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Examya

WhatsApp medical agent for exam orders in Chile.

13 posts

FHIR DiagnosticReport: how a lab result travels back to the ordering physician

The result is ready. Now it needs to reach the physician who ordered it — no PDF, no WhatsApp, no human middleman. Here's how FHIR DiagnosticReport works and how Examya implements it.

How I Built Patagonia's First Private COVID PCR Lab (And Why I Ended Up Building AI)

In March 2021, I hoisted 300 kg of biosafety cabinet by crane to a second floor during lockdown. By May we were running the first private COVID PCR tests in Chilean Patagonia. The nights that followed became the real origin of Examya.

When your sub-agent lies: 3 failing tests that gemini-flash swore were passing

gemini-flash reported 'all tests passing': 3 tests were failing, 353 lines of stray package-lock.json included. The 4-command protocol I built to audit sub-agents in Examya.

From 0 to WhatsApp Payments: Mercado Pago + Stripe from a Single Conversation

How I built a payment system embedded in WhatsApp that processes medical orders and charges automatically with Mercado Pago and Stripe.

FHIR + Law 21.668: How Examya Is Preparing for Chile's Mandatory Interoperability

How we're adding a FHIR layer on top of Examya's current stack (NestJS + Prisma + pgvector) to comply with Law 21.668 without rewriting anything.

Clinical labs: the missing piece for healthcare interoperability in Chile

We mapped 245 clinical labs from Arica to Punta Arenas. Four out of ten lack a functional digital presence. Law 21.668 will force them to interoperate in 2026. Here are the ground-level data.

Chile now requires clinical record interoperability: why this changes everything for digital health

Law 21.668 mandates all healthcare providers in Chile to make clinical records interoperable. I analyze what this means technically, which standards are coming (FHIR, SNOMED CT, AIToF), and how Examya is preparing for this newly mandatory market.

Crowdsourcing medical prices: how Examya builds cost transparency layer by layer

The real architecture behind Examya's 3-layer pricing intelligence: FONASA data, user crowdsourcing, and order generation from WhatsApp. With code, design decisions, and real bugs.

Medical OCR on WhatsApp: how my agent reads exam orders and lab results

The real architecture behind Examya's OCR pipeline: how an AI agent classifies WhatsApp photos, decides if they're medical orders or lab results, and automatically generates FONASA quotes. With real bugs and design decisions explained.

DeepEval: how I measure the quality of my medical agent with objective metrics

How I built an evaluation layer with DeepEval to measure the quality of Shuri, Examya's medical agent. With real data: from 20% to 70% on E2E, custom metrics for Chile's FONASA system, and why gpt-5-nano doesn't work for structured output.

pgvector + Embeddings in Production: The Foundation of Medical Reasoning in Examya

Architecture for semantic search and text similarity in production with pgvector, pg_trgm, and real MINSAL data.

One Week of Building: 82 Decisions That Shaped an AI Product

What Engram's memories reveal about a real week of development: bugs caught, architecture hardened, and the invisible decisions that make a medical agent work.

Examya: how I built a medical WhatsApp agent that processes exam orders

Technical details of implementing the Shuri agent in Examya, a system for processing medical orders via WhatsApp with FONASA integration.