Client is a high growth specialized infusion therapy provider operating a growing network of clinical centers. Infusion therapy carries real complexity: every prescription requires validation of drug interactions, dosing calculations, insurer-specific requirements, and patient history. The company’s Infusion Guides, the clinical staff who manage patient intake and treatment workflows, were spending most of their time on manual data entry, Rx interpretation, insurance verification, and documentation instead of patient care.
Before the client's partnership with Forte Group, growth was constrained by three systemic issues common across high-growth care delivery platforms:
We built the Virtual Infusion Guide (VIG), an AI platform that automates prescription processing, provides real-time clinical decision support, and captures expert knowledge in a system every Guide can access from day one. Five engineers built and maintained the platform that now serves as the technology backbone for Client’s clinical operation across 47 centers, scaling to 85 by year-end.
OCR pipelines extract structured data from prescription faxes, scanned documents, insurance forms, lab results, and clinical records across multiple formats. Azure OpenAI GPT-4 interprets the extracted content, triages pharmacy or medical eligibility, validates Rx completeness against clinical and insurance requirements, automates actions, and recommends follow-ups based on the referral. What previously took a Guide 20 minutes now takes 5.
A RAG-based conversational system using OpenSearch semantic search lets Guides query patient records, lab results, treatment protocols, and insurer workflows through natural language. A centralized knowledge base links drug requirements, insurer-specific rules, and validated responses from Client’s most experienced staff. The chatbot was bootstrapped from expert Guide conversations and fine-tuned on clinical ground truth, so it delivers answers grounded in how the best Guides actually work. A human-in-the-loop workflow routes low-confidence outputs to senior Guides for review, and their corrections feed back into the model.
LangChain-based agentic workflows orchestrate patient intake and clinical document routing across multiple systems. MCP (Model Context Protocol) endpoints expose healthcare data to AI agents securely, enabling multi-agent coordination across clinical workflows without compromising data governance. RPA processes handle administrative automation and document processing for tasks outside the AI workflow.
A Snowflake data warehouse with dbt transformations provides end-to-end pipelines for patient data, lab analysis, and clinical review workflows. MongoDB handles operational data storage. The platform integrates with EMR systems and scheduling platforms. Infrastructure runs on Azure Container Apps managed via Terraform, with logging, observability, and release management built in from the start. Beyond supporting the clinical product, the data layer creates a structured data asset that Client now uses for monetization through data-driven partnerships.
Client had the demand. What it lacked was the operational capacity to process growing referral volumes. The VIG platform solved that. Existing centers now absorb increasing volume without adding headcount proportionally. Each Guide handles more referrals at higher quality because the system carries the administrative load that used to consume their day.
For growth-stage operators and investors evaluating AI investments, this engagement shows three things. AI creates measurable operating leverage: the ratio of referral growth to engineering cost makes that concrete. A data foundation built alongside the product opens monetization paths that did not exist before. And the pharmacy market expansion shows how AI capabilities built for one use case transfer to adjacent verticals with minimal additional investment.
Azure OpenAI GPT-4, Anthropic Claude API, OpenSearch (semantic search and RAG), LangChain (agentic workflow orchestration), MCP (Model Context Protocol for multi-agent coordination), Azure Document Intelligence (OCR and document extraction), Snowflake (data warehouse), dbt (data transformations), MongoDB (operational data), Azure Container Apps (infrastructure), Terraform (IaC), EMR and scheduling platform integrations, RPA for administrative automation.
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