industry: How Heidi built production-ready AI for healthcare at

Australian AI Care Partner Heidi has achieved a significant milestone in healthcare technology, deploying its flagship AI product, Heidi Scribe, at a global scale. Now operational across more than 190 countries, the platform supports an estimated 2.7 million patient interactions weekly by automating administrative tasks for clinicians. This expansive reach is the direct result of strategic infrastructure decisions made early in the company's development, establishing a robust framework for secure, compliant, and accurate AI in a highly regulated industry.

Navigating the Complexities of Healthcare AI

Developing AI for sectors like healthcare presents unique and formidable engineering challenges, particularly regarding accuracy, security, and reliability. Yu Liu, co-founder and chief technology officer at Heidi, highlights that an AI error rate of just two percent, which might be a minor inconvenience in other industries, escalates into a critical clinical safety issue in healthcare. This fundamental difference mandates an architectural approach where every AI output can withstand scrutiny and auditability, forming a crucial component of patient care.

Healthcare AI also faces stringent compliance obligations, including data residency requirements dictated by regulations such as GDPR, HIPAA, and Australian Privacy Principles. Heidi addresses this by running logically isolated production deployments worldwide, ensuring patient data remains within its respective regional boundaries. This infrastructure-enforced residency is a core tenet, not merely a contractual promise.

Furthermore, auditability is baked into Heidi's system from day one. The architecture must consistently provide answers on what models processed, what they generated, and any subsequent clinician modifications, even months after a session. To maintain safety and prevent widespread issues, Heidi heavily invests in robust change management, incorporating continuous integration gates on risky changes, canary releases, and treating even database schema updates as reviewed code.

Data Infrastructure: The Foundation for AI Success

Heidi's platform processes a diverse array of medical data, including forms, referrals, and clinical notes, all requiring consolidation into a consistent format for seamless AI integration. Traditional rigid relational databases proved unsuitable for this dynamic workload. Consequently, Heidi opted for a document database, selecting MongoDB for its inherent flexibility in accommodating rapidly evolving AI data structures without constant schema reshaping.

Liu emphasizes that the AI model itself represents only about 20% of the system, with the data architecture determining whether the remaining 80% can handle real clinical loads. An AI Scribe session is a complex data constellation, encompassing transcripts, structured notes, templates, patient context, and EHR integration states, all subject to frequent evolution. MongoDB allows this session data to reside together in adaptive shapes, mirroring clinical workflows and enabling product evolution without disruptive migrations.

Specifically, Heidi leverages MongoDB Atlas, a globally distributed database platform that combines the document model's scalability and performance with built-in AI-ready features. Notably, MongoDB Vector Search eliminates the need for a separate bolt-on vector database, simplifying Heidi's architecture. By migrating to Atlas, Heidi observed a nearly 33% reduction in latency on critical APIs.

Crafting a Trustworthy Clinical RAG System

Heidi's Retrieval Augmented Generation (RAG) system, Heidi Evidence, operates under strict clinical guidelines, diverging significantly from consumer-grade RAG. Instead of retrieving from the open web, Heidi Evidence draws from licensed clinical knowledge bases, partnering with reputable sources like BMJ Best Practice, NICE CKS, and MIMS. This ensures the retrieved information is authoritative and medically sound.

A critical feature is its jurisdiction-awareness, providing UK clinicians with UK guidance and Australian clinicians with Australian formularies, recognizing that medical best practices can vary by region. Heidi’s vector embeddings and indexes are integrated within MongoDB Vector Search, residing in the same regionally isolated deployments as other patient data. This architectural choice physically prevents retrieval from crossing residency boundaries and streamlines security and compliance. Citations are a hard contract, meaning the model only processes retrieved chunks explicitly linked to source records.

Regional Isolation and Global Expansion

Heidi's global compliance strategy is underpinned by a meticulously engineered regional isolation model. "Each region is a full, isolated production deployment with its own MongoDB Atlas clusters, its own compute, and its own key," Liu explains. This robust infrastructure enables Heidi to provide clear answers on data residency to healthcare systems worldwide, from U.S. health systems to NHS trusts and Australian hospitals, because it's enforced by architecture, not just by contract.

The multi-cloud capability further enhances agility, allowing new regional deployments to be stood up rapidly using pre-built rails. This strategy proved instrumental in Heidi's U.S. market entry, where it became a strategic partner for non-profit MaineGeneral Health in rural healthcare and saw its AI scribe rolled out by Beth Israel Lahey Health, one of New England's largest health systems. A pilot at Beth Israel Lahey Health revealed a 74% reduction in after-hours documentation, often referred to as "pajama time," for clinicians.

Looking Ahead: Trust and an Agentic Future

Heidi's experience underscores the importance of foundational architectural decisions, especially regarding horizontal scalability for fast-growing data. Liu advises that choosing a shard key on day one is a design meeting, while re-partitioning a large, hot collection later is a major engineering undertaking. Heidi is actively partnering with MongoDB to tackle such challenges, solidifying its data-heavy AI product for future growth.

The company's roadmap extends beyond automating consult notes to supporting the entire clinical workflow, encompassing pre-visit context, post-visit documents, referrals, and workflow automation. Heidi is also exploring how MongoDB, alongside large language models and its proprietary tools, can power an agentic ecosystem for comprehensive clinical workflows. Liu concludes by emphasizing that in healthcare AI, reliability engineering is synonymous with trust engineering. Clinician trust, he states, is the ultimate product, and it is intrinsically architectural, built on consistent performance, low latency, and data integrity.

FAQ

Q: What makes deploying production AI in healthcare uniquely challenging compared to other industries?

A: Healthcare AI faces distinct challenges including stringent data residency requirements (e.g., GDPR, HIPAA), the critical need for near-perfect accuracy where errors can be clinical safety issues, and comprehensive auditability for every AI output. Organizations must also manage a diverse range of sensitive medical data and operate under slow-moving regulatory environments.

Q: How does Heidi ensure data residency and compliance for patient data globally?

A: Heidi employs a strategy of full, logically isolated production deployments for each region worldwide. This means that patient data, including vector embeddings and indexes, resides within its specific geographical boundaries, enforced by the underlying infrastructure, rather than relying solely on contractual agreements. MongoDB Atlas's global distribution capabilities facilitate this approach.

Q: What role does MongoDB play in Heidi's AI infrastructure and its RAG system?

A: MongoDB serves as Heidi's core document database, providing the flexibility needed to manage diverse and rapidly evolving medical data for AI workflows. MongoDB Atlas, specifically, offers scalability and incorporates AI-ready features like MongoDB Vector Search, which handles vector embeddings for Heidi's RAG system, eliminating the need for a separate vector database. This integration helps maintain data residency and improves overall performance.