Four real Motion Consulting Group AI engagements.

The Challenge

Runaway AI spend across both proprietary applications and traditional Enterprise Data Warehouse environments — Azure compute, LLM API calls, token costs, data costs all compounding monthly. The Chief Data Office alone was spending tens of millions per year, with that figure projected to nearly double by end of 2026.

Compounding the cost problem: suboptimized queries were producing 30-second to 5-minute response times on common requests and 5–30 minutes on complex ones — slowing analyst workloads and inflating compute costs simultaneously. Every redundant LLM call was a double tax: dollars and time.

The CDO needed a governance layer that didn't just monitor cost but actively reduced it at the point of query — before it hit production infrastructure.

The MCG Approach

Built the Query Probe Agent — an Agentic AI application that intercepts each query at initial run request, optimizes it for speed and cost, and routes intelligently before any LLM or warehouse spend occurs.

Five-person W-2 consultant team: 1 AI Architect, 1 Technical Project Manager, 2 AI Engineers, 1 AI/Data Engineer. POC/MVP developed in Kelly Services' Azure Cloud infrastructure, then custom-coded to the carrier's proprietary stack for enterprise rollout.

Self-improving architecture: the agent ships with a self-updating knowledge base, vector database, metadata collection function, and self-learning feedback engine refined via human interaction. Semantic similarity check eliminates redundant LLM calls before they're made.

Governance by design: every query intercepted, optimized, and logged — producing the audit trail the CDO needed alongside the cost savings.

The Challenge

Outdated rules-based chatbot couldn't support evolving patient or staff needs. Patient intake was slow and required constant manual intervention. Front-desk staff overwhelmed by repetitive tasks — appointment scheduling, basic inquiries, refill requests.

The existing system couldn't integrate securely with the EMR platform — limiting usefulness AND introducing HIPAA compliance risk every time staff worked around it. Sensitive health data was flowing through channels that hadn't been threat-modeled.

Provider needed a modern conversational solution that could handle sensitive health data securely, comply with HIPAA end-to-end, integrate with their EMR, and reduce administrative overhead — without expanding their security/compliance team.

The MCG Approach

3-person team with deep expertise in conversational AI architecture, Azure-based integration, and healthcare data security. Tight scope, fast delivery.

Built on Azure OpenAI + Power Virtual Agents, fully integrated with the internal EMR via Azure API Management for secure real-time record access and appointment scheduling. Custom business logic via Power Automate and Azure Functions. Microsoft Dataverse handled secure data and operational auditing.

HIPAA-first architecture: threat-modeled end-to-end before a single line of code. Every data flow documented; every component selected against the BAA-coverage matrix.

Omnichannel deployment across web and mobile from day one — patients meet the AI where they are, staff get one operations surface.

The Challenge

Major US energy company launched an AI-driven transformation in 2025 to meet explosive demand from AI data centers and next-generation smart infrastructure. The company executed a $12 billion acquisition to nearly double generation capacity by ~13 GW and expand its US footprint, while initiating natural-gas power-plant projects adding ~5 GW.

Operating reality: scale fast, but in a highly regulated, data-sensitive environment. AI couldn't simply be deployed — it had to be operationalized with the compliance, reliability, and precision that grid operators and regulators require.

Internal team had AI ambition but lacked the depth of MLOps + regulated-AI experience needed to ship at the scale and cadence the acquisition demanded.

The MCG Approach

Dual engagement model: AI/data consultant staffing AND specific projects under SOWs — senior consultants embedded across data science, MLOps, AI systems deployment, and cloud infrastructure.

Tech depth + domain fluency: Python, SQL, Spark, LLMs, GenAI, RAG, full MLOps toolchain. The differentiator was sourcing talent with deep technical proficiency PLUS the domain understanding required for regulated, data-sensitive energy environments.

Operationalization, not experimentation: consultants led the operationalization of AI-powered virtual power plant systems, integrated ML models for demand forecasting and grid optimization, and enabled AI-enhanced trading and smart-home platform intelligence.

AI Readiness roadmap established and actively executed against — turning a year of acquisition-driven scale into a multi-year, AI-architected operating model.

The Challenge

Struggling to forecast product shipments with the precision needed for supply chain efficiency. Legacy forecasting code and fragmented data pipelines couldn't keep pace with volatile market conditions. External data inconsistencies hindered model reliability.

Executives lacked any real-time, intuitive way to access operational insights — they relied on analysts to extract data manually. Every decision was either slow (wait for the analyst) or based on stale data (use yesterday's report).

Leadership wanted an agile AI solution that could improve forecasting accuracy AND deliver self-service executive analytics simultaneously. One transformation, two surfaces.

The MCG Approach

Comprehensive AI transformation modernizing data infrastructure, forecasting models, AND the executive analytics interface in parallel.

Conversational NLP into AWS QuickSight — leadership now queries datasets in natural language and gets tailored visualizations instantly. No code, no analyst-in-the-loop, no waiting for a dashboard to refresh.

Data backbone rebuilt: extracted and normalized from SAP HANA, engineered semantic models with business-relevant terminology, built automated pipelines feeding QuickSight in near-real-time.

Forecasting overhauled with new ML models tolerant of volatile market inputs, integration of high-confidence external signals, regional model tuning. Codebase upgraded Python 3.7 → 3.10 along the way — extending system support life by an estimated 36 months.