Case Studies
AI-Enabled Operations Transformation for a Regional Healthcare Network
We at Stratium partnered with a growing regional healthcare provider that managed twelve outpatient clinics across Texas. The client was experiencing significant operational strain caused by manual patient intake processes, inconsistent scheduling, and long administrative turnaround times. These issues created bottlenecks that slowed the patient experience and increased operational costs.
The leadership team wanted to understand how artificial intelligence and modern workflow automation could reduce administrative time and help them operate more efficiently without adding more staff.
Our Approach
We began by conducting a full operational assessment to map existing workflows and quantify inefficiencies. We analyzed scheduling patterns, patient flow data, and administrative workloads. Our team then built a three part solution. First, an AI powered scheduling assistant that automatically optimized patient appointments based on demand patterns, clinician availability, and care requirements. Second, a document intelligence pipeline that automatically extracted information from insurance cards and medical intake forms. Third, an operational dashboard that gave real time visibility into clinic performance.
We stayed close to the client’s operations team to ensure adoption. Staff were trained on the new tools and we implemented a short pilot period before full rollout.
Results
Within the first ninety days, administrative processing time dropped by forty percent. Clinics reported a twenty five percent reduction in scheduling conflicts, and average patient check in time fell from nine minutes to under five. These improvements allowed the network to absorb growing patient demand without increasing front office headcount. Leadership now uses the analytics dashboard to make staffing and resource decisions based on actual data, not assumptions.
AI-Driven Marketing and Sales Acceleration for an E-Commerce Brand
A fast growing e-commerce brand in the wellness sector approached Stratium to help address slowing revenue growth. Despite having a strong product and loyal customer base, the company relied on manual customer segmentation and broad email campaigns that failed to convert. Customer acquisition costs were rising each quarter and leadership needed a new strategy to scale without overspending.
Our Approach
We began by conducting a full review of the brand’s customer data, purchase patterns, and marketing processes. Our team identified several opportunities to use artificial intelligence to make the marketing engine more precise. We built an AI powered customer segmentation model that grouped buyers based on behavior, preferences, and predicted lifetime value. We also developed personalized outreach sequences for email and SMS that adapted content based on past purchases and likelihood to convert.
To support ongoing decision making, we created predictive demand models that forecasted weekly sales and inventory needs.
Results
Within sixty days, the company saw a thirty eight percent increase in email click through rates and a twenty seven percent lift in overall conversion rates. Customer acquisition cost decreased by twenty two percent due to more precise targeting. For the first time, the leadership team had clear visibility into demand patterns, which helped them reduce inventory shortages by almost fifty percent. These improvements positioned the brand for sustainable long term growth.
AI Automation and Workflow Optimization for a Manufacturing SMB
A mid sized manufacturing company with two hundred employees partnered with Stratium to modernize its internal operations. The company struggled with manual order processing, slow quoting cycles, and frequent delays in production planning. The existing processes depended heavily on spreadsheets and long email threads. These inefficiencies impacted revenue and strained customer relationships.
The CEO wanted to understand if AI and automation could streamline the workflow without disrupting existing systems and staff.
Our Approach
We started by shadowing employees in operations, production, and customer service to understand the full order to delivery process. After analyzing the findings, we developed an automation solution that included an AI powered order intake assistant, automated quote generation, and a production scheduling engine that prioritized jobs based on urgency and resource availability.
We integrated the new workflows into the client’s existing ERP system and provided hands on implementation support. Our team also introduced a performance dashboard that tracked key metrics like production lead time and order cycle time.
Results
The company reduced order processing time by fifty five percent and shortened quoting cycles from two days to less than four hours. Overall production throughput increased by twenty percent, enabling the company to take on additional volume without hiring more staff. Customer satisfaction scores rose significantly due to faster communication and fewer delays. The CFO reported that the improvements were equivalent to adding three full time operational team members at no additional labor cost.