HealthAI

A helpful, grounded health insurance chatbot

The Problem

Call center agents supporting health insurance members face a difficult task:

Answering complex, nuanced
questions about plan benefits in real-time while a member waits on the line.

Allow insurance agents to quickly answer complex health insurance queries without clicking through PDFs or websites.

Make it easy to surface information across multiple facets such as copays, deductibles, coinsurance, and coverage limits.

Give agents a "superpower" which allows them to give precise dollar amounts for out of pocket costs and treatment follow-ups.

Constraint	Brief	Rationale
Regulatory & Compliance	No member PII in queries	The chatbot must process & transmit plain questions only - no member names, IDs, or conditions should be sent over the wire.
Grounding & Truthfulness	Answers must be grounded in source data	The chatbot cannot hallucinate or infer benefits not explicitly documented; responses must cite actual plan attributes
Multi-plan Support	Agents handle queries across multiple plan ids	The chatbot should be able to query the correct plan context and retrieve relevant details
Follow-up Question Support	Complex queries may require multiple follow-up refinements	The chatbot should be able to handle interruptions and refinements to queries before retrieving the necessary data

Improvement	Description
Cost	FalkorDB and embeddings allow for far less input token usage w/ OpenAI
Latency	Related, the smaller payloads dramatically improve latency
Accuracy	The LangGraph nodes allow for semantic searches against the plan data as well as retrieving the appropriate sources
Retrieval & Caching	V2 narrows down what's relevant, and only sends the context necessary for the query. It also aggressively caches at multiple layers.