Case studies

Jackalope automates transforming complex medical data into standardized formats of OMOP CDM and SNOMED Clinical Terms (CT). This enables healthcare providers, practitioners, and scientists to effectively use real-world evidence (RWE) in healthcare for research and delivering enhanced patient care.

Our client was a U.S.-based, multi-institutional healthcare research initiative developing an AI-ready clinical data ecosystem for critical-care research. The initiative brought together several institutional data sources with differences in clinical workflows, source systems, coding practices, data availability, and OMOP ETL implementations. The client needed a systematic way to translate complex clinical research questions into reproducible computable phenotypes that could support cohort discovery, population characterization, outcome definition, and downstream analytical and machine-learning workflows. The project focused on structured electronic health record data represented primarily through the OMOP Condition, Procedure, Measurement, Observation, Device, Drug, Specimen and Visit domains. To preserve confidentiality, the organization and individual clinical use cases are not disclosed.

The client is a professional healthcare technology provider whose platform is used by multiple medical institutions to support clinical medical data interoperability. The project focused on enabling medical semantic search using LLMs and vector databases, allowing healthcare teams to structured clinical data extraction from free-text input. To achieve this, the platform integrates large language models for real-time query normalization and a locally deployed Quadrant vector database for high-performance concept retrieval. The solution was designed to deliver accurate concept mapping at scale, while aligning with MLOps and DevOps best practices to ensure reproducibility, modularity, and operational stability across environments.

The client provides an AI for quick service restaurants (QSR) restaurants that uses a custom NLP system to interpret natural customer speech and convert it into structured, kitchen-ready orders. The menu-aware NLP system handles complex, brand-specific menus with customizable combos, modifiers, and promotional items. It recognizes varied phrasing, synonyms, and marketing terms, and maintains contextual awareness across multi-turn conversations. The AI-driven order automation system supports slot filling, multilingual input (English and Spanish), real-time menu updates, and upselling logic.

Our client operates across industries, providing enterprise performance management with AI that centralize business metrics for better decision-making. Their product is an intelligent data management platform designed to streamline operations, track KPIs, and offer AI-driven performance management and predictive analytics through LLM integration for business analytics. The system consolidates company metrics like recruitment, sales, and operations into a single structured platform, eliminating inefficiencies from scattered data. It features AI decision support system, a chatbot for natural language queries, and customizable LLM-powered enterprise data processing and decision intelligence. Used by internal teams, executives, and stakeholders, the product is flexible and scalable, adapting to different industries as a centralized knowledge hub.

We built an AI-powered Knowledge Assistant for enterprise resource planning provider, delivering instant, accurate answers to complex queries. It uses NLP, vector search, and contextual understanding to integrate with knowledge bases and enhance customer experience. Our client is a leading provider of enterprise digital transformation solutions for various sectors, with a strong focus on the financial industry as well as manufacturing, distribution, and retail. Their offerings include tools for managing supply chain operations, financials, production, and customer relationships. They requested us to develop an LLM-based chatbot for internal documentation and ERP knowledge that gives users quick access to information about the company’s products and services. It uses a detailed database of presentations, documents, and common questions to provide fast and accurate answers to inquiries, helping their client to get a comprehensive consultation without the need to wait for a response from sales team members.

The implementation of AI-powered receipt and invoice processing for fintech enabled efficient handling of diverse receipt formats for taxi services. Using advanced OCR automation for finance with Tesseract, GANs for image enhancement, and LLMs for data structuring, we created a comprehensive intelligent document processing for expense management solution that ensured high data accuracy and reduced processing times.

Our product brings a new approach to healthcare claims management with its AI-driven capabilities. It streamlines claim processing and reduces denials, improving efficiency and financial outcomes for healthcare providers. The client is a startup operating in the intersection of FinTech and Healthcare sectors, focusing on insurance claims management. They are addressing the claim denial management issues, a significant challenge within the U.S. healthcare system. Our client’s goal is to transform the current claim handling process helping hospitals and medical practices to reduce financial losses and administrative burden: