Starter AI Pilot
Price Range: $15,000 – $25,000
Timeline: 3–5 weeks
Best For: Organizations testing AI with fast, low-risk validation
Objective
Help your team identify, prototype, and validate one high-impact AI use case without large upfront investment or disruption.
What’s Included:
| AI Discovery Workshop | 1–2 sessions with business and technical stakeholders to identify priority use cases |
| Use Case Selection & Scoping | Prioritization based on feasibility, data readiness, and expected ROI |
| Data Audit & Exploration | Review of available data sources (claims, CRM, financials, support tickets, etc.) |
| AI/ML Prototype | Working model using Azure Machine Learning or OpenAI that proves feasibility |
| Business-Friendly Output | Results visualized in Power BI or Excel for immediate stakeholder review |
| ROI Snapshot & Recommendations | Simple ROI estimation, next-step roadmap, and scalability guidance |
Sample Use Cases You Can Choose From:
| Category | Example Use Case |
|---|---|
| Revenue Optimization | Predict payer reimbursement timelines |
| Denial Prevention | Flag high-risk claims before submission |
| Patient Collections | Score likelihood of patient payment |
| Operational AI | Generate summaries of long clinical/admin notes using GPT |
| Sales & CRM | Analyze sales pipelines or lead scoring with AI |
Tech Stack (Lightweight & Scalable)
- Azure Machine Learning Studio or Azure OpenAI
- Power BI for visualization (optional)
- Data sources: CSV, SQL Server, Azure Data Lake, Synapse, Excel
Optional Add-Ons:
- Power BI integration & dashboard polish: +$3,000 – $5,000
- Additional use case prototype: +$5,000 – $10,000
- API wrapper for integration (finance systems, etc.): +$2,000 – $4,000
Expected Outcomes
- Working AI prototype tailored to your business needs
- Clear insights into data readiness and model accuracy
- Framework for go/no-go decision making
- Executive-ready deliverables to support further investment
Example: Healthcare Client Pilot
Use Case: Predicting cash inflows from top 5 payers
Timeline: 4 weeks
Result: Achieved 85% accuracy on 30-day collections forecast, leading to a full-scale rollout
🟡 Growth Tier – Operational AI Rollout
Price Range: $50,000 – $100,000
Timeline: 8–12 weeks
Best For: Teams ready to embed AI into core operations for measurable impact
🎯 Objective
Deliver production-ready AI models integrated with your operational systems to automate workflows, improve accuracy, and accelerate revenue cycles.
✅ What’s Included:
| Multi-Model Deployment | 2–3 predictive or LLM-based AI models tailored to your business processes |
| Azure ML Pipelines & APIs | Production-grade model deployment with API endpoints for easy integration |
| Data Pipeline Setup | Setup or enhancement of data infrastructure (Synapse, Data Factory, Fabric) |
| Dashboard & Reporting | Role-based Power BI dashboards with real-time insights and alerts |
| Model Monitoring & Retraining | Ongoing performance monitoring and scheduled retraining plans |
| Business Alignment Workshops | Collaborative sessions to ensure AI solutions align with operational goals |
🧠 Sample Use Cases You Can Choose From:
| Category | Example Use Case |
|---|---|
| Revenue Cycle Management | Payer reimbursement forecasting and denial risk scoring |
| Patient Engagement | AI-driven patient collections prediction and communication automation |
| Clinical & Admin Automation | Summarization of clinical notes using GPT models |
| Sales & Marketing | Lead scoring and pipeline analytics with AI insights |
🧰 Tech Stack (Robust & Scalable)
- Azure Machine Learning Service & Azure OpenAI for model development
- Azure Synapse Analytics, Data Factory, and Fabric for data pipelines
- Power BI for enterprise dashboarding and alerts
- API integration with existing enterprise systems (ERP, CRM, billing)
🔁 Optional Add-Ons:
- Model governance and audit frameworks: +$5,000 – $10,000
- Secure private endpoint and Azure Key Vault setup: +$3,000
- Ongoing model support and performance monitoring: +$2,000 – $5,000/month
🚀 Expected Outcomes
- Automated, accurate AI-powered decision support integrated into workflows
- Improved forecasting accuracy (80-90%) and revenue acceleration
- Operational efficiency gains and staff time savings
- Continuous model improvement and business alignment
👨💼 Example: Healthcare Client Growth Rollout
Use Case: Deployed 3 models for payer reimbursement, claim denial risk, and patient collections
Timeline: 10 weeks
Result: Achieved 87% forecasting accuracy and reduced denials by 15%, leading to 10% cash flow increase
