General
🩺 Persly-AI × LambdaDB: Strengthening Trust in Medical AI Through Better Data Infrastructure
Nov 25, 2025
"What seriously ill patients need isn’t just words of comfort — it’s answers they can trust." — Persly AI Team
We wanted to Ease the fear outside the hospital
"Patients battling cancer often get just three minutes to see a doctor. You can not possibly understand your condition in three minutes."
That’s how the persly team began our conversation.
After those brief consultations, many patients turn to online communities, but the process is long, tedious, and emotionally exhausting.
You might have to post multiple comments, wait several days for approval, and when you finally ask your first question, the replies are mostly ‘Stay strong,’ or ‘I went through that too.’
Seeing this imbalance between critical need and credible information, Parsley set out to build an AI-powered assistant grounded in official medical literature.
Unlike generative chatbots that create answers from scratch,Parsley’s model retrieves verified documents from a medical knowledge base and summarizes them in plain language.
“It’s not about generating text — it’s about retrieving facts. Our system references over a million validated medical documents from trusted institutions.”
The real challenge: Scaling medical data
As the knowledge base grew, so did the infrastructure challenge.
“We started with FAISS — it was open-source, easy to experiment with, and well-documented. But we quickly realized that identical queries could return different results depending on the algorithm.”
The team tested nearly every major vector database: Chroma, Pinecone, pgvector, Google Vector Search
each solved part of the problem, but none balanced performance, cost, and ease of use.
FAISS / Chroma → good for prototypes, not for production.
Pinecone → no Korea region; latency around 2 seconds on average.
pgvector → heavy contention with Postgres OLTP; indexing 10 million documents caused frequent slowdowns.
Google Vector Search → stable, but cost-prohibitive at $2.5 – 3 K USD/month.
“We needed three things at once — local region support, predictable cost, and large-scale upsert stability. but no single option delivered all three.”
LambdaDB felt like Pinecone's UX with GCP's Stability
When Parsley tried LambdaDB for the first time, the reaction was immediate: “Fast and effortless.”
It supports various regions in globe, It is fast, and it is affordable. In a sense, it combines Pinecone's usability with GCP's reliability."
Migrating their 10 Million+ medical document corpus was unexpectedly smooth.
“With Pinecone, bulk upserts required setting up S3 buckets and connecting URLs — tedious and error-prone. LambdaDB’s Bulk upsert API handled everything for us. The full migration only took half a day
Stability, Speed, and Transparent Cost
“What we felt most after switching to LambdaDB was stability.
The previous bottlenecks during large upserts disappeared entirely. Query performance improved dramatically: most searches now return in under 100 ms, and even complex queries stay within a half second. Just as important — the billing process became transparent.
“With Pinecone, it took three days for usage data to update. We were basically operating blind. With LambdaDB, costs are visible immediately. It feels like there’s no management overhead at all.
Operational flexibility improved too:
“You can delete or query documents using simple MongoDB-style filters. Pinecone only supports single-key filters, but LambdaDB allows compound AND conditions. For our upcoming global rollout, where data must be filtered by both language and country, that’s essential.”
Lessons from Trial and Error:
Start Small, Design Early
After multiple iterations, Parsley’s engineers distilled two hard-earned insights:
1️⃣ Start with small benchmarks.
“Once you embed, you can’t easily redo it. Begin with a small subset and validate retrieval quality first.”
2️⃣ Design metadata from day one.
“We once had Arabic mixed into some documents but no ‘language’ column in our schema — which meant we couldn’t filter or delete them later. Metadata design is everything.”
“Our Goal Is to Build a Platform That Truly Helps the Sick.”
Parsley’s long-term vision goes far beyond answering questions.
“When patient data aggregates over time, it can connect hospitals, pharmaceutical companies, and insurers. Imagine cancer patients being able to re-enter insurance plans at one-fifth the current cost because the data supports it. That’s the kind of positive impact we aim for.”
In medical AI, data infrastructure isn’t just a technical layer — it’s a matter of trust.
“A wrong answer can put someone’s life at risk. Accuracy and privacy are everything. LambdaDB gives us the confidence to scale responsibly.”
What Parsley Hopes to See Next
“It’d be great if LambdaDB hosted more sessions where experts share real-world RAG and indexing strategies. The AI ecosystem is growing fast, but seasoned practitioners are still rare. We’d love to join that conversation.”
Closing Thoughts
Parsley’s journey shows how the right infrastructure choice can directly enhance the reliability of an AI service.
LambdaDB helps teams like Parsley:
operate across regions with minimal latency,
handle millions of records without downtime,
and keep costs predictable and transparent.
For any AI company handling sensitive or large-scale data — from healthcare to customer support — LambdaDB’s value becomes clearer as your data grows.
🚀 Start Your PoC
Ready to see what LambdaDB can do for your workload?
We’re offering guided PoC (Proof-of-Concept) sessions for teams building RAG, agentic, or large knowledge-base systems.
📧 Contact us directly: contact@lambdadb.ai
Experience how LambdaDB keeps your vector infrastructure fast, elastic, and worry-free — even at a billion-document scale.
