kaiko.ai Tech Stack

Clinical AI assistant for fragmented hospital data and care workflows

Technology, Information and Internet Amsterdam 51–200 employees Founded 2021 Privately Held

Kaiko.ai builds healthcare-specific AI systems for hospital environments, not general-purpose models adapted to medicine. The tech stack reveals a data-intensive, evaluation-focused architecture: Dagster + Airflow orchestrate pipelines, dbt transforms medical data (FHIR standards), Ray and LangChain power reasoning across disconnected sources, and Cypress/Playwright automate clinical workflows. Active projects center on reducing hallucinations and scaling evaluation to clinical-grade performance — a signal the company is prioritizing safety and regulatory credibility over rapid feature deployment. Hiring is senior-skewed and engineering-forward, matching the hard technical problems of multi-domain reasoning and drift reduction in regulated environments.

Tech Stack 16 technologies

Core StackPython Dagster Apache Airflow dbt Apache Spark Kubernetes Jira Confluence Cypress Playwright LangChain RAG Git Ray AutoGen FHIR

What kaiko.ai Is Building

◆Challenges

Reducing drift and hallucinations
Scaling core data systems
Quality gaps cost engineering velocity
Large-scale evaluation for clinical-grade performance
Scalable commercial platform
Reducing context loss
Reasoning across data domains
Quality gaps cost clinician trust
Clinical credibility
Clear positioning

▲Active Projects

End-to-end evaluation stack
Synthetic benchmark generation
Durable memory state
Persistent outputs integration
Context lifecycle management
Platform integrations and apis
Go-to-market strategy
Core data systems ingestion and transformation
Repeatable pipelines and processes
Sales motion end to end

Hiring Activity

Accelerating15 roles · 10 in 30d

Department

Engineering

Sales

Data

Marketing

Seniority

Senior

Lead

Mid

Notable leadership hires: Head of Marketing

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About kaiko.ai

Kaiko.ai develops a multidisciplinary clinical AI assistant (kaiko.w) that aggregates fragmented patient data from pathology, radiology, lab systems, clinical notes, and imaging archives into a coherent context for care teams. The platform operates on hospital-specific protocols and guideline checks, uses agentic reasoning to connect multiple data sources, and maintains human-in-the-loop audit trails. Founded in 2021 and based in Amsterdam, the company serves hospital partners across Europe and is actively scaling engineering, sales, and data teams. Core technical challenges include reducing model drift and hallucinations, scaling evaluation infrastructure to validate clinical safety, and managing context lifecycle across years of longitudinal patient records.

HeadquartersAmsterdam

Company Size51–200 employees

Founded2021

Hiring MarketsSwitzerland, Netherlands, Peru

Frequently Asked Questions

What tech stack does Kaiko.ai use?

Python, Dagster, Apache Airflow, dbt, Apache Spark, Kubernetes, LangChain, AutoGen, Ray, and FHIR standards. Stack emphasizes data orchestration, evaluation, and agentic reasoning across healthcare data sources.

What is Kaiko.ai working on?

End-to-end evaluation stack, synthetic benchmark generation, reducing hallucinations and drift, scaling core data systems, context lifecycle management, and platform integrations. Primary focus is clinical-grade performance validation and reducing context loss across medical data domains.