordinated team, each specializing in different aspects of your business while sharing insights and coordinating actions automatically? This is the power of AI agents and multi-agent systems - the next evolution in enterprise AI that transforms how organizations automate complex workflows and decision-making processes.

Multi-Agent architecture for Enterprise Solutions

Understanding AI agents: Think of AI agents as intelligent software entities that can perceive their environment, make decisions, and take actions to achieve specific goals. Unlike traditional AI models that simply respond to queries, agents can initiate actions, maintain context across multiple interactions, and coordinate with other systems or agents to accomplish complex tasks.

Multi-agent orchestration: Multi-agent systems take this concept further by enabling multiple specialized agents to work together. Imagine a customer service ecosystem where one agent handles initial inquiry classification, another accesses customer history and context, a third agent analyzes sentiment and urgency, and a fourth coordinates with fulfillment systems - all working together seamlessly to provide superior customer experiences.

Azure AI Foundry agent capabilities: The platform provides the infrastructure needed to build, deploy, and manage both individual agents and multi-agent systems at enterprise scale. This includes agent orchestration frameworks, secure communication protocols between agents, and monitoring capabilities that ensure agent coordination remains effective as systems scale.

How Cazton enables agent success: Our expertise lies in designing agent architectures that align with your specific business processes and organizational structure. We help you identify which business functions benefit most from agent automation, design communication protocols between agents, and implement governance frameworks that ensure agent decisions align with business objectives.

The future of enterprise AI lies in intelligent coordination between specialized agents that understand your business context and can adapt to changing requirements. Our approach ensures your organization is positioned to leverage these advanced capabilities as they become essential for competitive advantage.

Building successful AI solutions requires more than just access to powerful models - it demands the right strategy, customization, and execution. We help enterprises design AI systems that are not only accurate and secure but also aligned with their unique business objectives. Whether the goal is to reduce hallucinations, improve precision and recall, or ensure regulatory compliance, our approach is tailored to solve real problems, not just check technical boxes.

Security and trust are foundational to every solution we build. We implement strict access controls, embed responsible AI practices, and integrate seamlessly with both modern and legacy tech stacks to ensure smooth adoption. From fine-tuning models to optimizing data pipelines and performance, we enable scalable AI applications that are enterprise-ready from day one.

Calling a language model API is easy. Building a robust, enterprise-grade AI system is not. At Cazton, we specialize in designing and deploying production-ready solutions that go far beyond simple API integrations.

Achieving success with Azure AI requires more than just model access - it takes a sophisticated toolkit: advanced prompting strategies for high-precision outputs, agentic frameworks to orchestrate complex tasks, evals to monitor and benchmark performance, guardrails to ensure safety and compliance, and retrieval-augmented generation (RAG) architectures that root responses in trusted, verifiable data.

We bring deep experience across modalities and systems - implementing full voice technology stacks, building asynchronous pipelines, handling data extraction and embeddings, and integrating with vector databases. Our team has worked with GraphDBs in tandem with LLMs, developed intelligent browser- and OS-level agents, leveraged the model-context protocol (MCP), and engineered reasoning models for intricate decision logic.

We were the first company in the world to implement evals on GPT‑4 even before OpenAI. That innovative mindset continues to define us. Whether you're building intelligent agents, automating business-critical workflows, or launching multimodal AI applications, we provide more than just technical tools - we deliver the strategy, architecture, and execution expertise to make it all real.

End-to-end AI lifecycle support: From initial strategy and planning to architecture, development, deployment, and enterprise scaling - we support every stage of your AI journey.
Tailored AI across platforms: We build and enhance intelligent applications for Web, iOS, Android, Windows, and desktop (Electron.js) environments - infused with real-time AI capabilities.
Versatile tech stack alignment: Our solutions leverage technologies like OpenAI, Azure OpenAI, Azure Cosmos DB, MongoDB, Azure AI Search, Spark, Kafka, Hadoop, Redis, Ignite, Semantic Kernel, LangChain, LlamaIndex, Microsoft Fabric, PyTorch, TensorFlow, Stable Diffusion, Keras, Scikit-learn, Microsoft Cognitive Toolkit, PineCone, Qdrant, FAISS, ChromaDB, Weaviate, Theano, Caffe, Torch, and/or others - integrated to fit your team's preferred tools and skills.
Embedded AI best practices: We implement proven development and MLOps strategies directly into your teams’ workflows - boosting model performance while improving long-term maintainability.
Enterprise-scale optimization: We fine-tune both AI models and infrastructure to maximize responsiveness, throughput, and reliability across high-traffic environments.
Prototype to production acceleration: Our team rapidly delivers proof-of-concepts to test new ideas, validate feasibility, and gain internal buy-in - reducing risk and speeding up innovation cycles.
Legacy system compatibility: We connect modern Azure AI capabilities to existing enterprise systems - enabling AI transformation without requiring a full infrastructure overhaul.
Security, compliance & risk controls: We enforce strong data governance, integrate privacy protocols, and align your solutions with industry standards like HIPAA, GDPR, and ISO.
Operational monitoring & continuous tuning: Post deployment, we provide tools and support for real-time monitoring, usage analytics, and continuous improvement of AI systems.
Upskilling & organizational enablement: Through training, workshops, and change management initiatives, we help teams build confidence and maturity in working with Azure AI tools.
Actionable analytics & insights delivery: We build dashboards and real-time reporting layers that translate complex model outputs into simple, business-friendly insights.
Strategic AI planning & innovation: We assist with long-term AI roadmaps, ROI analysis, capability assessments, and readiness planning to keep your organization ahead of the curve.

Partnering with Cazton means working with a team that’s focused on delivering meaningful business outcomes through AI. We take a holistic approach - considering your goals, systems, teams, and industry requirements - to ensure every solution is both effective and aligned to your enterprise needs. From strategy to execution, our priority is helping you realize the full value of AI in a way that fits your organization, scales with your vision, and drives measurable impact.

Cazton is composed of technical professionals with expertise gained all over the world and in all fields of the tech industry and we put this expertise to work for you. We serve all industries, including banking, finance, legal services, life sciences & healthcare, technology, media, and the public sector. Check out some of our services:

Cazton has expanded into a global company, servicing clients not only across the United States, but in Oslo, Norway; Stockholm, Sweden; London, England; Berlin, Germany; Frankfurt, Germany; Paris, France; Amsterdam, Netherlands; Brussels, Belgium; Rome, Italy; Sydney, Melbourne, Australia; Quebec City, Toronto Vancouver, Montreal, Ottawa, Calgary, Edmonton, Victoria, and Winnipeg as well. In the United States, we provide our consulting and training services across various cities like Austin, Dallas, Houston, New York, New Jersey, Irvine, Los Angeles, Denver, Boulder, Charlotte, Atlanta, Orlando, Miami, San Antonio, San Diego, San Francisco, San Jose, Stamford and others. Contact us today to learn more about what our experts can do for you.