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Quant finance is no longer just about finding alpha. The real edge increasingly comes from the ecosystem around it - data infrastructure, execution, risk systems, alternative data, compute and increasingly AI-driven research workflows. #Quant #FinTech #QuantFinance #AI
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What if anyone on your team could ask your data a question and get a live dashboard back in under a minute?
That was the brief. Business users were locked out of their own data. Every question waited on someone who could write SQL, data sat across disconnected systems, and security teams refused to send sensitive records to third-party AI tools.
So we flipped the model. Instead of moving enterprise data to an AI product, we moved the AI analytics product into the customer's AWS account.
What we built:
• Natural-language querying that turns plain-English questions into governed dashboards in under 60 seconds
• Federated queries across SQL and NoSQL sources through Trino
• AI query generation on AWS Bedrock Agents, with Bedrock Guardrails keeping model output in bounds
• Dashboards generated with Apache Superset, plus proactive anomaly and trend alerts
• A white-label React widget that embeds in any product
• Role-based access, row-level security and enterprise SSO enforced at every layer
The result:
Zero data egress. The whole platform deploys inside the customer's VPC and is live on AWS Marketplace.
Building AI features for a data-heavy or regulated product? Let's talk about doing it without your data leaving your cloud.
𝐑𝐀𝐆 𝐀𝐈 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦 | 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐭 𝐒𝐞𝐚𝐫𝐜𝐡, 𝐀𝐈 𝐀𝐧𝐬𝐰𝐞𝐫𝐬 & 𝐕𝐞𝐜𝐭𝐨𝐫 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞
I designed and built a RAG-powered AI knowledge platform that lets businesses search documents, websites, databases, and internal knowledge using natural language.
The system processes content, creates embeddings, stores them in a vector database, retrieves the most relevant information, and uses AI to generate accurate, source-grounded answers.
My services include: RAG development, document ingestion, semantic search, vector database setup, OpenAI/LLM integration, internal knowledge assistants, API integrations, and analytics.
The solution helps teams find information faster, reduce repetitive research, improve answer consistency, and build scalable AI-powered knowledge systems.
𝐓𝐞𝐜𝐡 𝐒𝐭𝐚𝐜𝐤:
OpenAI, LangChain, RAG, Pinecone, Qdrant, Weaviate, FAISS, Chroma, Python, FastAPI, PostgreSQL, APIs.
AI Behavioral Health Documentation Platform
AI-Powered Clinical Documentation Built for Behavioral Healthcare Workflows
Overview
Designed an AI-powered documentation platform focused on real clinical workflows across mental health and addiction treatment. The platform transforms live conversations and uploaded transcripts into structured clinical documentation while using custom knowledge-base logic to maintain domain-specific context.
The architecture was designed around security, scalability, healthcare interoperability, and AI-assisted clinical workflows, providing a foundation for integration with existing EMR systems.
The Challenge
Behavioral health documentation requires more than basic transcription. Clinical notes need to capture nuance, context, treatment terminology, and relevant patient information while fitting naturally into existing healthcare workflows.
The platform needed to:
Convert conversations into structured clinical notes
Understand behavioral health and addiction-treatment terminology
Ground AI outputs in custom clinical knowledge
Support transcript and speech-based inputs
Maintain secure handling of sensitive healthcare data
Provide a scalable foundation for EMR interoperability
AI-Powered Documentation
The system combines speech-to-text, LLM-based summarization, clinical nuance detection, and retrieval-augmented generation (RAG) to transform raw conversations into meaningful documentation.
Custom knowledge-base logic and semantic search help ground generated content in relevant clinical information rather than relying solely on general-purpose model knowledge.
Intelligent AI Architecture
The platform uses an agent-oriented architecture combining:
AI summarization
Clinical context and nuance detection
RAG pipelines
Semantic search
Vector-based knowledge retrieval
Private LLM deployment
LLM fine-tuning capabilities
Custom healthcare knowledge bases
This creates a flexible foundation for specialized behavioral-health documentation workflows.
Healthcare-Ready Infrastructure
Security and interoperability were considered at the architecture level, including:
HIPAA-ready cloud architecture
Encryption at rest
Secure backend infrastructure
PostgreSQL + pgvector
Healthcare interoperability support
HL7/FHIR integration readiness
EMR integration support
Serverless backend architecture
Technology Stack
Frontend: React, Vite
Backend: Python, FastAPI
AI/LLM: AWS Bedrock, Anthropic Claude, AI Agents
AI Infrastructure: RAG, Vector Database, pgvector, Semantic Search, LLM Fine-tuning
Database: PostgreSQL
Healthcare: HL7/FHIR, EMR Integration
Cloud & Security: AWS, HIPAA-ready architecture, Encryption at Rest
Speech: Speech-to-Text APIs
The Result
A scalable AI clinical documentation platform built specifically around behavioral healthcare workflows—combining conversational AI, clinical knowledge retrieval, secure infrastructure, and healthcare interoperability into a unified system.
therapists lose so many hours on notes, this solves a real problem