Evanke Labs

At Evanke, we recognize that effective AI innovation requires a collaborative approach. By partnering with Evanke Labs, clients gain access to –

Safe and secure spaces to experiment and research core AI capabilities.
Incubation environments and processes that enables development of tailored solutions for unique business challenges within managed sandboxes, ensuring compliance and safety.
Rigorous independent verification and validation, that ensures solutions are not only innovative but also transparent and trustworthy, aligning seamlessly with strategic objectives.

Clients collaborate with us to undertake core AI research and experimentation, leveraging our expertise and to further test, refine and optimize such capabilities — e.g., LLM quantization, agentic workflows, and retrieval -augmented generation (RAG) —all within secure, controlled environments to ensure compliance and safety.

Health & Life Sciences

Tissue Screening

Evanke Labs collaborated with a leading U.S. Organ Procurement Organization (OPO) to revolutionize human tissue screening for microorganisms and donor quality evaluation. Leveraging cutting-edge Natural Language Processing (NLP) and Generative AI, we analyzed Electronic Medical Records (EMRs) to assess donor eligibility.

Our approach included

Text Classification Models: Categorizing cardiovascular, renal, and other tissue types.
Large Language Models (LLMs): Identifying biomarkers across pathological reports, including serology and biochemistry.
Reinforcement Learning: Optimizing performance using insights from Medical Director reviews.

This innovative project highlights our expertise in blending advanced AI techniques to address critical challenges in healthcare and life sciences.

We incubate targeted client solutions prior to enterprise infusion by validating solution components and outcomes, while reducing data exposure and maintaining flexibility for seamless integration into enterprise IT processes.

Insurance

Conversational AI for Claims Intelligence

Evanke Labs partnered with a Fortune 50 insurance client specializing in workers' compensation within Property & Casualty (P&C). The core challenge was integrating insights from disparate claims systems and records into a unified, semantic understanding, enabling actionable intelligence through conversational AI.

Domain Augmented Enrichment: Enhanced the semantic understanding of the insurer’s claims data by training Retrieval-Augmented Generative (RAG) models with the insurer’s claims metadata.
Context Augmented Enrichment: Improved comprehension of claims and user contexts by leveraging Agentic Workflows, ensuring tailored and dynamic interactions.
Query Augmented Enrichment: Enhanced semantic accuracy and relevance with Question-SQL pairs across various frameworks, empowering claims agents with precise and contextually relevant answers to their queries.

This solution was meticulously incubated, ensuring readiness for seamless integration into the client’s enterprise IT processes, adhering to DevSecOps principles for secure and scalable deployment.

Conduct rigorous Independent Verification and Validation (IVV) to ensure the explainability of AI models, enhance transparency, detect biases, and prevent unintended outcomes.

Legal

AI for Petition Review and Compliance

Evanke Labs developed TACIT, an AI-driven framework to streamline petition review processes, focusing on explainability, fairness, and rigorous testing for secure deployment within the federal government.

Key capabilities include:

Explainability and Traceability: TACIT documents all datasets, training processes, and algorithms while persisting models (e.g., XGBoost, KNN) and hyperparameters for auditability. SHapley Additive explanation (SHAP) values were used to provide insights into feature importance and prediction drivers.
Fairness and Bias Mitigation: Balanced dataset representation ensures unbiased decisions, with safeguards to prevent bias related to filer type, demographics, or pre-determinations.
Robust Testing: Models undergo stratified test-train splits, extensive validation (e.g., accuracy, precision, recall), and evolving testing methodologies like vector distance metrics and LLM-based similarity scoring to adapt to new conditions.

This solution underscores Evanke Labs’ dedication to ethical, explainable, and production-ready AI systems for legal workflows.