Back office ops · Production

Komodo Health embeds MapAI generative AI assistant in MapLab enterprise healthcare analytics platform

The problem

Non-technical pharmaceutical team members — brand leads, Medical Affairs managers, and clinical researchers — had to wait weeks or months for data insights to be delivered, creating bottlenecks in decision-making across the product lifecycle.

Workflow diagram · grounded in source
1
User submits natural language question
trigger
“Simple questions and prompts posed in layman's English start the exploration”
2
AI agent understands intent
ai_action
“By employing an intelligence agent to understand the intent behind each question”
3
Route to specialized GenAI agents
routing
“our system can effectively delegate tasks to specialized GenAI agents capable of querying our extensive Healthcare Map™”
4
Return insights and visualizations
output
“users can ask simple questions using layman's English and quickly receive data insights and graphic visualizations”
5
Save cohorts for reuse
feedback_loop
“Cohorts and codesets can be saved and reused in deeper analyses”
Reported outcome

MapAI enables every team member regardless of technical skill to query healthcare data in natural language, accelerating decision-making and reducing dependence on specialist data teams.

Reported metrics
wait time for data insights before MapAIweeks or even months
time to get answers with MapAIquickly
Reported stack
MapAIMapLabHealthcare MapLangChainLangGraphLlama 3.1Mistral 7BPhi-3
Source
https://www.komodohealth.com/perspectives/new-gen-ai-assistant-empowers-the-enterprise
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

MapAI enables every team member regardless of technical skill to query healthcare data in natural language, accelerating decision-making and reducing dependence on specialist data teams.

What tools did this team use?

MapAI, MapLab, Healthcare Map, LangChain, LangGraph, Llama 3.1, Mistral 7B, Phi-3.

What results were reported?

wait time for data insights before MapAI: weeks or even months; time to get answers with MapAI: quickly (source-reported, not independently verified).

How is this back office ops AI workflow structured?

User submits natural language question → AI agent understands intent → Route to specialized GenAI agents → Return insights and visualizations → Save cohorts for reuse.

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