Overview

A US-based manufacturing organization with operations across the globe wanted to scale AI initiatives across business units while ensuring governance, security, and measurable ROI. While multiple teams were experimenting with AI, the leadership team identified key gaps: lack of a centralized operating model, inconsistent prioritization of AI use cases, and unclear adoption of roadmap.

To address this, we worked with the customer to establish an AI Center of Excellence (CoE), showcased AI use cases across manufacturing operations and corporate functions, identified and prioritized high-impact AI use cases, explored Microsoft technology offerings for AI platform, and rapidly prototyped a high-value AI solution using Microsoft Common Envisioning Framework and Azure AI best practices.

Challenge

Various teams within the organization such as Plant Operations, Supply Chain, Quality, Procurement, Engineering, Legal, Accounting, and Human Resources were interested in adopting AI but struggled with turning ambition into actionable enterprise execution.

Key challenges included:

·       Decentralized AI initiatives exist across departments due to the absence of centralized governance

·    No standard approach to evaluate use cases based on value, feasibility, and risk

·    Uncertainty about where to start and how to create a phased adoption roadmap

·    Difficulty aligning AI adoption with data readiness, security, compliance, and the operating model

·    Pressure to show results quickly through a working proof of value

Approach

To move forward, customer partnered with MAQ Software to deliver a structured engagement aligned to Microsoft’s Common Envisioning Framework and enterprise AI architecture standards.

1. Showcasing AI Potential in Manufacturing

   To help the customer accelerate their AI journey, we partnered with their teams to demonstrate how agentic AI could drive measurable value across the manufacturing value chain. Through this engagement, the customer expanded its view of AI opportunities across Finance, Supply Chain, HR, and IT, and identified several high-impact use cases, including:
   • Document intelligence use cases
   • Invoice processing
   • Intelligent processing of SOPs, work orders, compliance documentation
   • Quality inspection and defect detection
   • Procurement automation (Purchase Order, Goods Receipt Note, and invoice matching)
   • Supply planning and demand forecasting



2. Defining an AI CoE Strategy & Operating Model

   To move from experimentation to enterprise-scale execution, we worked closely with the customer to define a scalable AI Center of Excellence (CoE) strategy and operating model. This included:
   • A practical AI governance framework with defined decision rights, intake process, and approval checkpoints
   • Clearly defined CoE roles spanning business, IT, security, data, and AI teams, along with manufacturing engineering and plant operations stakeholders
   • An adoption strategy with success metrics tied to business outcomes
   • Executive alignment on prioritization principles and ways of working



3. Business Envisioning Workshops

   We facilitated collaborative envisioning workshops with business and technology leaders to align AI initiatives with the customer’s strategic priorities. These sessions enabled the customer to:
   • Identify priority scenarios across business functions
   • Define measurable outcomes and key value drivers
   • Establish success criteria and clarify scope for delivery



4. AI Use Case Scorecard & Prioritization

   To ensure early investment in the right initiatives, we helped build an AI use case inventory and applied a structured AI Patterns Scorecard to evaluate opportunities based on:
   • Expected business value
   • Implementation feasibility and data readiness
   • Risk, compliance, and operational dependencies

   This structured approach enabled leadership to shift from a broad set of ideas to a focused set of validated use cases, creating a clear roadmap for phased rollout.




5. Capability Envisioning

   To support long-term success, we assessed organizational and technical readiness across:
   • Data and platform maturity
   • Security and governance controls
   • Operating processes required for scalable AI delivery

   The engagement resulted in a high-level target architecture aligned to Azure AI services, supported by readiness gap analysis and implementation recommendations.




6. Rapid Prototyping for Quick Validation

   To build confidence and accelerate decision-making, we partnered with the customer to rapidly prototype a prioritized AI use case within one to two weeks. This helped validate:
   • User experience and adoption potential
   • Expected impact on business metrics
   • Integration feasibility with enterprise systems

Results

By the end of the engagement, the customer had a clear enterprise foundation for scaling AI initiatives.

Key outcomes included:

·       A formal AI CoE charter and operating model with governance and role clarity

·       An enterprise AI backlog with a prioritized inventory of use cases

·       A crawl, walk, run adoption roadmap aligned with business priorities

·       An Azure-aligned AI architecture blueprint designed for scalable deployment

·       A working prototype demonstrating measurable value and enabling leadership buy-in

Ready to scale with AI?

At MAQ Software, our mission is simple: scale AI for every developer and every customer. We partner with enterprises across retail, technology, manufacturing, and beyond to implement AI-first strategies to drive measurable business outcomes.

If your organization is facing similar challenges in scaling AI, schedule an AI CoE and Solution Envisioning Workshop with us today or contact CustomerSuccess@MAQSoftware.com.

Accelerating software development with agentic AI solutions

 

Engineering teams are expected to modernize platforms faster while maintaining quality, security, and governance. Agentic AI supports end-to-end SDLC workflows—automating code analysis, migration, testing, documentation, and production support.

Below, we highlight scenarios where intelligent agents solve business challenges across software engineering.

1. AI Agent for Developer Support

Developers faced slow response times due to manual troubleshooting. We built an AI agent that retrieves documentation, inspects code, and flags bugs based on support requests. This reduced resolution time and standardized troubleshooting.

2. SQL Performance Optimization Assistant

Performance issues were common due to inefficient queries. We developed an AI agent that reviews SQL code and rewrites it for better performance. This improved query speed and reduced compute usage.

3. Automated Code Migration Tool

Migration from legacy apps (WPF) to modern web architectures (Angular) was slow and error-prone. Our AI-powered migration tool preserved application structure and accelerated timelines by minimizing manual rework.

4. Test Case Generation Agent

QA cycles were delayed due to manual test design. We implemented an agent that generates test cases from requirements and code changes, and links them to user stories. This improved coverage and reduced test planning time.

5. PR Review & Coding Standards Agent

Code reviews were inconsistent across teams. We built an agent that evaluates PRs, flags style violations, detects anti-patterns, and drafts review comments. This improved code quality and reduced review effort.

6. Release Notes & Documentation Agent

Documentation became outdated quickly. We developed an agent that generates release notes, updates wiki pages, and maintains API documentation automatically based on code diffs. This improved adoption and reduced support escalations.

7. Responsible AI Prompting Assistant

Teams needed safeguards against unsafe prompts. We created an agent that rewrites unsafe or biased prompts to align with responsible AI standards. This improved safety and governance.

Ready to lead with AI?

These agentic AI solutions represent a few of our innovations driving impact. With rapid advancements in AI and evolving industry needs, we continue to find new opportunities to adapt our solutions to drive customer success.

At MAQ Software, we help engineering organizations accelerate modern software delivery using agentic AI—without compromising security and quality.

Contact CustomerSuccess@MAQSoftware.com to explore how AI can transform your business.

Reinventing legal operations with agentic AI solutions

 

Legal teams are expected to move faster while handling growing volumes of contracts, regulatory requirements, and litigation risk. Agentic AI helps legal organizations reduce manual workload by automating review, summarization, research, and intake workflows—while ensuring confidentiality and traceability.

Below, we highlight scenarios where intelligent agents solve business challenges in legal operations and compliance.

1. Contract Review Agent

Reviewing legacy contracts against current standards was time-consuming. Our AI agent compares contracts clause-by-clause, highlights risky terms, and produces a structured summary. This reduced review time from days to hours and accelerated negotiations.

2. Legal Intake & Triage Agent

Legal teams struggled with scattered requests across emails and chats. We built an agent that standardizes intake, identifies request type, routes it to the right team, and drafts first-pass responses. This improved turnaround time and reduced triage overhead.

3. Regulatory Change Monitoring Agent

Teams spent time tracking changes across multiple regulatory sources. We implemented an agent that monitors regulatory updates, summarizes the impact, and alerts stakeholders with recommended actions. This improved compliance responsiveness.

4. Outside Counsel Spend Optimization Agent

Legal spend lacked transparency and frequently exceeded budget. We built an agent that analyzes invoices, flags anomalies, and recommends cost controls. This improved spend governance and vendor accountability.

5. Document Discovery Assistant

Case teams struggled to sift through large document sets. Our agent indexes documents, extracts key entities, and answers questions with citation-backed responses. This accelerated discovery and improved case preparation.

6. Employment Law Agent

Recurring internal legal questions created bottlenecks. We deployed an AI assistant that searches past cases, retrieves relevant policies, and drafts a first-pass answer. This improved responsiveness while keeping legal oversight.

7. Legal Knowledge Base Bot

Employees struggled to locate policy guidance quickly. We created an agent that retrieves answers from approved documents and archives while protecting sensitive data. This improved legal self-service and reduced repetitive queries.

Ready to lead with AI?

These agentic AI solutions represent a few of our innovations driving impact. With rapid advancements in AI and evolving industry needs, we continue to find new opportunities to adapt our solutions to drive customer success.

At MAQ Software, we help legal organizations adopt agentic AI securely—accelerating contract workflows, compliance processes, and legal support operations.

Contact CustomerSuccess@MAQSoftware.com to explore how AI can transform your business.

Transforming finance with agentic AI solutions

 

Financial organizations face growing pressure to reduce operational overhead, strengthen compliance, and accelerate decisions. Agentic AI helps finance teams move beyond dashboards and manual workflows by enabling automated execution—backed by governance and audit trails.

Below, we highlight scenarios where intelligent agents solve business challenges across FP&A (financial planning & analysis), accounting, risk, and compliance.

1. Invoice Processing Tool

Manual invoice entry slowed down finance operations and increased errors. Our AI tool extracts key data from invoice PDFs, validates it against vendor master data, and stores results with an audit trail. This reduced processing time and improved compliance readiness.

2. FP&A Forecasting Agent

Forecasting cycles were slow due to spreadsheet-based workflows. We built an agent that monitors actuals, identifies variance drivers, and updates rolling forecasts automatically. This improved forecast agility and reduced cycle time.

3. Expense Anomaly Detection Agent

Finance teams struggled to detect irregular spend in time. We deployed an agent that monitors expenses, flags anomalies, and creates exception reports with recommended next steps. This improved control and reduced leakage.

4. Close Acceleration Agent

Month-end close required repetitive reconciliation efforts across systems. We developed an AI agent that compares ledgers, identifies mismatches, and generates reconciliation summaries. This shortened close timelines and reduced manual workload.

5. Policy Compliance Agent

Policy checks were manual and inconsistent across business units. We created an agent that validates expense claims against policy rules and highlights missing evidence. This reduced violations and improved audit outcomes.

Ready to lead with AI?

Our finance-focused agentic AI solutions represent a few of our innovations driving impact. With rapid advancements in AI and evolving industry needs, we continue to find new opportunities to adapt our solutions to drive customer success.

At MAQ Software, we help retailers accelerate adoption of agentic AI—improving customer experience, inventory outcomes, and operational efficiency.

Contact CustomerSuccess@MAQSoftware.com to explore how AI can transform your retail business.

Reshaping retail with Agentic AI solutions

 

Across the retail industry, organizations are using AI not just for analytics but for real-time execution—automating decisions across merchandising, pricing, operations, and customer engagement. At MAQ Software, we help retail leaders implement agentic AI solutions that act, reduce manual effort, and improve speed-to-market.

Below, we highlight real-world scenarios where intelligent agents solve business challenges across omnichannel retail operations.

1. Demand Forecasing Agent

Forecasting teams struggled with lagging indicators and fragmented data. We built an AI agent that consolidates historical sales, promotions, seasonality, and external signals to generate rolling forecasts. This improved forecast accuracy and reduced stockouts and excess inventory.

2. Dynamic Pricing Agent

Pricing updates were slow due to manual approvals and spreadsheet-driven workflows. We implemented an agent that monitors competitor pricing, inventory levels, and conversion rates to recommend pricing changes and trigger approval workflows. This increased margin while maintaining competitiveness.

3. Targeting Agent

Marketing teams often struggled to filter and select the right leads for campaigns. We created a natural language agent integrated with Teams and Microsoft 365 Copilot that allows users to define audiences and apply filters using everyday language. This significantly reduced targeting time and manual errors.

4. Customer Support Concierge (Teams + Copilot)

Customer service teams spent time searching for multiple sources for order and return details. We deployed an agent integrated with Teams that retrieves customer history, order status, and policies instantly. This reduced average handling time and improved customer satisfaction.

5. Product Content Generator

Merchandising teams struggled to keep product descriptions consistent across channels. We implemented an AI agent that generates SEO-ready descriptions, attributes, and bullet points from vendor feeds and images. This accelerated catalog readiness and improved search conversions.

6. Demo Video Generator

A client’s content team faced delays in creating demo videos. We implemented an AI tool that converts written scripts into high-quality, narrated videos. This sped up the production process and improved adoption across their customer base.

7. Store Operations Assistant

Store managers spent significant time on manual reporting and compliance checklists. We developed an AI assistant that summarizes store KPIs daily and flags anomalies (shrink, footfall, staffing gaps). This improved operational consistency across stores.

Ready to lead with AI?

Our retail-focused agentic AI solutions represent a few of our innovations driving impact. With rapid advancements in AI and evolving industry needs, we continue to find new opportunities to adapt our solutions to drive customer success.

At MAQ Software, we help retailers accelerate adoption of agentic AI—improving customer experience, inventory outcomes, and operational efficiency.

Contact CustomerSuccess@MAQSoftware.com to explore how AI can transform your retail business.

Overview

Generative AI is rapidly moving from experimentation to enterprise-scale adoption. However, the challenge is not model innovation but operational readiness. Organizations need secure, governed, and cost-effective systems to move generative AI into production.

Mosaic AI, developed by Databricks, is a comprehensive framework for building, deploying, and governing generative AI applications at enterprise scale. Natively integrated into the Databricks Intelligence Platform, Mosaic AI enables teams to operationalize machine learning and generative AI using a unified environment. It combines pre-built foundation models, flexible customization and deployment options, and built-in monitoring to accelerate time-to-market while maintaining control and compliance.

As organizations transition from isolated proofs of concept to production-grade AI systems, Mosaic AI provides essential capabilities such as secure model serving, version control, traffic management, cost-optimized inference, and end-to-end observability. These capabilities address common operational gaps and support reliable performance, cost visibility, and continuous model improvement.

Built-in components

·       Foundation & Open Models Catalog: Production-ready open and commercial models for text generation, summarization, embeddings, and reasoning—balancing quality, cost, and control.

·       Production-Grade Model Serving: Secure, scalable endpoints with autoscaling (including scale-to-zero to reduce idle inference costs), version management, traffic splitting, and CI/CD integration for safe rollouts.

·       AI Gateway for Governance & Control: Centralized layer for routing inference requests with usage tracking, rate limiting, and policy enforcement; requests and responses can be captured in Unity Catalog tables for audit and monitoring.

·       Unified Observability: Dashboards and logs capture latency, throughput, errors, and resource usage to enforce SLAs and accelerate troubleshooting.

·       Advanced AI Workflows: Fine-tuning, Retrieval-Augmented Generation (RAG), agent frameworks, fallback routing, model comparison, and built-in evaluation for quality, latency, and cost.

Visual overview

Model Catalog → Serving

[Model Catalog]
  |__ GPT / OSS / Claude / Llama / Gemma / GTE / BGE
           |
           v
   [Registered Model] —> [Serving Endpoint]
                          |  autoscale / versions / split traffic
                          v
                      [Production API]

Endpoint Deployment Pipeline

[Notebook or Pipeline]
      | register
      v
[Model Registry] —> [Create Endpoint]
      | compute: CPU/GPU  | scale-out | scale-to-zero
      v                   v
   [Tracing]           [Traffic Split]
      |                    |
      +----> [AI Gateway Policies] ----> [Prod]

AI Gateway Governance Flow

[Incoming Requests]
      |
   [AI Gateway]
   |   |-- Rate Limits
   |   |-- Access Rules
   |   |-- Usage Tracking
   |
   +--> [Inference Tables in Unity Catalog]
                 |
                 +--> [Dashboards / Audits / Monitoring]

End-to-End RAG / Agent Workflow

[User Query]
    |
[Retrieve Docs / Vector Search] --> [LLM Reasoning / Agent Steps]
    |                                   |-- tool calls
    |                                   |-- fallback routing
    +-------------------------------> [Final Answer with Citations]

Business outcomes

·       Faster time-to-market: Reduce time-to-market for AI-powered applications via pre-configured models and production-ready serving.

·       Security & Governance: Centralized policy control, audit trails, and monitoring aligned to enterprise standards.

·       Cost & Performance: Autoscaling, scale-to-zero, and traffic shaping ensure predictable spend and resilient performance.

·       Reliability: Fallback routing, HA patterns, and continuous evaluation to maintain SLAs and user trust.

·       Quality: Built-in evaluation and model comparison improve precision, tone, and task success over time.

Our approach to operationalizing Mosaic AI

MAQ Software helps enterprises move from AI experimentation to production by aligning Mosaic AI capabilities with measurable business outcomes.

·       Use case prioritization: Identify high-impact AI scenarios tied to business KPIs.

·       Architecture and governance design: Define secure, scalable architectures for Mosaic AI model serving and governance.

·       Production deployment: Implement versioned endpoints with traffic splitting, rollback support, and CI/CD integration.

·       Security and compliance instrumentation: Configure AI Gateway usage tracking, rate limits, and inference tables to support audit and compliance requirements.

·       Observability and reliability: Establish dashboards and service-level objectives (SLOs) for latency, error rates, and cost.

·       Advanced workflow enablement: Operationalize retrieval-augmented generation (RAG), agent-based workflows, and fine-tuning with built-in evaluation.

·       Continuous optimization: Create feedback loops across data, prompts, models, and governance policies to improve quality and efficiency over time.

Common enterprise use cases

·       Intelligent document processing and summarization.

·       Retrieval-augmented knowledge copilots for support and sales.

·       Natural-language analytics assistants over enterprise data.

·       Fraud detection and risk analysis with human-in-the-loop review.

·       AI-driven workflow automation using agentic orchestration.

What to monitor in production AI systems

Sustained AI performance requires continuous monitoring across the full system lifecycle. Organizations should track:

·       Performance and reliability: Latency percentiles, error rates, and service health across routes and model versions.

·       Cost and scalability: Request volume, concurrency patterns, autoscaling behavior, and inference cost drivers.

·       Model and prompt quality: Evaluation scores by task cohort to detect drift or degradation

·       Security and governance: Policy violations, rate limit events, and access trends captured through the AI Gateway.

·       Compliance readiness: Ongoing data retention, lineage, and governance checks to support audits.

Get started with Mosaic AI

Mosaic AI helps you move from experiments to secure, scalable, production-ready generative AI with governed model serving, integrated observability, and advanced workflows such as RAG and agents.

MAQ Software partners with enterprises to architect, deploy, and operationalize Mosaic AI solutions that deliver measurable business outcomes. Contact us at CustomerSuccess@MAQSoftware.com to get started today.

Unifying disconnected data systems using Microsoft Fabric and OneLake

Customer overview

The customer is a leading American healthcare company that manufactures and distributes medical supplies. Operating in over 125 countries, the organization supports healthcare providers worldwide with products and clinical solutions that improve patient care. Among over 40,000 employees, Data and Analytics teams build data tables and share insights across the organization.

The challenge

The Data and Analytics teams had been facing challenges typical of large, regulated enterprises, including data spread across ten different systems. Each platform required separate tools, licenses, and maintenance efforts.

This fragmented setup created several challenges:

·       High platform and licensing costs

·       Slow onboarding of new data sources

·       Inconsistent ranking results

·       No standardized process for building and sharing data products

·       Limited support for self‑service analytics, AI, and ML

To stay competitive, the organization needed a data platform that would allow them to efficiently access, integrate, and analyze data from multiple sources.

The solution

To modernize their data platform, the customer chose to implement Microsoft Fabric with OneLake as the unified data layer. As a Frontier AI organization with deep expertise in Fabric, we provided a combination of automation, architecture design, and engineering services to accelerate the implementation. We used a configuration-based data ingestion and job processing framework to streamline data onboarding across the Medallion architecture and reduced the number of notebooks and pipelines. We also used DevelopFAST, our AI-powered development tool, to automate key parts of the software development lifecycle. DevelopFAST generated user stories, technical design documents, and test cases from raw feature inputs. This reduced planning effort, improved code consistency, and shortened the time to first commit. Built-in AI functions, along with logging, auditing, and data quality checks, were all stored in OneLake.

Other services we provided included:

·       Designing the Fabric architecture and delivering proof-of-concept solutions

·       Developing data products using the Medallion architecture

·       Capacity planning and performance optimization reviews

·       Workshops, training sessions, and best practice recommendations to drive adoption

Figure 1: Solution architecture

Why OneLake?

The solution brought together data virtualization, data mesh architecture, centralized security, and desktop file explorer components within a single platform.

OneLake’s data virtualization technology enabled the organization to access ten disconnected systems, including cloud-hosted ERP platforms, on-prem systems, Oracle environments, and other databases. Using internal shortcuts in OneLake, teams could access source data directly without needing to move or replicate it.

Additionally, OneLake’s data mesh architecture functioned as a decentralized repository, enabling business domains to create and share their own data products across the organization.

OneSecurity allowed the organization to manage data access permissions with row-level, role-level, and object-level security. With OneLake, permissions could be defined in a single place, eliminating the need for defining security across multiple layers.

OneLake Desktop allowed teams to copy and move files locally as well as delete or rename Fabric items. This functionality enabled updating Fabric workspaces in a manner similar to a SharePoint drive integrated with Windows File Explorer.

Business impact

With Microsoft Fabric, the customer unified ten disconnected data sources into a single, secure platform with built-in AI capabilities.

The results were clear and measurable:

·       Overall data platform costs were reduced from retiring licenses such as WebFOCUS

·       Data onboarding time decreased by 40%

·       Improved data accessibility with OneLake data virtualization and shortcuts

·       Centralized security with OneSecurity

Future expansion

The organization continues to work with us to further modernize its analytics platform. Planned Data and Analytics initiatives include:

·       Fabric’s new mirrored database feature to bring data directly into OneLake

·       Enabling schema-level security using OneSecurity

·       Migrating SAP data to OneLake using Fabric connectors

·       Moving reports from Tableau to Power BI

These efforts will expand self-service capabilities and unlock AI-powered insights for users across the organization.

Interested in learning more?

As a Fabric Featured Partner and Frontier AI firm, MAQ Software helps enterprises unlock the full value of Microsoft Fabric. From implementing new data agents to optimizing existing platforms, we support organizations at every stage of their AI and analytics journey.

Contact us at CustomerSuccess@MAQSoftware.com or explore our apps or consulting services on Microsoft Azure Marketplace:

·             Microsoft Fabric: 2-Hour Briefing

·       Microsoft Fabric: 4-Week Assessment

·       Microsoft Fabric: Accelerated 8-Week Pilot Implementation

·       Real-time Intelligence using Microsoft Fabric: 1-Day Workshop