Overview

A healthcare AI company that had raised a Series C round came to Bacancy with a clinical decision support tool that demoed well enough to win over investors but fell apart the moment it faced real clinicians. The problem was the application layer, not the model, so that is where we focused: we added guardrails to keep it in its clinical lane, an automated eval suite to catch regressions, a human-review fallback for low-confidence answers, and production monitoring to watch it all in real time. With that foundation in place, the platform moved from a controlled demo to a live platform serving 10,000 clinicians, running its first 90 days without a single critical incident and clearing its HIPAA audit along the way.

Technologies Used

Reactjs
Python
Machine Learning
AWS

Project Highlights

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Context-aware guardrails that constrain model output to clinical scope

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Automated evaluation suite covering 500 clinical test cases

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Human-in-the-loop fallback triggered on low-confidence outputs

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Structured logging and real-time alerting across the production stack

Challenges & Solutions

1

The model hallucinated in clinical edge cases, with no constraints on what it could output to a clinician.

2

There was no fallback path when the model was uncertain, so low-confidence answers reached users unchecked.

3

No evaluation framework existed. Testing was manual, so regressions slipped through between builds.

4

Nothing was monitored in production, which made the platform impossible to deploy to regulated users or take through a HIPAA audit.

Solutions by Bacancy

1

Our AI developers built a guardrail layer that constrains every response to validated clinical context and blocks out-of-scope outputs before they reach a clinician.

2

Our ML engineers designed an automated evaluation suite of 500 test cases that runs on every release, catching accuracy and safety regressions before they reach production.

3

The team added a human-review fallback that routes any low-confidence output to a qualified clinician instead of surfacing an unverified suggestion.

4

Our engineers implemented structured logging and real-time alerting across the stack, giving the client the audit trail and observability needed to clear the HIPAA audit.

Core Features

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Context-aware clinical guardrails

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Automated 500-case evaluation suite

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Human-in-the-loop review on low-confidence outputs

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Production monitoring with structured logging and alerting

No. of Resource

5

No. of Resource

Time Frame

Nov 2025 – March 2026

Time Frame

Project Snapshot

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Outcomes

100% pass on the HIPAA audit

90 days live with zero critical incidents

4.7/5 clinician satisfaction rating

10,000 clinicians served at production launch

99.9% uptime across the first 90 days in production

500 automated test cases replacing all manual testing

Technical Stack

Frontend React.jsTypeScriptTailwind CSS
Backend Python (FastAPI)Node.js
Database PostgreSQL
AI/ML LLMNLPML
In-Memory Data Store Redis
API Communication RESTful APIs, WebSocket

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